Flowchart

Ex situ gap analysis can be carried out at different levels:

Individual CWR level: whether the target CWR taxa are adequately represented by existing ex situ accessions.
Ecogeographic level: whether the whole ecogeographic range of the CWR is represented ex situ. Ecogeographic diversity can be used as an indicator of genetic diversity, the assumption being that the conservation of maximum ecogeographic diversity will result in the conservation of maximum genetic diversity. Characterizing populations according to the environmental conditions in which they grow can also help to identify useful abiotic traits such as extