Chapter 28: Processing of germplasm, associated material and data

M. Mackay
Bioversity International, Rome, Italy via dei Tre Denari 472/A 00057 Maccarese, Rome, Italy
E-mail: m.mackay(at)cgiar.org
A. Alercia
Bioversity International, Rome, Italy via dei Tre Denari 472/A 00057 Maccarese, Rome, Italy
E-mail: a.alercia(at)cgiar.org

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This chapter is a synthesis of new knowledge, procedures, best practices and references for collecting plant diversity since the publication of the 1995 volume Collecting Plant Genetic Diversity: Technical Guidelines, edited by Luigi Guarino, V. Ramanatha Rao and Robert Reid, and published by CAB International on behalf of the International Plant Genetic Resources Institute (IPGRI) (now Bioversity International), the Food and Agriculture Organization of the United Nations (FAO), the World Conservation Union (IUCN) and the United Nations Environment Programme (UNEP). The original text for Chapter 28: Processing of Germplasm, Associated Material and Data, authored by J. A. Toll, has been made available online courtesy of CABI. The 2011 update of the Technical Guidelines, edited by L. Guarino, V. Ramanatha Rao and E. Goldberg, has been made available courtesy of Bioversity International.

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Abstract

	A handheld GPS device, which is one of the central technologies in a new CIAT project on site-specific agriculture. (Photo: Neil Palmer/CIAT ).

Together, the material and associated information assembled prior to the completion of a collecting mission represent the most important outcome of the mission. Furthermore, any accessions or reference materials are of little value unless accompanied by the associated information that facilitates their use. There have been few changes to the activities involved with the processing of germplasm, associated material and data acquired during plant collection missions since the original version of these guidelines was published. Changes primarily relate to technological advances and standardization of descriptors, which allow much of the information associated with the mission to be captured electronically, either during or after the actual mission. These include notebook computers, personal digital assistants (PDAs), smartphones, digital cameras and geographic positioning systems (GPS). The data-collection forms used in the past are no longer available but contemporary technology, along with data standards, allow these to be quickly developed and customized for specific genera and situations. Several international initiatives have occurred since 1995; these offer updated information that mainly relates to the data component. This chapter provides guidelines to assist in the processing of germplasm and reference samples along with associated data.

 

Introduction

While much of the work involving the processing of germplasm, associated material and data is determined during the planning phase of a collecting mission, actually completing these activities may require significant onsite decision making regarding procedures and processes to ensure that the material and its associated information are both as complete and accurate as possible. Since 1995, little has changed in the processing, drying, cleaning, treatment and phytosanitary inspection, sharing out, packaging and dispatch of collected samples. However, in the case of gathering and handling data, there have been some significant changes, mostly associated with the availability of new technologies and the standardization of descriptors to facilitate data gathering, management and storage during the collecting mission, so that their use in research and plant improvement can be facilitated. There are also more options for safety duplication today. International initiatives, such as the International Treaty on Plant Genetic Resources for Food and Agriculture and the Global Crop Diversity Trust, bring additional responsibilities and resources for collectors. And there are many online resources to assist in the planning and execution of collecting missions. These change constantly, but internet search engines enable the most current resources to be quickly located and assessed.

 

Current Status

The International Plant Genetic Resources Institute (IPGRI) changed its operating name to Bioversity International in 2006. All reference to IPGRI in the original text should now read as ‘Bioversity’.

Since 1995, there have been ongoing technological developments that provide opportunities to electronically capture most, if not all, of the data previously written onto data-collection forms. At that time, most information from collecting missions was captured as hardcopy/written text on standard forms, and photographic records were based on film technology. Today, digital photography has mostly replaced film-based photographs and also provides options for storing and sharing data more widely than what was possible with film-based photographs and slides. Additionally, new technologies can now make the capture, processing, storage and distribution of data much more accurate and efficient.

There are three technological advances relevant to this chapter:

• Portable computers, personal digital assistants (PDAs), smartphones and global positioning systems (GPS) all allow the accurate capture of pertinent data in the field.

• Digital photography makes the capturing, labelling and duplication of images much easier than what was possible in 1995. Image files, together with associated metadata, can be downloaded to computers as required.

• Environmental data associated with collection sites can be electronically harvested using geographic information system (GIS) technology.

The portable computer, now mostly represented by notebook/laptop and netbook designs, allows data to be captured in situ on electronic text-based forms (facsimiles of hardcopy forms) entered directly into a database. This allows ‘data standards’ to be enforced by enabling the operator to select input based only on the accepted standards for the specific data being recorded. For example, the options for ‘biological status’ in the FAO/IPGRI List of Multi-crop Passport Descriptors (MCPD) (FAO/IPGRI 2001) can be fixed in the database as the only permitted values for this data field. More recently, other options have become available for electronically capturing collecting-mission data onsite. These include the PDAs, GPS and smartphones. They employ a number of common operating systems, most of which have GPS and database applications to facilitate the ease of capturing information, processing and storing it, and ensuring quality.

A GPS device can determine the georeference of the collecting site simply and accurately (Kaplan and Hegarty 2006)). The use of a standard geographical datum, such as WGS84, is recommended and should be part of the georeference annotation. Many modern smartphones also have GPS functionality, providing an alternative option for capturing georeferences.

Digital photography, together with associated computer software for annotating, editing and enhancing digital images, allows significant additional information to be captured and reviewed in situ before accepting it for inclusion with the other information associated with each germplasm and/or reference sample.

While time is precious during actual collecting activities, it can be a simple process to capture additional data outside collecting hours. One example would be to harvest environmental information about each collecting site from continuous surface layers of such data stored on a personal computer. These data could include climatic data, such as that available from WorldClim (Hijmans et al. 2005) or other sources (De Pauw 2002). This type of information can be generated in advance by creating continuous surface digital maps for the region where the collecting mission is to take place, for environmental parameters from point data using the ‘thin-plate smoothing spline’ method (Hutchinson and Corbett 1995), as implemented in ANUSPLIN software (Hutchinson 2000). Then the value of each parameter can be extracted from the digital maps for each collecting site and associated with the germplasm sample. Adding value to germplasm in this way requires the use of GIS technology, such as Diva GIS (Hijmans et al. 2001).

The data can be dispatched as hardcopy (computer-generated, typed or handwritten) or on electronic media, or they can be transmitted electronically. A hard copy of the data should accompany sample shipments.

Although many organizations develop their own collecting forms according to their specific needs, this has led to difficulties in data exchange. To address this challenge, Bioversity International, in collaboration with crop experts, promotes the standardization of descriptors, and many crop descriptor lists include a crop-specific collecting form. A useful source of Bioversity descriptors and derived standards is available on the Bioversity website (www.bioversityinternational.org/?id=3737).

Germplasm should be duplicated in at least two other collections. While some base collections are still able to provide a duplication service, it is also recommended that duplication in ‘significant international collections’ (including the CGIAR international collections) be ensured. There is also the option to have material securely backed up in the Svalbard Global Seed Vault (www.croptrust.org/main/arcticseedvault.php?itemid=211).

The IPGRI software mentioned in the earlier version of this chapter, in the section on processing reference samples, no longer exists. Relevant information and descriptors for creating electronic tools for data collection are available from various sources, including Bioversity’s website (www.bioversityinternational.org). Additional information and resources are available from a number of other sites, including the Kew Royal Botanic Gardens (www.kew.org) and the Australian National Botanic Gardens (www.anbg.gov.au). A data-collecting template for direct use or for developing a customized tool for data input will soon be available from Bioversity International.

 

Future challenges/needs/gaps

Data standards are crucial to the long-term utility of all information gathered during collecting missions. Without accepted standards, information about collecting sites is less easily shared, putting correct identification of germplasm accessions at risk. It is imperative that the MCPD List of passport descriptors (FAO/IPGRI 2001) be used in all collecting activities to facilitate the effective access to and use of collection-site data.

Interoperability and/or linkages between information systems should be a goal behind documenting collection-site data. Interoperability allows systems to work together (inter-operate), thereby sharing data, while links allow users to identify a source of information in one system and then move to the second system using tagged URLs in the first system. These and similar technologies allow many systems to contribute to a greater store of information.

 

Conclusions

While there have been no significant changes in handling seed and reference samples during the collecting mission, a number of advances in the form of new technologies and standardized descriptors are now widely used for information capture, quality enhancement, processing, storing, distribution and ensuring that the collected germplasm can be more easily utilized in research and breeding programmes.

Updates primarily involve the capturing of data electronically in standardized format, the use of electronic storage and distribution technologies, and the associated shift from using paper forms for data collection and distribution.

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References and further reading

De Pauw E. 2002. An Agro-Ecological Exploration of the Arabian Peninsula. ICARDA, Aleppo, Syria.

FAO. 1993. International code of conduct for plant germplasm collecting and transfer. Appendix E of Report of the Conference of FAO Twenty-seventh Session, Rome, 6–24 November 1993. FAO, Rome.

FAO/IPGRI. 2001. Multi-Crop Passport Descriptors. FAO and IPGRI, Rome.

Hijmans RJ, Guarino L, Cruz M, Rojas E. 2001. Computer tools for spatial analysis of plant genetic resources data: 1. DIVA-GIS. Plant Genetic Resources Newsletter 127:15–19. Available online (accessed 20 January 2012): www.diva-gis.org/docs/pgr127_15-19.pdf.

Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A. 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25:1965–1978. Available online (accessed 20 January 2012): www.worldclim.org/worldclim_IJC.pdf.

Hutchinson M. 2000. ANUSPLIN Version 4.1. User Guide. Centre for Resource and Environmental Studies, Australian National University, Canberra, Australia.

Hutchinson M, Corbett J. 1995. Spatial interpolation of climatic data using thin plate smoothing splines. In: FAO, editor. Co-ordination and Harmonisation of Databases and Software for Agroclimatic Applications: Proceedings of an Expert Consultation Held in FAO Rome, Italy, from 29 November to 3 December 1993. FAO, Rome.

Kaplan ED, Hegarty CJ. 2006. Understanding GPS: Principles and Applications. Artech House, London.

 

Internet resources

Australian National Botanic Gardens: www.anbg.gov.au

Bioversity International: www.bioversityinternational.org

Bioversity International Collecting Missions Database: http://singer.cgiar.org/index.jsp?page=coll-sample-data

Bioversity International Descriptor Lists and Derived Standards: www.bioversityinternational.org/?id=3737

DIVA-GIS: www.diva-gis.org

FAO/IPGRI Multi-Crop Passport Descriptors (MCPD): www.bioversityinternational.org/nc/publications/publication/issue/faoipgri_multi_crop_passport_descriptors.html

Global Crop Diversity Trust: www.croptrust.com

International Treaty on Plant Genetic Resources for Food and Agriculture: www.planttreaty.org

Royal Botanic Gardens, Kew: www.kew.org

Svalbard Global Seed Vault: www.croptrust.org/main/arcticseedvault.php?itemid=211

WorldClim: www.worldclim.org

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Chapter 3. An introduction to plant germplasm exploration and collecting: planning, methods and procedures, follow-up

J. M. M. Engels
Bioversity International, Maccarese, Rome, Italy
E-mail: j.engels(at)cgiar.org

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This chapter is a synthesis of new knowledge, procedures, best practices and references for collecting plant diversity since the publication of the 1995 volume Collecting Plant Diversity: Technical Guidelines, edited by Luigi Guarino, V. Ramanatha Rao and Robert Reid, and published by CAB International on behalf of the International Plant Genetic Resources Institute (IPGRI) (now Bioversity International), the Food and Agriculture Organization of the United Nations (FAO), the World Conservation Union (IUCN) and the United Nations Environment Programme (UNEP). The original text for Chapter 3: An Introduction to Plant Germplasm Exploration and Collecting: Planning, Methods and Procedures, Follow-up, authored by J. M. M. Engels, R. K. Arora and L. Guarino, has been made available online courtesy of CABI. The 2011 update of the Technical Guidelines, edited by L. Guarino, V. Ramanatha Rao and E. Goldberg, has been made available courtesy of Bioversity International.

Dedication: This chapter is dedicated to the late Dr R. K. Arora, co-author of the original chapter, who passed away after a brief illness in March 2010.

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Abstract

	Collecting in Kephalonia,Greece. (Photo: S. Petitt; Source: www.cam.ac.uk)
	All collecting information is carefully recorded. (Photo: A.Vezina/Bioversity International)

The significant developments that have occurred in information technologies and molecular genetics and genomics have had a direct and beneficial impact on germplasm exploration and collecting. The powerful information-management tools that are now available to collectors allow them to combine many different data sets and to extract much more practically useful and relevant information for the planning of collecting missions as well as the collecting itself. Furthermore, the political and legal situations regarding the status of and ownership of plant genetic resources have changed drastically over the past 15 years. The implementation of the Convention on Biological Diversity (in 1993), the International Treaty on Plant Genetic Resources for Food and Agriculture (in 2006) and the establishment of the Nagoya Protocol (in 2011) have had and continue to have a significant impact on matters related to the access and benefit sharing of plant genetic resources. Ethical considerations and awareness have also evolved and have become more important for individual scientists when collecting germplasm in foreign countries.

 

Introduction

Since the first edition of these Technical Guidelines in 1995, there have been significant changes in policy, science and technology that have had a direct impact on exploration and collecting plant genetic resources.

 

Current Status

Molecular tools

The technological developments that have taken place in molecular biology and genetics over the past 10 to 15 years (particularly the enormous decrease in the price of genotyping) have revolutionized our understanding of the way genetic diversity is spread across populations, species and genepools. This, combined with the new power of information management (including bioinformatics), provides a much better basis for the plant collector to understand the taxonomy of a crop genepool, the sampling strategy to be followed, the sites to visit for collecting and how to optimize the conservation of genetic diversity in collections. There are strategies on how the content of crop collections can be improved (Van Treuren et al. 2009). For example, an optimization strategy that builds on the concept of core collection has been proposed. It relies on hierarchically structuring the crop genepool and assigning a relative importance to each of its different components. The resulting optimized distribution of the number of accessions is compared with the actual distribution, which allows under- and over-representation within a collection to be identified. This proposed optimization strategy is applicable to individual genebanks, as well as consortia of cooperating genebanks, which makes it relevant for ongoing activities aimed at sharing responsibilities among institutions, such as A European Genebank Integrated System (AEGIS) (http://aegis.cgiar.org).

Because of the increased importance of the exchange of DNA samples for research, it is important to include this in the overall collecting strategy. This means that good-quality, properly sampled plant material will become increasingly important, as will knowledge of the considerable differences among species as to which organs or plant parts are the most suitable for DNA extraction. For a very recent and complete publication on collecting for DNA banking, see Gemeinholzer et al. (2010). This paper gives guidelines for all the tasks required for pre-DNA isolation of samples, from both plants and animals. It includes information about sampling strategies, methodological considerations for collecting different types of plant tissues, strategies for tissue preservation and DNA isolation in the field, relevant logistics and safety considerations in the field, labelling of samples and recording essential information, transportation practices from the field to the laboratory and necessary equipment. Further details can be found in Chapter 40 of these Technical Guidelines (on collecting DNA for conservation), and Prendini et al. (2002) provide an earlier overview of this particular field.

In 2004, Bioversity conducted an international survey on the application of DNA technology in their genebanks, and of the 243 respondents, 51 (20.9%) indicated that they store DNA. Almost all of the institutes that store DNA extract it themselves, using standard manual extraction protocols, and one-third of these use commercial kits for DNA extraction. De Vicente and Andersson (2006) list a number of the reasons institutes, including genebanks and other organizations that collect plant genetic resources for food and agriculture (PGRFA), store DNA, as well as which techniques they use to serve their clients.

Information technologies and tools

The increased availability and ease of use of GIS technologies and GPS receivers over the past decade mean that sophisticated spatial analysis can be used routinely to support collecting efforts. Guarino et al. (2002) provide a comprehensive overview of how spatial analysis of the geo-referenced data generated by the process of conservation and use of genetic resources, using GIS, can feed back to enhance and facilitate the process and, indeed, to add value to germplasm collections. GIS allows the extent and distribution of diversity to be analysed and, thus, in the context of this collecting manual, allows the following activities to be undertaken more efficiently:

• ecogeographic surveying: the process of collating information on the taxonomy, genetic diversity, geographic distribution, ecological adaptation and ethnobotany of a plant group, as well as on the geography, ecology, climate and human setting of the study area. Such surveys can allow the identification of areas that are likely to contain genetic resources with specific traits or taxa of interest, areas that are highly diverse, areas that would complement other areas in conservation efforts, areas that contain material that is currently missing or underrepresented in collections, and areas where the genetic resources in question are threatened with genetic erosion. (See also Chapter 14, of this update, on ecogeographic surveys.)

• fieldwork: including field aids and the timing of collecting missions

• the design, management and monitoring of in situ reserves (Monitoring activity, in particular, is of potential relevance in the context of these Guidelines.)

GIS tools allow one to carry out complex analyses combining different (spatially referenced) data sources and to generate maps. This facilitates the uptake of outcomes by responsible authorities and encourages the development and implementation of conservation policies. Tools such as FloraMap (see http://gcmd.nasa.gov/records/CIAT_FloraMap.html) and DIVA-GIS (www.diva-gis.org) can be used to determine potential distributions, assuming that climatic variables are the principal drivers of the geographic spread of plant species. Examples of the successful application of these tools include the location of wild and rare potato species in Peru (Hijmans and Spooner 2001) and optimizing a collecting mission for a rare wild Capsicum species (C. flexuosum) in Paraguay (Jarvis et al. 2005). Spatial analysis using GIS can also be used to identify regions of particularly high species richness and diversity.

The general accessibility and use of the internet has also revolutionised the sharing of data on biodiversity, geography and the environment. Scheldeman and van Zonneveld (2010) have produced a comprehensive training manual cum technical guide on the spatial analysis of data on plant diversity and distribution. See also Chapter 15/16 and Chapter 19 of this update.

Gap analysis is a systematic approach that allows collectors to target areas that contain traits and taxa that are underrepresented in existing ex situ collections. This method has been developed for crop wild relatives (see http://gisweb.ciat.cgiar.org/GapAnalysis/?p=139). The tool is a joint product of Bioversity International, the International Rice Research Institute (IRRI) and the International Centre for Tropical Agriculture (CIAT). More recently, Maxted et al. (2008) used the gap analysis methodology to identify actual areas for the in situ conservation of crop wild relatives.

Legal and policy changes

In the policy area, the developments that have taken place with regard to access and benefit sharing—particularly those resulting from the International Treaty on Plant Genetic Resources for Food and Agriculture (hereinafter "the Treaty") and a number of protocols and agreements related to the Convention on Biological Diversity—have also had a direct influence on the procedures required to obtain access to genetic resources for collecting and/or exchange. The most recent relevant instrument is the so-called Nagoya Protocol on access and benefit sharing that was agreed upon during the COP 10 meeting in Nagoya, Japan, and has been signed by 92 countries, to date.

Prior informed consent (PIC) needs to be obtained from the relevant authorities in a given country before a collecting mission can be conducted, and access (i.e., permission to transfer the germplasm outside the country) is granted on the basis of mutually agreed terms (MAT), which may include measures that cover sharing with the providing country any benefits that might arise from the commercialization and utilization of the germplasm.

For crops and species that are included on the Annex I list of the Treaty, there is a standard material transfer agreement (SMTA) that needs to be concluded before any germplasm can be legally sent and/or received. (For more details on this, see Chapter 2 of these Technical Guidelines, on legal issues.) For non-Annex I crops, a material transfer agreement, established by the national authorities, needs to be included. However, in some instances (as in Europe) it has been agreed to also distribute germplasm that is non-Annex I material from genebanks with an SMTA that includes a specific footnote on its non-Annex I status.

Because of the political changes that have taken place over the past 10 years or so (and which are expected to continue) with respect to access to genetic resources and benefit sharing, organizations like the centres of the Consultative Group on International Agricultural Research (CGIAR) and individual countries have changed or are in the process of adjusting their collecting procedures to the new requirements. For instance, some countries like the following have established national legislation on access and benefit sharing of PGRFA (the years mentioned in brackets relate to the year of publication of the relevant regulation or law; in the case of the Nordic countries these dates are the same as they refer to a regional Ministerial Declaration on Access and Rights to Genetic Resources adopted in 2003): the Philippines (2005) and a number of European countries, including Bulgaria (2002), Denmark (2003), Finland (2003), France (2006), Hungary (2004), Iceland (2003), Italy (2007), Malta (2004), the Netherlands (2002), Norway (2003), Portugal (2002), Russia (2001), Spain (2007), Sweden (2004) and Switzerland (1999).

The legal situation and status of plant genetic resources has drastically changed and widened over the past 15 years. In many countries, the rights and status of local communities have been recognized, and many individuals have become aware of the importance of their own contributions to genetic diversity. With this in mind, it is important for collectors to inform themselves about the ethical considerations related to genetic resources, which might also have an impact on local and indigenous people. Recently, ethical considerations regarding the collecting, research and use of agricultural biodiversity have been reviewed, and recommendations on ethical issues have been made (Engels et al. 2010). Whereas easy access to genetic resources for breeding purposes is important, there are international agreements and legal frameworks that have been developed to ensure adequate recognition of the contributions of local communities and traditional farmers in creating and nurturing these resources. Consequently, Engels et al. (2010) have applied the code of ethics of the International Society of Ethnobotany to plant genetic resources to create awareness among scientists and policymakers who are concerned with agro-biodiversity research and its potential impact on local communities. Collectors are encouraged to seriously consider the ethical principles presented in the paper and to integrate these principles into their personal ethical framework.

The analysed code of ethics recognizes the inalienability of indigenous peoples, traditional societies and local communities: i.e., the right not to have their traditional territories and the natural resources within them, including related practices, cultures and the associated traditional knowledge, affected by the introduction of new but foreign ideas, inventions, knowledge and other findings. Indigenous communities should have the right to decide whether or not to accept such an introduction. The idea of the inalienability of rights has existed for authors of creative works in the "moral rights" aspect of copyright law, as embodied in the concept of the "integrity" of a created work. And a similar principle is included in Article 20 of the UN Declaration on the Rights of Indigenous Peoples, which states that indigenous peoples have the right to maintain and develop their political, economic and social systems or institutions. An example illustrating this principle is the introduction of new crop varieties with "improved" characteristics, such as short-stemmed teff (Eragrostis tef) in Ethiopia. Such varieties were developed without considering the importance of teff straw as a major source of livestock feed during the dry season.

Any research programme aimed at traditional farmers must first assess the needs and requirements of these farmers and include the intended users of the new variety in decisions on the specific breeding objectives for any given crop. Participatory plant-breeding approaches, which are included in a number of development projects, address this principle squarely and are increasingly seen to result in better-adapted and more sustainable varieties (see also Chapter 18 of these Technical Guidelines, on: a participatory approach to collecting plant genetic resources and documenting associated indigenous knowledge in the field). Consequently, in the context of initiatives on participatory breeding and crop improvement that include the collecting of genetic diversity from local farming communities, it would make good sense to involve the local farmers in such collecting activities in a participatory manner.

The principle of equity is another key consideration in the Convention on Biological Diversity and is an important component of the benefit-sharing arrangements that are expected to be established between the researcher or collector and the local people when obtaining and subsequently using agro-biodiversity or local knowledge. It is also one of the principles in the Code of Ethics of the International Society of Ethnobotany (http://ethnobiology.net/code-of-ethics) and is included in the UN Declaration on the Rights of Indigenous Peoples (http://www.un.org/esa/socdev/unpfii/documents/DRIPS_en.pdf): the recognition that indigenous peoples must be fairly and adequately compensated for their contributions to research activities and outcomes involving their knowledge. The FAO Code of Conduct for Germplasm Collecting and Transfer (www.fao.org/nr/cgrfa/cgrfa-global/cgrfa-codes/en) also includes the equitable sharing of benefits arising from the use of collected germplasm.

 

Future challenges/needs/gaps

• Worldwide, the lack of sufficient and well-qualified human capacity has been reported and experienced in areas that are of critical importance to the collecting of genetic resources, e.g., taxonomy, genetics and ecology (FAO 2010). This is a serious challenge for global conservation efforts, and the organizations connected with conservation at different levels must take action to correct the situation.

• Whereas for a (few) major crops it has been reported that a large part of their genetic diversity is present in ex situ collections, many other crops, particularly the so-called "neglected and underutilized" species, and the crop wild relatives are significantly underrepresented at the species level—and even more so at the level of genetic diversity within species (FAO 2010).

• We now have a better understanding of the extent and nature of gaps in ex situ collections than we did when the first version of these Technical Guidelines was written; however, the picture is still far from complete. The use of molecular data to improve our understanding on the extent, nature and distribution of genetic diversity, more detailed field surveys and better geo-referencing of accessions will be very helpful to more accurately identify gaps and redundancy within and among collections as well as in a given genepool as a whole (FAO 2010).

• As the linkages between in situ and ex situ conservation have not been well defined, at least from a legal perspective, and, for many crop genepools, clear conservation strategies are missing to guide genebanks and their curators, it is not always clear whether it is better to collect from farmers’ fields or natural habitats and store the germplasm ex situ or to keep it in situ/on-farm.

• The potential role of DNA samples and the storage of sequence information for conservation purposes is not fully recognized or established, and this (among other reasons) seems to hamper genebanks in moving forward faster with this technology in the management and distribution of genetic resources.

 

Conclusions

As a number of the changes and technological developments mentioned above are addressed in more detail in other chapters of these Technical Guidelines, the reader is advised to read the chapters or sections in which these developments and technologies/tools/methods are more fully described. Due to the ever-increasing amount of information the scientific community is producing on many of the species we are interested in collecting, studying and/or using in improvement programmes, there is a danger of information overload, and consequently, it is important to be very clear about the objective of a given exploration or collecting effort and to use the available information to the benefit of that effort.

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References and further reading

De Vicente MC and Anderson MS (eds). 2006. DNA banks-providing novel options for genebanks? Topical Reviews in Agricultural Biodiversity. International Plant Genetic Resources Institute, Rome, Italy. Click here to download this publication. (0.7 MB)

Engels JMM, Dempewolf H, Henson-Apollonio V. 2010. Ethical considerations in agro-biodiversity research, collecting, and use. Journal of Agriculture and Environmental Ethics 24:107–126. DOI 0.1007/s10806-010-9251-9.

FAO. 2010. The Second Report on the State of the World's Plant Genetic Resources for Food and Agriculture. Food and Agriculture Organization of the United Nations, Rome. Available online (accessed 28 October 2011): www.fao.org/agriculture/crops/core-themes/theme/seeds-pgr/sow/sow2/en.

Gemeinholzer B, Rey I, Weising K, Grundmann M, Muellner AN, Zetzsche H, Droege G, Seberg O, Petersen G, Rawson DM, Weigt LA. 2010. Organizing specimen and tissue preservation in the field for subsequent molecular analyses. In: Eymann J, Degreef J, Häuser C, Monje JC, Samyn Y, Vanden Spiegel D, editors. ABC-Taxa, Volume 8. Manual on Field Recording Techniques and Protocols for All Taxa Biodiversity Inventories, Chapter 7. pp.129–157.

Guarino L, Jarvis A, Hijmans RJ, Maxted N. 2002. Geographic information systems (GIS) and the conservation and use of plant genetic resources. In: Engels JMM, Ramanatha Rao V, Brown AHD, Jackson MT, editors. Managing Plant Genetic Diversity. CAB International, Wallingford, UK. pp. 387–404. Available online (accessed 16 February 2012): http://gisweb.ciat.cgiar.org/sig/download/biological_mapping/guarino.pdf.

Hijmans RJ, Spooner DM. 2001. Geographic distribution of wild potato species. American Journal of Botany 88(11):2101–2112. Available online (accessed 18 February 2012): www.amjbot.org/content/88/11/2101.full.pdf.

Hunter D, Heywood VH, editors. 2011. Crop Wild Relatives: A Manual of In Situ Conservation. Issues in Agricultural Biodiversity. Earthscan, London.

Jarvis A, Williams K, Williams D, Guarino L, Caballero PJ, Mottram G. 2005. Use of GIS in optimizing a collecting mission for a rare wild pepper (Capsicum flexuosum Sentn.) in Paraguay. Genetic Resources and Crop Evolution 52(6):671–682. Available online (accessed 18 February 2012): http://ddr.nal.usda.gov/handle/10113/18374.

Kameswara Rao N, Bramel PJ, editors. 2000. Manual of Genebank Operations and Procedures. Technical Manual No. 6. International Crops Research Institute for the Semi-Arid Tropics, Andhra Pradesh, India. Available online (accessed 18 February 2012): http://pdf.usaid.gov/pdf_docs/PNACJ826.pdf.

Maxted N, Dulloo ME, Ford-Lloyd BV, Iriondo J, Jarvis A. 2008. Gap analysis: A tool for complementary genetic conservation assessment. Diversity and Distribution 14(6):1018–1030.

Maxted N, Kell SP. 2009. Establishment of a Global Network for the In Situ Conservation of Crop Wild Relatives: Status and Needs. FAO Commission on Genetic Resources for Food and Agriculture, Food and Agriculture Organization of the United Nations, Rome. (mimeograph)

Prendini L, Hanner R, DeSalle R. 2002. Obtaining, storing and archiving specimens for molecular genetic research. In: DeSalle R, Giribet G, Wheeler W, editors. Techniques in Molecular Systematics and Evolution. Series: Methods and Tools in Biosciences and Medicine. Birkhauser, Basel. pp. 176–248.

Scheldeman X, van Zonneveld M. 2010. Training Manual on Spatial Analysis of Plant Diversity and Distribution. Bioversity International, Rome. Available online (accessed 6 October 2011): www.bioversityinternational.org/training/training_materials/gis_manual/gis_download.html.

Van Treuren R, Engels JMM, Hoekstra R, Van Hintum ThJL. 2009. Optimization of the composition of crop collections for ex situ conservation. Plant Genetic Resources 7:185–193.

 

Internet resources

A European Genebank Integrated System (AEGIS): http://aegis.cgiar.org

Diva-GIS: www.diva-gis.org

FAO Code of Conduct for Germplasm Collecting and Transfer: www.fao.org/nr/cgrfa/cgrfa-global/cgrfa-codes/en

FloraMap: http://gcmd.nasa.gov/records/CIAT_FloraMap.html

Gap Analysis: http://gisweb.ciat.cgiar.org/GapAnalysis/?p=139

International Society of Ethnobotany (ISE): http://ethnobiology.net

International Treaty for Plant Genetic Resources for Food and Agriculture: www.planttreaty.org

Nagoya Protocol on Access and Benefit Sharing: www.cbd.int/abs

Standard Material Transfer Agreement: www.planttreaty.org/content/what-smta

United Nations Declaration of the Rights of Indigenous Peoples (UNDRIP): www.un.org/esa/socdev/unpfii/documents/DRIPS_en.pdf

 

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Chapter 41: Gap analysis: A tool for genetic conservation

N. Maxted
School of Biosciences University of Birmingham, Edgbaston Birmingham, UK
E-mail: n.maxted(at)bham.ac.uk
N. P. Castañeda Álvarez
International Center for Tropical Agriculture (CIAT)—Bioversity International Cali, Colombia and School of Biosciences University of Birmingham, Edgbaston Birmingham, UK
E-mail: n.p.castaneda(at)cgiar.org
H. A. Vincent
School of Biosciences University of Birmingham, Edgbaston Birmingham, UK
E-mail: holly.vincent(at)gmail.com
J. Magos Brehm
School of Biosciences University of Birmingham, Edgbaston Birmingham, UK
E-mail: joanabrehm(at)gmail.com

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This chapter is a synthesis of new knowledge, procedures, best practices and references for collecting plant diversity since the publication of the 1995 volume Collecting Plant Diversity: Technical Guidelines, edited by Luigi Guarino, V. Ramanatha Rao and Robert Reid, and published by CAB International on behalf of the International Plant Genetic Resources Institute (IPGRI) (now Bioversity International), the Food and Agriculture Organization of the United Nations (FAO), the World Conservation Union (IUCN) and the United Nations Environment Programme (UNEP). The 2011 update of the Technical Guidelines, edited by L. Guarino, V. Ramanatha Rao and E. Goldberg, has been made available courtesy of Bioversity International.

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Abstract

Gap analysis is a well-established conservation technique used by ecological conservationists to identify areas in which selected elements of biodiversity are represented. Through comparison with existing in situ protected-area networks, it identifies habitats or ecosystems that need additional protection. However, the methodology has recently been adapted for application in the context of broader agro-biodiversity conservation, to encompass both in situ and ex situ strategies for conserving genetic diversity. The extended methodology involves the following steps: (a) circumscription of target taxon and target area, (b) assessment of natural diversity through a review of intrinsic taxonomic, genetic and ecogeographic diversity combined with threat assessment, (c) assessment of current complementary between in situ and ex situ conservation strategies, (d) reformulation of the conservation strategy through analysis of the differences between the pattern of natural, intrinsic diversity and the elements of that diversity already effectively represented by existing in situ and ex situ conservation actions. The methodology is reviewed and illustrated using various temperate and tropical examples of crop wild relatives and landraces.

 

Introduction

When the original text for chapter 14 of these Technical Guidelines, on ecogeographic surveys, was being prepared, knowledge of the early application of ecological gap analysis was limited and still in the early stages of development (Burley, 1988; Margules et al., 1988; Margules, 1989; Pressey and Nicholls, 1989; Pressey et al., 1993). However, an extensive literature on “gap analysis” (i.e., how to identify areas in which selected elements of biodiversity are underrepresented) has subsequently arisen, including Margules and Pressey (2000) Balmford (2003), Brooks et al. (2004), Lipow et al. (2004), Dietz and Czech (2005), and Riemann and Ezcurra (2005). Although within the wider conservation community, the literature on gap analysis was originally and is still primarily applied to indigenous forests (particularly on small islands rich in endemic species), the technique has recently been extended to agro-biodiversity conservation, encompassing conservation strategies for both in situ and ex situ genetic diversity. Maxted et al. (2008a) showed how the existing methodology might be adapted for more general conservation use and proposed a specific methodology. Subsequently, gap analysis has rapidly established itself as the methodology for conservation planning (FAO, 2009; Maxted and Kell, 2009; Parra-Quijano et al., 2012a; Ramírez-Villegas et al., 2010). As stressed in the update of chapter 14 on ecogeographic surveys, the current application of gap analysis techniques is not replacing ecogeography; it has, in effect, subsumed ecogeography as an element within the broader, more schematic gap analysis protocol. As such, ecogeographic surveys will continue to be routinely undertaken, but increasingly within the broader context of gap analysis. Therefore, chapter 14 can be seen as a sister chapter to this chapter, which specifically addresses gap analysis and should be read in conjunction with that chapter. Both ecogeographic surveys and gap analysis form essential components in planning for plant genetic-resource conservation.

The basic premise of gap analysis is that the target taxon’s ecogeographic and genetic distribution and diversity are compared with the elements that are currently actively conserved.¹ The “gap” is therefore the component of the target taxon’s ecogeographic and genetic distribution and diversity that is not currently actively conserved and which becomes the conservation priority. As such, the protocol for genetic gap analysis involves four steps: (a) circumscription of taxa, (b) identifying the breadth of ecogeographic and genetic distribution and diversity for the target taxa, (c) matching current in situ and ex situ conservation actions with the breadth of ecogeographic and genetic distribution and diversity to identify the conservation “gaps” and (d) formulating a revised in situ and ex situ conservation strategy (Maxted et al., 2008a).

¹ “Active conservation” is where the target taxon population is specifically managed to maintain its diversity, as opposed to “passive conservation” where the site is managed and it is hoped that the target taxon population benefits from the general site-based management.

 

Current status

Circumscription of target taxon and target area

The first step in the gap analysis protocol is to establish the taxonomic (e.g., genus, section or species) and geographic (e.g., global, regional, country or provincial) breadth of the analysis. The approach that has largely been applied thus far is for individual analyses to focus on distinct crop genepools, see Maxted et al. (2005), Maxted et al. (2008b) and the CIAT-IRRI-Bioversity International GapAnalysis project (http://gisweb.ciat.cgiar.org/gapanalysis). However, in terms of establishing in situ and ex situ conservation priorities, it might be of greater practical value and more cost efficient to establish multi-genepool conservation targets irrespective of individual genepool results, so collection or establishing genetic reserves is led by overall targets for plant genetic resources. This multi-genepool approach was taken by Maxted et al. (2012) for the temperate legume genera Cicer, Lathyrus, Lens, Medicago, Pisum and Vicia species (see figure 41.1) and by Whitehouse (2011) for the in situ conservation of temperate cereal genera Avena, Aegilops, Hordeum, Secale and Triticum (see figure 41.2). Interestingly, both the legume and cereal analysis showed the area at the northern end of the Bekaa Valley in Lebanon and Syria to be a target for in situ and ex situ conservation action for both wild legumes and cereals. Both analyses showed some coincidence between undertaking the analysis for each genepool separately and the combined legume or cereal analyses, but for the legumes, the chickpea and the oat genepools, the individual analyses were quite distinct from the combined analyses.

Figure 41.1: Complementarity analysis for Cicer, Lathyrus, Lens, Medicago, Pisum and Vicia species. Source: Maxted et al. (2012).

Figure 41.2: Complementary analysis Avena, Aegilops, Hordeum, Secale and Triticum species. Source: Whitehouse (2011).

Further, the International Center for Tropical Agriculture (CIAT) in collaboration with the International Rice Research Institute (IRRI) and Bioversity International undertook a gap analysis to identify the gaps in the ex situ collections of the crop wild relatives (CWR) of 13 crop genepools² (figure 41.3) (see http://gisweb.ciat.cgiar.org/gapanalysis), using all the species in the genus as the basis of the analysis. The conclusion was that if overall conservation of plant genetic resources is the aim, combining genepools for analysis may be an effective technique for identifying the highest priority sites but individual genepool priorities might be missed where the included taxa deviate from the combined norm, so a combination of using the individual and combined genepool approach is recommended.

² Pigeon pea (Cajanus), chickpea (Cicer), finger millet (Eleusine), barley (Hordeum), lentil (Lens), pearl millet (Pennisetum), bean (Phaseolus), sorghum (Sorghum), wheat (Triticum and Aegilops), faba bean (Vicia), cowpea (Vigna) and maize (Zea).

Figure 41.3: Priorities for in situ and ex situ conservation of the crop wild relatives of 13 genepools: (A) Taxa-level priorities (B) Genus-level priorities

Source: http://gisweb.ciat.cgiar.org/gapanalysis.

In the case of the CIAT study, a large number of taxa and areas were identified as priorities for conservation. As seen in figure 41.3, the number of taxa requiring further conservation is concentrated in a wide proportion of the African continent and northern Australia. When these priorities are summarized at the genus level, places where four different genera are potentially found is Madagascar, followed by central Africa, northern Australia and western Mexico. What all these priority regions have in common is the absence of large protected areas that could serve for the in situ conservation of the plant genetic resources of the crops aforementioned.

Subsequently, it has been proposed that gap analysis should not be based on the whole genus alone, but should also consider the analysis of the priority species as well, the reason being that in larger genera, the bulk of the species that are unlikely to be of immediate potential use might mask the ecogeographic distribution of those species of highest potential value. This can be illustrated by the case of cowpea relatives in sub-Saharan Africa. Moray and Maxted (2012) compared the results of analysing the whole genus of 124 Vigna taxa with the 14 Vigna taxa present in the primary (gp1) and secondary genepools (gp2), those most closely related to cowpea, and, perhaps not surprisingly, found the results were distinct (see figures 41.4 and 41.5). The conclusion was that if you are trying to establish the priority areas for ex situ or in situ conservation, then, as funds are likely to be limiting, the gap analysis should consider the priority taxa alone as well as the whole genus, allowing an informed decision to be made as to where to focus conservation. As noted in chapter 14, a Harlan and de Wet Global Priority Checklist of CWR Taxa (Vincent et al., 2012) is now available for 173 crop genepools, based on a published genepool and taxon group concept (see www.cwrdiversity.org). This will guide those wishing to establish which CWR should be included in gap analysis based on their priority as trait donors for breeding.

Figure 41.4: Complementary analysis for all 124 African Vigna taxa. Note: The darker green squares represent the highest priority regions for conservation, while the orange and red squares identify areas of lower priority. Source: Moray and Maxted (2012).

Figure 41.5: Complementary analysis for 14 priority African Vigna taxa (primary and secondary CWR taxa). Source: (Moray and Maxted (2012).

A third option would be to give different weights to different species in the analysis, based on their relative rarity. See Arponen et al. (2005) and Venevsky and Venevskaya (2005) for further discussion of weighted endemism. This approach has yet to be widely applied in the context of genetic resources but is available in the WorldMap software (see internet resources, below). It is also advisable not to ignore the wider potential genepool, which might contain further non-genepool and taxon group-concept priority taxa with useful traits, especially if tertiary genepool taxa have previously been used in breeding.

Assessment of natural diversity

The level of diversity occurring within the target taxon must be defined at the taxonomic, genetic or ecogeographic levels (i.e., how many taxa occur in the circumscribed taxon and what is their ecogeographic pattern of distribution).

Assessment of taxonomic diversity

Assessing taxonomic diversity involves listing the taxa encompassed by the taxonomic circumspection, whether for the entire genus or for genera (as in the case of Triticum aestivum, which has priority species in three genera: Aegilops, Amblyopyrum and Triticum). This is likely to involve identifying the accepted classification for the target taxon by consulting specialist publications and taxon experts, or searching online sources of information. As noted above, the analysis will usually be based on the highest priority taxa in genepool GP1B or GP2 or, if the genepool concept is unavailable, the equivalent taxon group TG1-3. Genepool and taxon group concepts with the included accepted taxa and synonyms are listed in the Harlan and de Wet Global Priority Checklist of CWR Taxa (Vincent et al., 2012) (see www.cwrdiversity.org).

Assessment of genetic diversity

Having established the list of taxa to be included in the gap analysis, the next step would be to collate existing data or generate new data on the inherent genetic diversity within those taxa. However, this might not be realistic because the knowledge about inherent patterns of genetic diversity is often limited and it could be too resource intensive to collate de novo. Consequently, genetically based approaches to conservation assessment, either in terms of “richness” (the total number of genotypes or alleles present regardless of frequency) or “evenness” (the evenness of the frequencies of different alleles or genotypes), can therefore only be applied to the most highly prioritized taxa. However, proxy or surrogate measures of genetic diversity may be applied, and it can be argued that ecogeography can act as an adequate proxy when there is a lack of specific data on genetic diversity. It should be remembered, however, that using any proxy will not be as accurate as using primary data.

As an example of the use of data on genetic diversity in conservation planning, van Zonneveld et al. (2012) used microsatellite markers to understand the spatial genetic diversity of Annona cherimola throughout the Andean region in Peru, Ecuador and Bolivia, finding places with the highest genetic diversity (southern Ecuador and northern Peru), places that are a priority for in situ conservation (southern Ecuador and northern Peru) and existing gaps in ex situ collections (southern Ecuador). In a classic study, Ferguson et al. (1998) investigated the relationship between ecogeography and genetic diversity in the wild lentil genepool. The distribution and genetic diversity of Lens culinaris subsp. orientalis show that the subspecies is geographically distributed from western Turkey to Tajikistan, but if the genetic diversity is partitioned into 10 clusters, then the bulk of the genetic diversity is focused almost entirely in the western part of the fertile crescent; thus, in this case, ecogeographic data would not be an adequate proxy for genetic diversity as it would indicate ex situ sampling or the establishment of an in situ genetic reserve across the geographic distribution rather than concentrating both in the western Fertile Crescent, as indicated in figure 41.6. Whether genetic diversity is studied or, as commonly occurs, ecogeography is used as a proxy for genetic diversity, an additional assessment will need to be made of the genetic diversity that exists in natural taxa or populations in order to determine if it is well represented by the samples held in genebanks or by populations represented in protected areas or genetic reserves.

Figure 41.6: Distribution of Lens culinaris subsp. orientalis divided into 10 clusters of genetic diversity. Source: Ferguson et al. (1998).

Assessment of ecogeographic diversity

As there is a lack of knowledge of natural patterns of genetic diversity for the vast majority of crop wild relatives, it is often necessary to employ some form of proxy measure such as ecogeographic diversity (merely because of the prohibitive cost of undertaking genetic diversity studies de novo), even if we know that it might not be an adequate proxy of genetic diversity. Assessing ecogeographic diversity involves the collation of secondary information on the ecology and geography of the species under study (see chapter 14; Guarino et al., 2006; Maxted and Guarino, 2003). The collation and analysis of ecogeographic data is discussed in detail in the original version of chapter 14, along with its update, and so will not be reiterated here.

Recently, Parra-Quijano et al. (2012b) extended the concept of ecogeographic analysis by creating and testing an ecogeographic land characterization map for Spain. The map characterized the habitat preferences of plant species and their adaptations to the environment. Subsequently, this map was used for identifying ecogeographic gaps by comparing those categories represented in ex situ facilities with the categories occurring within the potential niche of the species (Parra-Quijano et al., 2011a). During the collection stage, Parra-Quijano et al. (2011a) identified two populations of Lupinus angustifolius, each occurring in different ecogeographic categories and displaying different phenotypes that may be understood as the adaptation response to these ecological characteristics. Using a similar approach, Ramírez-Villegas et al. (2010) performed a principal component analysis with 19 bioclimatic variables. They took the first two components and divided each into 20 different classes to compare the potential coverage of each species with the germplasm coverage and then assigned numerical scores according to the level of ecological representativeness in germplasm facilities.

Ecogeographic data is useful for determining the habitat requirements of a particular group of plant species and for understanding sympatric distributions of taxa within a genepool. This latter is illustrated in figure 41.7, where a selection of the wild relatives of cultivated tomato (section Lycopersicoides) is grouped according to their bioclimatic requirements. Solanum galapagense (orange polygon) and S. cheesmaniae (aquamarine polygon) are species occurring specifically in the Galapagos Islands (Ecuador). Their niche characteristics diverge from most of the other species in the section, as reflected in the graph. In the case of S. chilense (yellow polygon), part of its habitat requirement coincides with most of the section but a large part also gravitates towards PC1, suggesting a probable adaptation to ecological conditions that is not seen in the rest of the species.

To obtain the most comprehensive view of a taxon’s ecogeographic diversity, data should be collated from herbaria and genebanks (see chapter 14 for a detailed discussion). Herbarium data can be collected from online sources such as GBIF (http://data.gbif.org), personal visits to herbaria, inventories and literature reviews. While germplasm passport data can be obtained from sources such as GENESYS (www.genesys-pgr.org), which brings together information from the germplasm banks in trust with the members of the Consortium of International Agricultural Research Centers (http://singer.cgiar.org), the European ex situ collections (EURISCO, http://eurisco.ecpgr.org) and the plant germplasm system of the United States (www.ars-grin.gov/npgs/searchgrin.html).

Ecogeography is also useful for “predictive characterization”, proposing inferences based on ecological adaptations and the potential identification of desirable traits for adaptation to abiotic and biotic conditions of interest for breeders (such as resistance to insect pests). A specific application of predictive characterization is focused identification of germplasm strategy (FIGS) (see www.figstraitmine.org). This approach combines the information available on climate and ecogeography, species distribution and the distribution of a particular trait of interest (such as resistance to pests or diseases) in order to create environmental profiles of the habitats in which a given population (genotype) might have evolved. FIGS has been used to successfully identify seven new alleles for resistance to powdery mildew (genePm3) from an initial number of 16,089 wheat accessions (see Bhullar et al., 2009). The use of the FIGS methodology can thus aid breeders in identifying the in situ populations or ex situ accessions of crop landraces or wild relatives most likely to contain the traits of interest (MacKay and Street, 2004).

Figure 41.7: Kernel-density plot of the first two dimensions of an assessment based on variables derived from temperature and precipitation, for the section Lycopersicoides, genus Solanum

Threat assessment

This is an important facet of gap analysis, because it facilitates the relative assessment of conservation priorities: those taxa most threatened will have a higher conservation priority than those less threatened.

Threat assessment is now routinely carried out through the application of the IUCN Red List Categories Version 3.1 (IUCN, 2001). Using associated data from herbaria or genebank accessions as a basis for the assessment, the most likely criteria to be used are criterion B (geographic range in the form of either extent of occurrence or area of occupancy) and D (very small or restricted population). Each year more taxa are included in global, regional and national Red Data lists, but until recently only a relatively small number of plant taxa (and particularly CWR taxa) had been assessed.

Recently, a project funded by the European Community specifically undertook an IUCN Red List assessment for priority European CWR diversity (Bilz et al., 2011; Kell et al., 2012). The CWR species were selected on the basis of being native to Europe, the economic importance of their related crop, their relative relationship to the crop (i.e., ease of trait transfer to the crop) and their inclusion on Annex I of the International Treaty on Plant Genetic Resources for Food and Agriculture (“the Treaty”). The final list of CWR species to be assessed comprised 591 species in 25 crop genepools/groups, 188 of which were endemic to Europe. However, 20 were subsequently assessed as “Not Applicable”, either due to their marginal occurrence in Europe or because they were introduced to Europe after 1500 AD. The status of the remaining 571 species was assessed at two regional levels: geographical Europe (572 species) and the 27 EU member states. At the European level, 313 (55%) were assessed as “Least Concern”, 166 (29%) as “Data Deficient”, 26 (5%) as “Near Threatened”, 22 (4%) as “Vulnerable”, 25 (4%) as “Endangered” and 19 (3%) as “Critically Endangered”. The priority in Europe would therefore be to focus attention on the 66 “Critically Endangered”, “Endangered” and “Vulnerable” species. This, it is hoped, will prove a useful resource for Europe, but the lack of population data is likely to remain a limitation in wider IUCN Red List assessments in other regions.

In the absence of sufficiently detailed population data to undertake an IUCN Red List assessment, a relatively large ecogeographic dataset can be used to make a more tentative threat assessment. Maxted et al. (2005) proposed and applied a technique referred to as taxon vulnerability assessment in situations where there was insufficient data to permit a formal IUCN Red Listing. Vulnerability to a loss of genetic diversity (and even extinction) can be assessed by compounding seven criteria as follows:

1. Rarity is estimated from the total number of herbarium specimens and genebank accessions of each taxon in the ecogeographic database. It is assumed that in most cases this will provide a true indicator of actual occurrence, unless there is evidence to the contrary or the taxon is cultivated or very rare, both of which cases can lead to relative over-sampling by collectors.

2. Distributional range is calculated by taking a given radius around each collecting locality and then merging the resulting circles, providing an approximation of the overall species range using the methodology described by Hijmans and Spooner (2001).

3. Representation in ex situ collections compared to herbarium collections can provide a relative estimate of whether a species’ genepool is sufficiently sampled ex situ.

4. The relative geographic coverage of ex situ collections is compared to the geographic breadth, based on ex situ conserved accessions and herbarium samples.

5. Intra-species coverage of ex situ collections can be used comparatively for species that have multiple infra-specific categories to estimate if each infra-specific taxon is adequately represented in ex situ collections.

6. The usage potential of a species is particularly relevant for the conservation of plant genetic resources, where there will be a particular incentive for conserving those species with the highest use potential. It might also be the case that species with high use potential are more likely to be threatened due to excessive utilization.

7. An assessment of taxon extinction can be estimated by applying Solow’s equation (Solow, 1993) as proposed by Burgman et al. (1995), which uses a combination of collection timing, frequency and specimen numbers.

Each of these seven criteria is assessed for each species, and a numerical score is recorded. These are then summed to establish relative taxon vulnerability.

Assessment of current conservation strategies

The diversity occurring naturally in situ can be compared to the diversity currently conserved in order to assess the efficiency of both in situ and ex situ conservation techniques and so identify the weaknesses (gaps).

Assessment of in situ conservation

Within the context of plant conservation, the definition of in situ conservation provided by the Convention on Biological Diversity (CBD) (1992) includes two distinct conservation techniques: protected area (genetic reserve) conservation for wild species and on-farm conservation in the case of traditional crop varieties, widely known as landraces. Genetic reserve conservation maintains wild species in their natural surroundings, usually within an existing protected area where the site has been selected and is managed and monitored to maintain the genetic diversity of the target taxa.

• Genetic reserve/protected area assessment: This involves a review of existing protected areas and the species within them that are being actively managed for conservation. As few centralized databases detail which species are being actively conserved in the world’s protected areas, obtaining detailed knowledge of the protected areas in the target area is likely to involve contacting the managers of these areas to ascertain if particular species are present and being actively managed and monitored. It is increasingly possible to use geographic information system (GIS) techniques to compare the protected area spatial layers from the World Database of Protected Areas (http://protectedplanet.net) with species distributional data to predict which priority species are found in which protected areas. But, having matched these datasets, there would still be a need to contact the managers of specific protected areas in order to confirm that the species predicted to be present are indeed present.

• On-farm conservation assessment: Similarly, the on-farm conservation of landraces requires reviewing existing on-farm conservation projects and the crop species included. The review of on-farm conservation is likely to be simpler than the review of protected areas due to the more limited number of on-farm conservation projects and the relative ease of discovering which crop species are included.

In terms of assessing in situ conservation, it must be admitted that there are so few locations where there is currently any effective in situ conservation of agrobiodiversity, the sites where natural target-taxon diversity is located are almost always likely to be those sites where in situ conservation is recommended.

Assessment of ex situ conservation

To assess the gross completeness of material conserved ex situ, a comparison should be made between the unique samples recorded from herbaria versus those from genebanks, although these sources of information can become outdated (Bettencourt et al., 1989). Other sources of material that is currently being conserved can be obtained from botanic gardens, as well as from national and international catalogues, databases and web sites. Herbarium data can be collected from online sources (such as GBIF, http://data.gbif.org), personal visits to herbaria, inventories and literature reviews. Germplasm passport data can also be obtained from sources like GENESYS (www.genesys-pgr.org), which brings together information from the germplasm banks in trust with the members of the Consortium of International Agricultural Research Centers (http://singer.cgiar.org), the European ex situ collections (http://eurisco.ecpgr.org) and the plant germplasm system of the United States of America (www.ars-grin.gov/npgs/searchgrin.html). Other initiatives, such as that of the European Native Seed Conservation Network are now in the process of providing additional wild-species genebank accessions associated with botanic gardens through the European Native Seed Conservation Network Database (ENSCOBASE) (http://enscobase.maich.gr). Depending on the extent and scope of the study, local sources could also be considered for obtaining data. As an illustration, figure 41.8 shows the sampling deficiencies for the crop wild relatives of tomato. The bold line in the graph represents the average representativeness of the dataset used, and the dashed line is the total representativeness. Taxa located below the bold line have fewer samples in ex situ holdings compared with the number of samples in herbaria (ex situ sampling deficient). In this case, the “gap” under-collected species are S. habrochaites, S. corneliomulleri, S. juglandifolium and S. cheesmaniae.

Figure 41.8: Number of genebank samples of the wild relatives of tomato (Solanum lycopersicum) versus total samples (total is calculated by adding germplasm and herbarium samples)

When using ecogeographic distribution as a proxy for data on genetic diversity, the ideal ex situ collection would contain samples from geographically diverse sites spread throughout the entire range of distribution of the crop or species. Such a proxy can be calculated using herbarium and genebank collection data, and the circular area statistic (CA) (Hijmans et al., 2001). CA is calculated by assigning a circle of set radius around each collection, and the total area of those circles for all collections is calculated (counting overlapping regions only once). For collections that are geographically highly concentrated, the CA is relatively low compared to a set of collections that are geographically distributed over a wide region (due to greater overlap in clumped collections). This statistic can be used to compare germplasm collections with all collections (germplasm and herbarium collection data) in order to identify how geographically representative the germplasm collection is. Germplasm collections whose geographic distribution is representative should have a CA statistic similar to that of the entire collection. Conversely, germplasm collections where the geographic distribution is poorly represented would have a low CA compared to the entire collection, due to concentrated ex situ collecting in regions representing only a subset of the wider distribution of the species.

Ramírez-Villegas et al. (2010) used a quantitative approach to determine the requirements for ex situ conservation of the genus Phaseolus, assessing three concepts per taxon level: sampling representativeness, geographical and environmental coverage. Sampling representativeness consists of a comparison between the total number of populations sampled and those sampled as genebank accessions. This is referred to as the sampling representativeness score (SRS). Ramírez-Villegas et al. estimated geographical coverage by comparing the potential distribution of the taxon with the circular statistic (CA) within a 50km radius of the germplasm samples, this value is stored as the geographic representativeness score (GRS). The environmental coverage representativeness (or environmental representativeness score, ERS) is estimated by overlapping the whole geographic extent of the taxon with the two principal components of 19 bioclimatic variables and the corresponding germplasm accessions. The final priority score (FPS) is calculated by averaging SRS, GRS and ERS. The lower the value of FPS, the higher the priority for conserving the taxon. Additionally, Ramírez-Villegas et al. (2010) validated the priority for the conservation list produced after applying this methodology by comparing it with the scores given by a recognized expert on Phaseolus (Dr Daniel Debouck), obtaining a high correspondence with his priorities for conservation (reported as rho=0.788).

Reformulation of conservation strategy

An assessment of the effectiveness of current conservation coverage in relation to natural in situ diversity identifies the element of diversity that is under-conserved, i.e. the “gaps” in the existing conservation strategy, and helps refocus the strategy to conserve the maximum diversity and to fill these gaps. The revised priorities are likely to require complementary in situ and ex situ conservation actions to ensure the comprehensive conservation of the target taxon’s genepool.

In situ conservation priorities

Genetic reserve/protected area

The location and establishment of genetic reserves should be based on the gap analysis outlining the most appropriate location for the genetic reserve. Genetic reserves will commonly be established within existing protected areas because (a) these sites already have an associated long-term conservation ethos and are less prone to hasty management changes associated with private land or roadsides where conservation value and sustainability is not a consideration, (b) it is relatively easy to amend the existing site management to facilitate genetic conservation of CWR species and (c) it means that creating new conservation sites can be avoided, thereby also avoiding the possibly prohibitive cost of acquiring land that had not previously been managed for conservation (Maxted et al., 2008c). Therefore, the simplest way forward in economic and political terms is to locate genetic reserves in existing protected areas, such as national parks or heritage sites. This is likely to provide some benefit to local people and so is also likely to gain their support when participatory approaches are employed.

On-farm conservation priorities

Traditional crop or landrace richness can be used to indicate priority sites for in situ conservation for the on-farm conservation of landraces. Areas that have a high concentration of landraces (either of multiple or individual crops) are desired for on-farm conservation projects.

Ex situ conservation priorities

Species and areas within a species range that have been under-sampled in ex situ collections are highlighted as priorities for future collection and subsequent ex situ conservation.

 

Future challenges/needs/gaps

In the medium term, the understanding of patterns of genetic diversity and their relation to elements of the landscape will allow us to recognize in more detail where the areas of allele richness and uniqueness are, and if there is a correlation with the local environment, thus confirming the validity of using subrogates of diversity such as ecogeographic methods. These analyses could be performed in genepools or genebank collections that have been largely characterized at the molecular level. Increasing interest in (and need for) conserving key plant species should be prioritized using a gap analysis approach, so that the allocation of resources will target the most important species (whether for food, forage, breeding, cultural matters or any other ecosystem service). And finally, there are different types of refined and structured methodologies for gap analysis in the conservation area. Those methodologies based on computer scripts should use open-source coding and should be user friendly, thereby encouraging their use and avoiding the limitations that licensed software might impose to users with restricted budgets.

Two key global initiatives are currently underway and both have at their heart the gap analysis of agrobiodiversity. The Global Crop Diversity Trust has launched the project on Adapting Agriculture to Climate Change (Guarino and Lobell, 2011), which, in part, aims to systematically sample priority CWR taxa and ensure that the germplasm is stored ex situ in the country of origin, the Millennium Seed Bank and the Svalbard Global Seed Vault. Alongside this, during the 13th regular session of the Commission on Genetic Resources for Food and Agriculture, held in 2011, the FAO was requested to elaborate on the means and opportunities for establishing a global network for in situ conservation and on-farm management of plant genetic resources for food and agriculture in coordination with the Treaty, the Global Strategy for Plant Conservation of the CBD and other relevant stakeholders. Both initiatives use as their starting point the Harlan and de Wet Global Priority Checklist of CWR Taxa and ecogeographic datasets for the included taxa to which the basic gap analysis methodology described above is applied. With so much attention currently being paid to the application of the gap analysis methodology, it should come as no surprise if the methodology itself evolves rapidly, so those wishing to undertake gap analysis should keep abreast of these methodological developments.

 

Conclusion

Although gap analysis is a fairly recent tool for the conservation of plant genetic resources, it is proving to be an effective instrument in the planning of complementary conservation strategies that encompass both in situ and ex situ applications. It has also shown how the study of the passport collection data of herbarium and germplasm accessions, coupled with ecogeographic analyses, can quantify the completeness of current in situ and ex situ conservation actions and identify gaps in conservation diversity at both the taxon and geographic level, which, in turn, helps in the prioritization of future conservation actions.

This basic methodology has been successfully applied to assist the development of national conservation strategies for CWR diversity in the UK (Maxted et al., 2007) and Israel (Barazani et al., 2008), and, at a monographic scale, the African genus Vigna (Maxted et al., 2005), the FAO CWR project (Maxted and Kell, 2009) and the CIAT-IRRI-Bioversity International GapAnalysis project, showing that the methodology is sufficiently robust to yield useful results that can bolster conservation efficiency. If the conservation community is to meet the challenge of the CBD Strategic Plan “Target 13” by 2020, the status of the genetic diversity of crop and livestock agricultural ecosystems and of wild relatives will have to be improved (CBD, 2010) and tools such as the gap analysis methodology described above are likely to prove essential.

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References and further reading

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Balmford A. 2003. Conservation planning in the real world: South Africa shows the way. Trends in Ecology and Evolution 18:435–438.

Barazani O, Perevolotsky A, Hadas R. 2008. A problem of the rich: Prioritizing local plant genetic resources for ex situ conservation in Israel. Biological Conservation 141: 596–600.

Bettencourt E, Konopka J, Damania AB. 1989. Directory of Crop Germplasm Collections. 1. Food LEGUMES. International Board for Plant Genetic Resources, Rome.

Bhullar NK, Street K, Mackay M, Yahiaoui N, Keller B. 2009. Unlocking wheat genetic resources for the molecular identification of previously undescribed functional alleles at the Pm3 resistance locus. Proceedings of the National Academy of Sciences of the United States of America 106: 9519–9525.

Bilz M, Kell SP, Maxted N, Lansdown RV. 2011. European Red List of Vascular Plants. Publications Office of the European Union, Luxembourg.

Brooks TM, Bakarr MI, Boucher T, Da Fonseca GAB, Hilton-Taylor C, Hoekstra JM. 2004. Coverage provided by the global protected area system: Is it enough? Bioscience 54:1081–1091.

Burgman A, Grimson RC, Ferson S. 1995. Inferring threat from scientific collections. Conservation Biology 9:923–929.

Burley FW. 1988. Monitoring biological diversity for setting priorities in conservation. In: Wilson EO, Peter FM, editors. Biodiversity. National Academy Press, Washington DC. pp. 227–230

CBD. 1992. Convention on Biological Diversity: Text and Annexes. Secretariat of the Convention on Biological Diversity, Montreal.

CBD. 2010. Strategic Plan for Biodiversity 2011–2020, Including Aichi Biodiversity Targets. Secretariat of the Convention on Biological Diversity, Montreal.

Dietz RW, Czech B. 2005. Conservation deficits for the continental United States: An ecosystem gap analysis. Conservation Biology 19:1478–1487.

FAO. 2009. The Second Report on the State of the World’s Plant Genetic Resources for Food and Agriculture. Food and Agriculture Organization of the United Nations, Rome.

Ferguson ME, Ford-Lloyd BV, Robertson LD, Maxted N, Newbury HJ. 1998. Mapping the geographical distribution of genetic variation in the genus Lens for the enhanced conservation of plant genetic diversity. Molecular Ecology 7:1743–1755.

Guarino L, Lobell DB. 2011. A walk on the wild side. Nature Climate Change 1:374–375.

Guarino L, Maxted N, Chiwona EA. 2006. A methodological model for ecogeographic surveys of crops. IPGRI Technical Bulletin No. 9. International Plant Genetic Resources Institute, Rome.

Hijmans R, Spooner D. 2001. Geographic distribution of wild potato species. American Journal of Botany 88:2101–2112.

Hijmans RJ, Cruz M, Rojas E, Guarino L. 2001. DIVA-GIS Manual. Version 1.4. A Geographic Information System for the Management and Analysis of Genetic Resources Data. International Potato Center, Lima, Peru.

IUCN. 2001. IUCN Red List Categories Version 3.1. IUCN Species Survival Commission. International Union for Conservation of Nature and Natural Resources, Gland, Switzerland.

Kell SP, Maxted N, Bilz M. 2012. European crop wild relative threat assessment: Knowledge gained and lessons learnt. In: Maxted N, Dulloo ME, Ford-Lloyd BV, Frese L, Iriondo JM, Pinheiro de Carvalho MAA, editors. Agrobiodiversity Conservation: Securing the Diversity of Crop Wild Relatives and Landraces. CAB International, Wallingford, UK. pp. 218–242.

Lipow SR, Vance-Borland K, St. Clair JB, Henderson J, McCain C. 2004. Gap analysis of conserved genetic resources for forest trees. Conservation Biology 18(2):412–423.

Mackay MC, Street K. 2004. Focused identification of germplasm strategy—FIGS. In: Black CK, Panozzo JF, Rebetzke GJ, editors. Cereals 2004. Proceedings of the 54th Australian Cereal Chemistry Conference and the 11th Wheat Breeders’ Assembly, 21–24 September 2004, Canberra, Australian Capital Territory. Royal Australian Chemical Institute, Melbourne. pp. 138–141.

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Margules CR, Nicholls AO, Pressey RL. 1988. Selecting networks of reserves to maximise biological diversity. Biological Conservation 43:63–76.

Maxted N, Guarino L. 2003. Planning plant genetic conservation. In: Smith RD, Dickie JB, Linington SH, Pritchard HW, Probert RJ, editors. Seed Conservation: Turning Science into Practice. Royal Botanic Gardens, Kew, UK. pp. 37–78.

Maxted N, Kell SP. 2009. Establishment of a global network for the in situ conservation of crop wild relatives: Status and needs. FAO Consultancy Report. Food and Agriculture Organization of the United Nations, Rome.

Maxted N, Mabuza-Dlamini P, Moss H, Padulosi S, Jarvis A, Guarino L. 2005. An Ecogeographic Survey: African Vigna. Systematic and Ecogeographic Studies of Crop Genepools 10. International Plant Genetic Resources Institute, Rome.

Maxted N, Scholten MA, Codd R, Ford-Lloyd BV. 2007. Creation and use of a national inventory of crop wild relatives. Biological Conservation 140:142–159.

Maxted N, Dulloo E, Ford-Lloyd BV, Iriondo J, Jarvis A. 2008a. Genetic gap analysis: A tool for more effective genetic conservation assessment. Diversity and Distributions 14:1018–1030.

Maxted N, White K, Valkoun J, Konopka J, Hargreaves S. 2008b. Towards a conservation strategy for Aegilops species. Plant Genetic Resources: Characterization and Utilization 6(2):126–141.

Maxted N, Iriondo J, Dulloo E, Lane A. 2008c. Introduction: The integration of PGR conservation with protected area management. In: Iriondo JM, Maxted N, Dulloo E, editors. Plant Genetic Population Management. CAB International, Wallingford, UK. pp. 1–22.

Maxted N, Hargreaves S, Kell SP, Amri A, Street K, Shehadeh A, Piggin J, Konopka J. 2012. Temperate forage and pulse legume genetic gap analysis. Bocconea 24:5–36.

Moray C, Maxted N. 2012. Prioritising conservation of crop resources: A case study of African cowpea (Vigna unguiculata). Conservation Letters, In Press.

Parra-Quijano M, Iriondo JM, Torres E. 2012a. Improving representativeness of gene bank collections through species distribution models, gap analysis and ecogeographic maps. Biodiversity and Conservation 21(1):79–96.

Parra-Quijano M, Iriondo JM, Torres E. 2012b. Ecogeographical land characterization maps as a tool for assessing plant adaptation and their implications in agrobiodiversity studies. Genetic Resources and Crop Evolution 59(2):205–218.

Pressey RL, Nicholls AO. 1989. Efficiency in conservation evaluation: Scoring versus iterative approaches. Biological Conservation 50:199–218.

Pressey RL, Humphries CR, Vane-Wright RI, Williams PH. 1993. Beyond opportunism: Key principles for systematic reserve selection. Trends in Ecology and Evolution 8:124–128.

Ramírez-Villegas J, Khoury C, Jarvis A, Debouck DG, Guarino L. 2010. A gap analysis methodology for collecting crop gene pools: A case study with Phaseolus beans. PLoS ONE 5(10): e13497. doi:10.1371/journal.pone.0013497.

Riemann H, Ezcurra E. 2005. Plant endemism and natural protected areas in the peninsula of Baja California, Mexico. Biological Conservation 122:141–150.

Solow AR. 1993. Inferring extinction from sighting data. Ecology 74:962–964.

van Zonneveld M, Scheldeman X, Escribano P, Viruel MA, van Damme P, Garcia W, Tapia C, Romero J, Sigueñas M, Hormaza JI. 2012. Mapping genetic diversity of cherimoya (Annona cherimola Mill.): Application of spatial analysis for conservation and use of plant genetic resources. PLoS ONE 7(1): e29845. doi:10.1371/journal.pone.0029845.

Venevsky I, Venevskaya S. 2005. Species-energy theory for vascular plants and application of this theory for mapping of national biodiversity hotspots. In: Macroecological tools for Global Change Research. Virtual Institute for Macroecology, Institute for Climate Impact Research, Potsdam. pp. 138–139.

Vincent H, Wiersema J, Dobbie S, Kell SP, Fielder H, Castañeda Alvarez NP, Guarino L, Eastwood R, León B, Maxted N. 2012. A prioritised crop wild relative inventory as a first step to help underpin global food security. Proceedings of the Royal Society B. In preparation.

Whitehouse K. 2011. An ecogeographic and GIS analysis of cereals: Avena, Aegilops, Hordeum, Secale and Triticum. Unpublished Master’s Thesis. University of Birmingham, Birmingham, UK.

 

Internet resources

Biodiversity and WorldMap: www.nhm.ac.uk/research-curation/research/projects/worldmap/index.html

Botanic Gardens Conservation International (BCGI): www.bgci.org

CIAT-IRRI-Bioversity International GapAnalysis project, ex situ gap analysis results of 13 crop gene pools: http://gisweb.ciat.cgiar.org/gapanalysis

Consortium of International Agricultural Research Centers (SINGER): http://singer.cgiar.org

EURISCO, European ex situ collections: http://eurisco.ecpgr.org

European Native Seed Conservation Network (ENSCOBASE): http://enscobase.maich.gr

FAOSTAT, agricultural statistics and data: www.faostat.fao.org

GBIF, global biodiversity data: http://data.gbif.org

GENESYS, global database of major ex situ genebank holdings: www.genesys-pgr.org

GlobeCover, European Space Agency global land cover map: http://ionia1.esrin.esa.int

Harlan and de Wet Global Priority Checklist of CWR Taxa: www.cwrdiversity.org

Harmonized World Soil Database v 1.2: www.iiasa.ac.at/Research/LUC/External-World-soil-database/HTML

International Panel on Climate Change (IPCC), climate-change forecasts: www.ipcc-data.org/ddc_climscen.html

IUCN Red List, database of extinction threat assessments: www.iucnredlist.org

JSTOR Herbaria: http://plants.jstor.org

STRM 90m Digital Elevation Data: http://srtm.csi.cgiar.org/index.asp

The Plant List, working list of all known plant species: www.theplantlist.org

Tropicos®, herbarium resources, Missouri Botanical Gardens, USA: www.tropicos.org

UNEP WCMC World Database of Protected Areas: www.protectedplanet.net

US Genetic Resources Information Network (GRIN): www.ars-grin.gov/npgs/acc/acc_queries.html

World Database of Protected Areas: http://protectedplanet.net

Worldclim Global Climate Layers, 1km resolution grids of climate and derived bioclimate datasets: www.worldclim.org

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Chapter 14: Ecogeographic surveys

N. P. Castañeda Álvarez
International Center for Tropical Agriculture (CIAT) - Bioversity International
Cali, Colombia and
School of Biosciences, University of Birmingham,
Edgbaston, Birmingham, UK
E-mail: n.p.castaneda(at)cgiar.org
H. A. Vincent
School of Biosciences, University of Birmingham,
Edgbaston, Birmingham, UK
E-mail: holly.vincent(at)gmail.com
S. P. Kell
School of Biosciences, University of Birmingham,
Edgbaston, Birmingham, UK
E-mail: s.p.kell(at)bham.ac.uk
R. J. Eastwood
Millennium Seed Bank Seed Conservation Department, Royal Botanic Gardens
Kew, UK
E-mail: r.eastwood(at)kew.org
N. Maxted
School of Biosciences, University of Birmingham,
Edgbaston, Birmingham, UK
E-mail: n.maxted(at)bham.ac.uk

	2011 version (0.9 MB)	1995 version (0.7 MB)
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This chapter is a synthesis of new knowledge, procedures, best practices and references for collecting plant diversity since the publication of the 1995 volume Collecting Plant Diversity: Technical Guidelines, edited by Luigi Guarino, V. Ramanatha Rao and Robert Reid, and published by CAB International on behalf of the International Plant Genetic Resources Institute (IPGRI) (now Bioversity International), the Food and Agriculture Organization of the United Nations (FAO), the World Conservation Union (IUCN) and the United Nations Environment Programme (UNEP). The original text for Chapter 14: Ecographic Surveys, authored by N. Maxted, M. W. van Slageren and J. R. Rihan, has been made available online courtesy of CABI. The 2011 update of the Technical Guidelines, edited by L. Guarino, V. Ramanatha Rao and E. Goldberg, has been made available courtesy of Bioversity International..

Please send any comments on this chapter using the Comments feature at the bottom of this page. If you wish to contribute new content or references on the subject please do so here.

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Internet resources for this chapter

 

	Tomato (Solanum lycopersicum L.) wild relatives potential richness map. This maps depicts the number of taxa that are potentially found per unit of area. Darker colours represent greater richness of the tomato genepool. (Map by N. P. Castañeda Álvarez/May 2012)

Abstract

Since the completion of the original version of this chapter, even greater emphasis has been placed on the conservation and exploitation of the broader crop genepool and, as such, ecogeography remains a critical tool in formulating effective and efficient conservation strategies, although, increasingly, ecogeographic surveys are seen as an element within a more comprehensive systematic gap analysis (see chapter 41 in the 2011 version of these Technical Guidelines). However, ecogeographic techniques themselves have advanced significantly since the 1995 chapter on ecogeographic surveys, particularly in terms of information availability and management. Most notably, the affordability in terms of cost, timing and resolution of geographical information systems (GIS), as well as the improvement in (and lower costs for acquiring) hardware and software, now makes ecogeographic analysis a routine task for the agrobiodiversity conservationist. In this updated chapter on ecogeographic surveys, ecogeography itself is redefined and the relationship between ecogeography and gap analysis is reviewed. Progress in the current status of nine key areas is examined: (1) selection of target taxa, (2) ecogeographic database standards, (3) ecogeographic data availability, (4) collection methods for ecogeographic data, (5) on-line gazetteers, (6) threat assessment, (7) GIS analysis and prediction, (8) assessing the impact of climate change and (9) germplasm use. There have been significant advances in ecogeographic techniques in recent years, they remain critical to the conservation and utilization of plant genetic resources.

 

Introduction

The 1995 chapter starts with the statement that “plant collectors are like detectives: they gather and analyse clues in order to trace plants of interest.” This statement remains essentially as true today as it was in 1995 when the original text was compiled, although technically, today we may wish to stress more the tracing of genetic diversity within the broader crop genepool. Conservationists do not just look randomly for the diversity they are targeting; the planning and targeting of conservation is associated with careful preparation to identify where to sample germplasm, where to establish genetic reserves, and how precisely the resource will be conserved and later used. Essentially, ecogeographic surveys use historic provenance and population data as a basis for planning and targeting future conservation: past data are predictive. For example, given the requirement to conserve the perennial wild relative of garden peas Vavilovia formosa (Steven) Fed., we know that this species has historically always been found growing on limestone scree slopes above 2000 metres in Southwest Asia, so this is the habitat and location where we can expect to be able to collect germplasm for ex situ conservation or to conserve it in situ today. Although having made this simple point, it must be admitted that there are areas of the world that remain under-surveyed even today (such as eastern Turkey and the Democratic Republic of the Congo), and so past data are imperfect. But here we can use predictive modelling based on the available historic data to more effectively plan and implement conservation.

Soon after publication of the original chapter, it was clear that the definition of ecogeography needed to be amended; therefore, it is good to have the opportunity here to revise the definition. The first limitation was that the definition implied that only ecological, geographical and taxonomic information was used, but anyone that has undertaken an ecogeographic survey or study will know that knowledge of the pattern of genetic diversity for the target taxon is equally relevant to conservation planning. Given the conservation goal of maximizing genetic diversity, it could be argued that without full consideration of patterns of genetic diversity (where they are known), information based on ecological, geographical and taxonomic evidence could be misleading and could waste scarce conservation resources. The second problem with the definition is the separation of collection from broader conservation. It is now universally accepted that the application of in situ and ex situ techniques are necessary and complementary, the one providing (among other benefits) a security back-up for the other. The collation and analysis of ecogeographic data is an equally critical precursor for effective ex situ and in situ conservation, so both conservation strategies deserve equal weight in the definition. Therefore, the following revision of the definition of ecogeography is proposed:

Ecogeography is a process of gathering and synthesizing information on ecological, geographical, taxonomic and genetic diversity. The results are predictive and can be used to assist in the formulation of complementary in situ and ex situ conservation priorities.

When the 1995 text was prepared, there was already extensive literature on “gap analysis” (i.e., how to identify areas in which selected elements of biodiversity are underrepresented, e.g., see Margules 1989). This body of literature has continued to be extended (i.e., Balmford 2003; Brooks et al. 2004; Dietz and Czech 2005; Margules and Pressey 2000; Riemann and Ezcurra 2005). This literature was originally applied to indigenous forests, particularly on small islands rich in endemic species. However, Maxted et al. (2008) showed how the existing methodology might be adapted for more general conservation and proposed a specific methodology for the genetic gap analysis of crop wild relatives (CWR), which involves four steps: (a) identify priority taxa, (b) identify ecogeographic breadth and complementary hotspots using genetic diversity, distribution and environmental data, (c) match current in situ and ex situ conservation activities with the identified genetic diversity, ecogeographic breadth and complementary hotspots to identify the so-called conservation “gaps” and (d) formulate a revised in situ and ex situ conservation strategy. Gap analysis has rapidly established itself as the methodology for conservation planning (FAO 2009; Parra-Quijano et al. 2011; Ramírez-Villegas et al. 2010), not replacing ecogeography, but in reality subsuming ecogeography as an element within the gap analysis protocol. As such, ecogeographic surveys will continue to be routinely undertaken, but increasingly within a gap analysis context. Therefore, an additional chapter (Chapter 41 entitled “Gap Analysis: A Tool for Genetic Conservation”) has been added to these Guidelines, which reviews the gap analysis methodology. It should be seen as a sister chapter to read in conjunction with this chapter.

 

Current status

Although ecogeographic surveys are a necessary precursor to the conservation of both crop and wild plants, the increasing interest in the conservation and use of CWR diversity means that the techniques used to collate ecogeographic data have advanced significantly since 1995, most notably in the methods of data collation and subsequent analysis. The use of ecogeographic techniques for crop and wild-plant diversity was thoroughly reviewed by Guarino et al. (2006) and for crop wild relatives by Maxted and Guarino (2003). The following is a review of the major innovations since the original text was published in 1995, demonstrating the continuing value of ecogeographic techniques and the novel opportunities and challenges they offer for the conservationist.

Selection of target taxa

Any conservation action requires a clear target and, as mentioned in the 1995 text, this may be established in the project commission, but the recent creation of a global priority checklist of CWR taxa (Vincent et al., 2012; www.cwrdiversity.org/home/checklist) should assist conservation planning by having a pre-existing prioritizing list of priority taxa for the major and minor crops of the world. It is referred to as the Harlan and de Wet Global Priority Checklist to acknowledge the pioneering work of Harlan and de Wet (1971) in first proposing the Gene Pool (GP) concept to explain the relative value of species in their potential as gene donors for crop improvement. The database contains background information on 174 crop genepools and 1397 priority CWR species: those deemed priority CWRs as defined by their membership in GP1b or GP2, or Taxon Groups (TG) 1b, 2 or 3. There are also a limited number of GP3 and TG4 taxa included if they have previously been shown to be useful in breeding. The Gene Pool concept designated the crop itself as GP1a, while GP1b are the wild or weedy forms of the crop that cross easily with it. GP2 are secondary wild relatives (less closely related species from which gene transfer to the crop is possible but difficult using conventional breeding techniques), and GP3 are tertiary wild relatives (species from which gene transfer to the crop is impossible, or if possible, requires more advanced techniques, such as embryo rescue, somatic fusion or genetic engineering).

If the necessary crossability information is lacking, as it is for most crop genepools, then the taxon group concept (Maxted et al. 2006) can substitute for the genepool concept. The taxon group concept employs the taxonomic hierarchy as a proxy for taxon genetic relatedness and thus crossability, so TG1a is the crop, TG1b is the same species as the crop, TG2 is the same series or section as the crop, TG3 is the same subgenus as the crop, and TG4 is the same genus. The assumption is that the taxonomic classification is related to crossability, and if genepool and taxon group concepts are compared for those crop genepools where a genepool concept is known, this assumption seems well founded. The data recorded in the database for each crop include the associated published genepool concept or taxon group concept, complete with references, and confirmed and potential CWR taxa used in crop breeding and improvement. Then, for each taxon (including crop taxa) the following information is recorded: accepted name (standardized to the Plant List, www.theplantlist.org), taxonomic classification, common name, main synonyms, seed storage behaviour, geographic distribution, key herbaria holding specimens (derived from geographic distribution) and utilization data with references. It is expected that the checklist will be dynamic in that as new genepool or taxon group concepts are published, authors will be able to upload their concept to the online database and make it readily available to the user community. It will act as a guide to those establishing geographical and genepool CWR conservation priorities in individual countries or crops.

Ecogeographic database standards

Ecogeographic data, in part, describe the population location and, therefore, the ecology and environment where particular taxa may occur. It is common for these data to be organized under different standards, data structures and file formats, usually capturing different sorts of details. Thus, when merging ecogeographic information taken from different sources, attention is needed to combine similar information correctly and avoid combinations that might lead to mistakes or data loss.

Increasingly, global efforts to standardize biological information have promoted standards to improve the recording and exchange of data within databases. The Taxonomic Databases Working Group (TDWG) (www.tdwg.org) is one of the main initiatives for developing and promoting standards that enable the exchange of biological records. The current standard recommended by TDWG for biological records is the “Darwin Core” (http://rs.tdwg.org/dwc/index.htm). The standard “Access to Biological Collection Data” (http://wiki.tdwg.org/twiki/bin/view/ABCD/AbcdIntroduction) is also recommended; however, it does not comply with all the specifications of the working group. Alternative systems to organize, store and distribute biological information are also available. Botanical Records and Herbarium Management (BRAHMS) (http://dps.plants.ox.ac.uk/bol/brahms/Home/Index) is a well-known system, focused on herbaria, living collections and seed-bank data that enables users to manage, analyse and share their data.

Despite the relative diversity of standards available, interoperability between standards is becoming common, allowing data repositories to gather information and benefiting users who require access to biological records.

Online ecogeographic data availability

Perhaps one of the key changes between 1995 and today is the exponential growth of web-enabled ecogeographic datasets, most notably the Global Biodiversity Information Facility (GBIF) established in 2001 (http://data.gbif.org), which provides extensive access to global taxon nomenclature, taxon and accession distribution, conservation and environmental data. Established to encourage free and open access to biodiversity data via the internet and now encompassing a network of 57 countries and 47 organizations, GBIF promotes and facilitates the mobilization, access, discovery and use of information about the occurrence of organisms over time and across the planet. It facilitates the digitization and global dissemination of primary biodiversity data (e.g., data from natural history collections, libraries and databases). GBIF taxon searches can be limited to the country or countries of interest, or data can be downloaded and the necessary records extracted. To access data via GBIF (http://aegro.jki.bund.de/aegro/index.php?id=195):

• Use the search facility to find data on your taxon of interest.

• When you click on the taxon link provided, you will be asked to accept the terms of the GBIF user license agreement. Read the terms and then click on “Accept terms”.

• You will then be provided with a number of links to data related to your taxon. Follow these links to find the information needed. For example, click on “occurrences” to find a list of recorded occurrences of the taxon (note that the map function will not show distribution data unless there are coordinates available).

• To save a list of the occurrences, you can download the results in different table formats (i.e., as an MS Excel spreadsheet or as tab- or comma-delimited text).

• Before you download the data, you can limit the fields included by unchecking the fields that are not needed. However, it is advisable to download all data, then delete or hide those fields that are not needed, in case the data might be of use at a later date.

GBIF has rapidly become the largest web-enabled supplier of nomenclatural, distribution, conservation and environmental data, drawing information from a growing global network of natural history collections and associated agencies.

Example of specimen and label for digitization from Herbário João de Carvalho e Vasconcellos, Instituto Superior de Agronomia, Lisbon, Portugal (Photos: N. P. Castañeda Álvarez)

The plant information accessible via GBIF is primarily derived from digitized herbarium or field records. There are other initiatives being developed to provide access to herbarium specimens, as well as national programs that are digitizing their collections and making the data available via the internet (table 14.1). Therefore, once the priority list of herbaria and gene banks that are likely to contain the necessary CWR collections have been identified, the herbarium and gene bank websites should be visited to see if the required data are online, which would further reduce the need and expense of visits to herbaria and gene banks.

 

Table 14.1: Data on Herbarium and National Collections Being Digitized for Internet Access

Name	Description			Web Address
JSTOR Plant Science	images of specimens from 155 institutions			http://plants.jstor.org
Databases on crop gene bank holdings
GENESYS	a global portal to germplasm accession holdings of plant genetic resources for food and agriculture		www.genesys-pgr.org
European Plant Genetic Resources Search Catalogue (EURISCO)			http://eurisco.ecpgr.org/nc/home_page.html
System-wide Information Network for Genetic Resources (SINGER)			http://singer.cgiar.org
Genetic Resources Information Network of the United States Department of Agriculture (GRIN)			www.ars-grin.gov/npgs/searchgrin.html
Wild plant species gene bank databases
ENSCONET			http://enscobase.maich.gr
Botanic Garden Conservation International (BGCI) database			www.bgci.org/plant_search.php
National programs
Russia	AgroAtlas		www.agroatlas.ru
Brazil	CRIA		www.cria.org.br
Japan	NIAS		www.gene.affrc.go.jp/databases_en.php
Mexico			www.biodiversidad.gob.mx/genes/proyectoMaices.htm
Other (Taxanomic groups, herbaria, etc.)
Harold and Adele Lieberman Germplasm Bank (cereals)		www.tau.ac.il/lifesci/units/ICCI/genebank1.html
Manchester Museum		http://emu.man.ac.uk/mmcustom/BotQuery.php
Millennium Seed Bank, Kew		www.kew.org/science-conservation/save-seed-prosper/millennium-seed-bank/index.htm
National History Museum, UK		www.nhm.ac.uk/research-curation/collections/departmental-collections/botany-collections/search/index.php
Royal Botanic Gardens Kew		http://apps.kew.org/herbcat/navigator.do
Royal Botanical Garden of Edinburgh		www.rbge.org.uk/databases
SolanaceaeSource		www.nhm.ac.uk/research-curation/research/projects/solanaceaesource
United States Virtual Herbarium		http://usvirtualherbarium.org
Virtual Australian Herbarium		http://plantnet.rbgsyd.nsw.gov.au/HISCOM/Virtualherb/virtualherbarium.html#Virtual

 

One consequence of the recent efforts to digitize specimen and accession records and to make them available through multiple web-enabled databases and portals is that the same information may be duplicated in several sites. Care should be taken to eliminate duplicates if they are likely to bias subsequent analyses and give a false impression of the actual current conservation status of target taxa.

As shown in figure 14.1, not every specimen’s passport information from a particular herbarium is available through the internet; therefore, it is necessary to arrange visits to herbaria to query the local databases and also to get information for specimens that are not available through any database.
 

Figure 14.1: Availability of specimen information from the world’s main herbaria through GBIF

Note: P = Muséum National d'Histoire Naturelle, Paris; NY = New York Botanical Garden, New York; LE = Karmarov Institute, St Petersburg; K = Royal Botanic Gardens, Kew; MO = Missouri Botanical Garden, St Louis; GH = Harvard University, Cambridge, MA; US = Smithsonian Institution, Washington DC; L = Nationaal Herbarium Nederland, Leiden; BR = National Botanic Garden of Belgium, Meise; F = Field Museum of Natural History, Chicago; PE = Institute of Botany, Chinese Academy of Sciences, Beijing; E = Royal Botanic Garden, Edinburgh; BO = Herbarium Bogoriense, Cibinong; MANCH = University of Manchester, Manchester; MA = Real Jardín Botánico, Madrid. Full contact addresses are provided at Index Herbariorum: http://sciweb.nybg.org/science2/IndexHerbariorum.asp. Source: Thiers (n.d.) and GBIF (n.d.).

Methods for collecting ecogeographic data

	Figure 14.2: Example of the photographic approach for a specimen of Solanum anguivi
	Source: Courtesy of Instituto de Investigação Científica Tropical–Jardim Botânico Tropical, Lisbon, Portugal.

In the 1995 text, it was largely assumed that the way to collate ecogeographic data was for the conservationist to select priority herbaria or gene banks, visit the priority institutions, select specimens or accessions (largely on the basis of quality of ecogeographic data) and then type the selected specimen or accession passport data into a computer. This is still likely to be the most commonly used approach. However, currently this is not the only method that can be employed; the project on Adapting Agriculture to Climate Change, led by the Global Crop Diversity Trust (Guarino and Lobell 2011), is using an approach that is based more on photography (see figure 14.2). This involves visiting the priority herbaria, photographing the selected specimens, and then, back at base, working out the latitude and longitude necessary for GIS analysis (see Annex A for more detailed instructions). This approach has the obvious advantage of being relatively quick, therefore reducing the time the conservationist needs to spend at the host herbarium, and it provides a permanent image of the specimen, which can be checked if the identification is thought to be incorrect. It also means that specimen identification in the herbarium is not as critical because the image may later be seen and validated by an expert.

The Adapting Agriculture to Climate Change project has also defined the minimum threshold at 20 specimens, to produce a reliable distribution model representing the potential geographic areas in which the species might be found (Hernandez et al. 2006; Pearson et al. 2007; Wisz et al. 2008). Further, the project has extended the list of ecogeographic data that can be obtained from herbarium specimens (see Annex B).

On-line gazetteers

Ecogeographic data gathered through visits to herbaria, exchanges with other researchers, or querying online databases generates lists of locations where a certain specimen has been reported and/or collected. Recent collections might also include precise coordinates taken with GPS. When only a description of the locality is available, it is necessary to use some form of gazetteer to establish the location more precisely, a process that is sometimes called georeferencing.

Choosing which of the available strategies to use depends on the time and resources available. Manual methods (which use book gazetteers that list locations with latitude and longitude, and detailed maps) are still useful, particularly for those who are familiar with the localities where the specimen was originally collected. It is recommended that this strategy be applied to small datasets (i.e., fewer than 100 records) because it is very time consuming. Increasingly, for larger datasets, reliable online services, such as that of the National Geospatial-Intelligence Agency (NGA) (http://earth-info.nga.mil/gns/html), are used. The NGA provides the NGA GEOnet Names Server (GNS), which allows the user to retrieve information based on administrative and locality details. An advantage of this service is that it provides information about the features that are present in the area (e.g., rivers, populated areas, administrative divisions).

More-automated services are also available through the internet. However, care needs to be taken when using these; some are not always available because of lack of maintenance of servers, services that are no longer free, etc. They require data to be submitted in specific formats, and the percentage of locations found is usually low (only 10% to 15%). Biogeomancer (http://bg.berkeley.edu) is a widely known service aiming to improve the quality and quantity of biodiversity data represented in maps. It has an option to georeference records by batch, but first you are required to register on their site. The main advantage of Biogeomancer is that it calculates the uncertainty of each estimated coordinate, allowing the user to decide whether it is useful for his/her analysis. Although not implemented yet, the Google Geocoding API (https://developers.google.com/maps/documentation/geocoding) might also be a tool of interest for georeferencing biological data because of the search algorithms supported by Google and also the ease of creating personalized routines through the API.

Threat assessment

In recent years there have been several initiatives to conserve CWR diversity, most notably the project on Adapting Agriculture to Climate Change (Guarino and Lobell 2011). While this will result in more systematic complementary conservation of CWR diversity in time, the sheer numbers of CWR taxa make comprehensive CWR conservation unlikely. Conservationists will continue to need to prioritize and select which taxa to conserve. One commonly used means of doing this is relative threat.

It is recognized worldwide that biodiversity is currently under severe threat from a range of deleterious factors, such as habitat destruction, degradation and fragmentation, overexploitation, introduction of exotic species, and changes in agricultural practices and land use. However, the predicted impact of climate change is likely to be more catastrophic in terms of the loss of both species and infraspecific genetic diversity. Globally, Hilton-Taylor et al. (2008) estimate that since the year 1500, 115 plant species have become extinct or extinct in the wild, a further 8457 plant species are at risk of extinction, and approximately two-thirds of assessed plants are currently threatened. Further, climate change is predicted to increase average temperatures by 2°C to 4°C in Europe over the next 50 years, which will cause considerable changes in regional and seasonal patterns of precipitation (IPCC 2007). This will have a direct impact on the natural reproductive cycles and distribution of wild plant species, and is predicted to result in a 27% to 42% loss of plant species in Europe by 2080 (Thuiller et al. 2005) and a 60% loss of mountain plant species by 2100 (EEA 2009). Although it is difficult to quantify the loss of genetic diversity within species, it is likely to be very much greater than the loss of species themselves, given that most of the species that remain extant will suffer some loss of genetic diversity (Maxted 2003; Maxted et al. 1997a). It can be argued that CWR species are particularly threatened by climate change because many are associated with disturbed habitats (Hopkins and Maxted 2010) and these habitats are particularly threatened by climate change (Hopkins et al. 2007).

In the face of this level of threat, it is perhaps inevitable that the relative threat to a taxon will be used as a means of prioritizing plant genetic resources for conservation and that threat assessment will either be associated with or become an element of an ecogeographic survey, particularly given that the necessary data required for threat assessment are commonly generated during an ecogeographic survey. For general threat assessment and prioritization of biodiversity, the standardized system of applying the IUCN Red List categories (IUCN 2001) is commonly used. The IUCN threat assessment is data-driven on the basis of different criteria under which a taxon may be listed, each with distinct data requirements. However, the IUCN criteria assess the entire taxon, commonly at species level, and conservation of the range of genetic diversity within taxa or landraces is not easily considered. Various authors have used the IUCN Red List ethos to propose a set of categories and criteria for landrace threat assessment (Joshi et al. 2004; Porfiri et al. 2009).

Even with the limitations of assessing genetic diversity using the IUCN Red List categories, the technique is useful in distinguishing species-level priorities. The first extensive IUCN Red List assessment of CWR diversity has recently been published for European species (Bilz et al. 2011; Kell et al. 2012). In total, 571 native European CWR of high-priority human and animal food-crop species were assessed: 313 (55%) were assessed as Least Concern, 166 (29%) as Data Deficient, 26 (5%) as Near Threatened, 22 (4%) as Vulnerable, 25 (4%) as Endangered and 19 (3%) as Critically Endangered. All assessments have been published on the IUCN Red List website (www.iucnredlist.org). The most common threatening factors recorded were intensive “livestock farming and ranching”, increasing “tourism and recreation areas” and development of “housing and urban areas”. Nearly half of the CWR species had at least one accession conserved ex situ but virtually none were actively conserved in situ in protected areas. It would be useful if this initiative were repeated in other regions, as readily available Red List assessments would facilitate the ecogeographic process.

GIS analysis and prediction

Geographic information systems (GIS) have proved to be very flexible tools with applications in countless areas, including business, government, the sciences and nongovernmental organizations (ESRI 2012). The use of GIS for agriculture, ecology, biogeography and studies of natural resources has helped us to better understand patterns and relationships between different elements of nature. In particular, GIS has been used in studies of plant genetic resources to identify areas of high diversity (Maxted et al. 2004; Ocampo et al. 2007; Scheldeman et al. 2007; and chapter 15/16 in these updated Guidelines), species requiring further conservation (Dulloo et al. 1999; Jarvis et al. 2003), potential areas to collect germplasm (Ferguson et al. 1998; Jarvis et al. 2005; Parra-Quijano et al., 2011; Ramírez-Villegas et al. 2010), suitable areas for in situ conservation (Draper et al. 2003; Maxted 1995; Peters et al. 2005) and levels of threats affecting plant species (Jarvis et al. 2008), as well as creating informative compilations such as atlases (Azurdia et al. 2011; Hijmans et al. 2002).

Species distribution modelling (SDM) algorithms are tools frequently used in studies of plant genetic resources because they allow the prediction of areas that meet the environmental conditions required by a particular species. SDM inputs differ by algorithm (table 14.2).

Table 14.2: List of Algorithms for Species Distribution Modelling

Modelling algorithm	Type of input required	Software source
Maxent (Phillips et al. 2006)	Presence and absence data (pseudo-absences allowed)	www.cs.princeton.edu/~schapire/maxent
Bioclim (Nix 1986)	Presence data	http://diva-gis.org
DOMAIN (Carpenter et al. 1993)	Presence data	http://diva-gis.org
Artificial Neural Networks (ANN)	Presence data	http://openmodeller.sourceforge.net
Ecological-Niche Factor Analysis – ENFA- (Hirzel et al. 2002)	Presence data	www2.unil.ch/biomapper
Genetic Algorithm for Rule Set Production –GARP- (Stockwell and Noble 1992)	Presence and absence data	www.nhm.ku.edu/desktopgarp
HABITAT (Walker and Cocks 1991)	Presence data
Generalized Linear Model-GLM-	Presence and absence data	R: package “dismo”, function “glm” R: package “BIOMOD”
Generalized Additive Model (GAM)	Presence and absence data	R: package “mgcv” R: package “BIOMOD”
Mahalanobis Distance (MD)	Presence data	www.jennessent.com/arcview/mahalanobis_grids.htm
Classification Tree Analysis (CTA)		R: package “BIOMOD”
Surface Range Envelope (SRE)		R: package “BIOMOD”
Generalized Boosting Model (GBM)	Presence and absence data	R: package “BIOMOD”
Breiman and Cutler’s random forest for classification and regression (RF)		R: package “BIOMOD”
Flexible Discriminant Analysis (FDA)		R: package “BIOMOD”
Multiple Adaptive Regression Splines (MARS)	Presence and absence data	R: package “BIOMOD”

SDM algorithms require inputs as presence (and absence) data and environmental layers. Sources of environmental layers at the global scale are Worldclim (www.worldclim.org), which offers precipitation and temperature layers (Hijmans et al. 2005), SRTM-CIAT (http://srtm.csi.cgiar.org), containing digital elevation data, and Global Land Cover (http://glcf.umiacs.umd.edu).

Assessing the impact of climate change

Climate change is a global concern, as growing evidence demonstrates that it is already happening and future scenarios calculate that its effects are likely to have a drastic impact on life on earth (IPCC 2001, 2007). Agrobiodiversity is not an exception; it also is likely to suffer genetic erosion and extinction, but crop landrace and CWR diversity are expected to offer sources of traits to adapt crops to climate change (Farooq and El-Azam 2004; Hajjar and Hodgkin 2007; Maxted et al. 1997). However, other taxa might not respond adequately to such effects of climate change as floods, drought, heat and changes in precipitation patterns.

Analysis based on algorithms that model species distribution and on global circulation models (GCMs) permit us to produce estimates on how climate change will affect the environmental conditions where species are found. One such study is by Jarvis et al. (2008), which evaluated the predicted impact of climate change by 2055 on the CWR of groundnut (Arachis), potato (Solanum tuberosum) and cowpea (Vigna), finding that 16% to 22% of all species modelled (316) are at risk of extinction, while a significant number of species might lose more than 50% of their geographic coverage. Perhaps also of concern was the finding that the predicted impact of climate change varied significantly between crops: they found that 24 to 31 (of the 51) Arachis species were projected to become extinct and their distribution area reduced by 85% to 94% over the next 50 years, while Vigna species were predicted to lose only zero to two of the 48 species. This demonstrates the importance of assessing the impact of climate change for all landraces or CWR taxa in threatened areas.

Links to germplasm use

As noted above, in recent years there have been several initiatives to systematically conserve CWR diversity; however, the goal of genetic conservation is not just to maximize the genetic diversity conserved but also to promote its exploitation, and users are more likely to exploit diversity if it is easily accessible and meets their requirements. Two consequences follow from this statement: first, although the two conservation strategies are complementary and should be applied together for any genepool, they are not of equal value in terms of their application for the user. It is well established that users are more likely to exploit ex situ conserved resources because of the ease of access; the greater likelihood that characterization, evaluation and pre-breeding has been undertaken; the fact that in situ conserved resources are likely to be more remote to the user; and seed will only be available for part of the growing cycle. As such, there is a utilization argument for always ensuring that in situ conserved material is also duplicated ex situ. Ex situ conservation is more than a mere safety back-up of in situ conservation.

Second, with limited resources for any form of active conservation, there will always be a need to prioritize target taxa, and if one of the reasons for conservation is utilization, then it can be argued that the conservationist should have a clear idea what the user community requires. Therefore, it can be argued that if it has not already been considered as part of the conservation commissioning process, an additional element of the ecogeographic survey would be a review of the user community’s requirements. And by “user community”, we would mean breeders attempting to address climate change mitigation or changing consumer demands, landscape restorers trying match taxa for planting in a particular locality, or disaster relief agencies or individual farmers trying to replace locally adapted crop landraces. FAO (2009) emphasizes that “Considerable opportunities exist for strengthening cooperation among those involved in the conservation and sustainable use of PGRFA, at all stages of the seed and food chain. Stronger links are needed, especially between plant breeders and those involved in the seed system, as well as between the public and private sectors.” An analysis of user requirements would ensure greater use of conserved material, ensure that conserved germplasm is more than a museum exhibit, secure the long-term future of the conservation action itself and, finally, create opportunities to develop new partnerships that bridge the gap between the conservation and use of CWR and landraces.

 

Future challenges/needs/gaps

It may be that, in time, ecogeographic surveys will be subsumed as a component of the more encompassing and structured gap analysis approach to conservation planning, but whether this is the case or not, the collation, analysis and use of ecogeographic data will remain a key facet of conservation planning.

It seems likely that, with time, the limited availability of passport data for ecogeographic analysis will diminish as more and more natural history collections are digitized and web-enabled, although this is likely to remain limited in smaller herbaria and gene banks where resources are scarce. The problem may be that the analysis of the vast ecogeographic datasets that are available, with such large and complex datasets, will require a new generation of analysis programs, in terms of both multivariate statistic and GIS capabilities. A key question also remains of how much data do we need to analyse in order to obtain a significant answer? How many herbarium records is enough? In the 1995 text, the authors concluded that “There is no specific answer to this question.” But, upon reflection, it now seems that more concrete advice would be helpful. As noted above, for the project on Adapting Agriculture to Climate Change, it is recommended that the passport data from a minimum of 20 specimens per species be included. However, a recent study by Feeley and Silman (2011a) concluded that significantly larger sample sizes (of 75 to 100) are required to accurately map species ranges, although they also note that only around 5% of tropical plant species are represented by more than 20 specimens (Feeley and Silman 2011b). The project on Adapting Agriculture to Climate Change could provide a practical answer to this question.

The ground-breaking paper published by Jarvis et al. (2008) evaluated the predicted impact of climate change on three crop genepools: groundnut (Arachis), potato (Solanum tuberosum) and cowpea (Vigna). They showed that all species modelled are at risk of extinction and some may lose more than 50% of their geographic coverage. An interesting facet of this analysis was that the genepools were predicted to respond significantly differently to climate change. What about a broader survey of crop genepools? We now have a global priority list of 1397 CWR species. How is each of them likely to respond and how will their vulnerability affect conservation priorities? Given the growing concern over the impact of climate change and food security, surely a broader survey is an urgent priority.

Einstein once commented that “God does not play dice” in relation to the formation of the universe. It can be equally argued that neither should conservationists. No conservationists would get into a land cruiser, drive and hope to bump into the plant they hope to conserve. Ecogeography is at the heart of all conservation planning; however, ecogeographic surveys are still too often commissioned on an ad hoc basis. It can be argued that adopting a more strategic approach to global, regional and national conservation planning would not only be effective but would also save scarce conservation resources.

Conclusions

There have been significant advances in the techniques associated with ecogeographic surveys in recent years, particularly in data capture and data analysis, but there are likely to be additional advances in the next few years. There will be a need for a new generation of multivariate statistical and GIS techniques to enable the potentially vast and complex datasets that are likely to become available for routine use in planning conservation activities.

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References and further reading

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Scheldeman X, Willemen L, d’Eeckenbrugge C, Romeijn-Peeters E, Restrepo M, Romero J, Jiménez D, Lobo M, Medina C, Reyes C, Rodríguez D, Ocampo JA, Van Damme P, Goetgebeur P. 2007. Distribution, diversity and environmental adaptation to highland papayas (Vasconcellea spp.) in tropical and subtropical America. Biodiversity Conservation 16:1867–1884.

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Internet resources

Access to Biological Collection Data, standard: http://wiki.tdwg.org/twiki/bin/view/ABCD/AbcdIntroduction

AgroAtlas (Russia): www.agroatlas.ru

Artificial Neural Networks (ANN): http://openmodeller.sourceforge.net

Bioclim (Nix 1986): http://diva-gis.org

Biogeomancer: http://bg.berkeley.edu

Botanic Garden Conservation International (BGCI) database: www.bgci.org/plant_search.php

Botanical Records and Herbarium Management (BRAHMS): http://dps.plants.ox.ac.uk/bol/brahms/Home/Index

CRIA (Brazil): www.cria.org.br

Darwin Core, standard: http://rs.tdwg.org/dwc/index.htm

DOMAIN (Carpenter et al. 1993): http://diva-gis.org

Ecological-Niche Factor Analysis (ENFA) (Hirzel et al. 2002): www2.unil.ch/biomapper

ENSCONET: http://enscobase.maich.gr

European Plant Genetic Resources Search Catalogue (EURISCO): http://eurisco.ecpgr.org/nc/home_page.html

GENESYS: www.genesys-pgr.org

Genetic Algorithm for Rule Set Production (GARP) (Stockwell and Noble 1992): www.nhm.ku.edu/desktopgarp

Genetic Resources Information Network of the United States Department of Agriculture (GRIN): www.ars-grin.gov/npgs/searchgrin.html

Global Biodiversity Information Facility (GBIF): http://data.gbif.org

Global Land Cover: http://glcf.umiacs.umd.edu

Global priority checklist (The Harlan and de Wet Crop Wild Relative Checklist): www.cwrdiversity.org/home/checklist

Google Geocoding API: https://developers.google.com/maps/documentation/geocoding

Harold and Adele Lieberman Germplasm Bank (cereals): www.tau.ac.il/lifesci/units/ICCI/genebank1.html

Index Herbariorum: http://sciweb.nybg.org/science2/IndexHerbariorum.asp

IUCN Red List: www.iucnredlist.org

JSTOR Plant Science (images of specimens from 155 institutions): http://plants.jstor.org

Mahalanobis Distance (MD): www.jennessent.com/arcview/mahalanobis_grids.htm

Manchester Museum: http://emu.man.ac.uk/mmcustom/BotQuery.php

Maxent (Phillips et al. 2006): www.cs.princeton.edu/~schapire/maxent

Mexico: www.biodiversidad.gob.mx/genes/proyectoMaices.html

Millennium Seed Bank, Kew: www.kew.org/science-conservation/save-seed-prosper/millennium-seed-bank/index.htm

National Geospatial-Intelligence: Agency (NGA), NGA GEOnet Names Server (GNS): http://earth-info.nga.mil/gns/html

National History Museum, UK: www.nhm.ac.uk/research-curation/collections/departmental-collections/botany-collections/search/index.php

NIAS (Japan): www.gene.affrc.go.jp/databases_en.php

Plant List: www.theplantlist.org

Royal Botanic Gardens Kew: http://apps.kew.org/herbcat/navigator.do

Royal Botanical Garden of Edinburgh: www.rbge.org.uk/databases

SolanaceaeSource: www.nhm.ac.uk/research-curation/research/projects/solanaceaesource

SRTM-CIAT: http://srtm.csi.cgiar.org

System-wide Information Network for Genetic Resources (SINGER): http://singer.cgiar.org

Taxonomic Databases Working Group (TDWG): www.tdwg.org

United States Virtual Herbarium: http://usvirtualherbarium.org

Virtual Australian Herbarium: http://plantnet.rbgsyd.nsw.gov.au/HISCOM/Virtualherb/virtualherbarium.html#Virtual

Worldclim: www.worldclim.org

 

Annex A. Digital Recording of Passport Data

Before digitization commences:

• Seek and obtain permission from the host herbarium director to photograph the specimens before any specimens are photographed.

• Be careful when manipulating specimens. Curators appreciate keeping the order of the collection intact, and require notification and authorization for the removal of any part of the dried plant.

• Offer to provide the host herbarium with a complete set of the digitized photographs of the specimens and then repatriate the electronic dataset.

Equipment required for digital recording:

• A digital camera, ideally with a minimum resolution of 6 megapixels (mpx).

• Two storage device (SD) cards of at least 1–2 gigabytes (GB). Note, however, that depending on the camera you are using, the size in bytes of the SD card might affect the speed of the camera (the more bytes, the slower the performance). Having two SD cards available during the visit will allow you to take pictures continuously.

• An extra battery for the camera, so one battery can be used while the other one is charging, to avoid delays while waiting for the battery to charge.

• At least one external hard disk to store and back up all images taken during the visit.

• List of target taxa whose specimens are to be digitized. The list should identify priority taxa (for when digitizing time is limited) and, if available, their native range.

• Electronic or paper notebook to record the process of data collation.

• Prepare paper tags with the abbreviations: “Fl”, “Fr” and “Inflo”. Using these tags will allow you to better capture the phenological status of the sample when digitization is taking place. Use “Fl” when the specimen is flowered, “Fr” when it has fruits and “Inflo” for the family Poaceae and when it is not possible to distinguish between flowering and fruiting.

Selecting the specimens to photograph:

• First identify the system the herbarium follows to organize the collection (i.e., alphabetically, monocots and dicots separated, APGIII, etc.) and plan the digitization of the target taxa within the time available. Avoid over-digitization of some taxa at the expense of neglecting other priority taxa.

• When you are familiar with the organization of the collection, start with the highest priority taxa.

• The herbaria are likely to have tens, hundreds or even thousands of specimens of the priority taxa, so select only those to photograph that have the highest quality and the most complete passport data that can be digitized for latitude and longitude accurately (for example, this can be achieved for “10km NW from Cali”, but not for “20 minutes from Cali”). If a taxon is particularly rare or a specimen has some unique characteristic, then it might be worth digitizing a specimen with inferior passport data.

Recommendations when taking pictures:

• Use the maximum resolution your camera offers. It is desirable to have at least 6 mpx.

• Photograph the label and/or annotations of the specimen folder in order. This will help when organizing images and may be an additional source of determination information.

• Photograph the whole specimen sheet. Try to include all annotations the specimen has (i.e., stamps, codes, fruits, flowers). Flowers and fruits are necessary to correlate with the collection date to identify the taxon’s collection window.

• Photograph the herbarium label, determination label and any additional annotation in close-up.

• When duplicates of two or more specimens are encountered that share the same collecting number, place the specimens side by side and photograph them together. (This will allow the digitizer to recognize morphological details—flower or fruits—from the dried plant.)

• Once the images for a particular specimen have been taken, review the image; erase any blurry photographs and repeat the photograph if necessary. Things to avoid that might hamper image quality:

• Avoid taking blurry photos; make sure the photo is correctly focused.

• Avoid taking horizontally skewed photos as this might affect the collation of the label information.

• Avoid using the digital zoom; specimens should be directly comparable.

• Avoid casting a shadow across the specimen with your body while taking the photograph.

• If possible use the “macro” option for taking the photograph as this will help maximize the capture of specimen details.

• Avoid the use of flash as it will accentuate the shadows on the specimen; instead, try to take the photos in a well-lit spot in the herbarium.

• Make sure you keep one, preferably two, back-ups of all photographs taken.

Organizing the images:

• To provide security, it is best to periodically upload the collated images to an FTP site—as additional back-up security but also to provide access for the staff digitizing and georeferencing the specimens.

• Before uploading images to the FTP site, rotate them as necessary so they appear in portrait form, then organize the images into a logical structure, such as:

Figure 14.3: Recommended folder structure for storing herbarium images

 

Annex B. Extended List of Ecogeographic Data Descriptors

The ecogeographic data collection template is based on the template produced by the Millennium Seed Bank Enhancement Project Species Targeting Team (2004–2008), where it was used to capture all information from herbarium labels and subsequently georeference the locality data. The general template from the Botanical Records and Herbarium Management (BRAHMS) rapid data entry system was tailored to follow Royal Botanical Gardens, Kew’s core field standards, and the specific requirements of the project. The format allows the data to be directly used for analysis and mapping. The data collected formed the basis of seed collecting guides produced by Kew and distributed to collecting partners.

In the Adapting Agriculture to Climate Change project (see above), data will be utilized for gap analyses, as well as the production of collection guides. To reflect this extended use, additional fields have been added to the original template. These facilitate the collection of data from a wide range of sources (including public and private digital datasets, herbarium vouchers and genebank datasets) and the management of restrictions on data usage. The modifications were developed collaboratively by the Royal
Botanical Gardens, Kew; the International Centre for Tropical Agriculture (CIAT); and the University of Birmingham. As such, the data types are extensive but it is not necessary to have a complete set to undertake an ecogeographic analysis. However, the more complete the set, the more sophisticated the analysis and the more detailed the prediction and, ultimately, the conservation.

Click here to open a table of the Ecogeographic Data Descriptors.

 

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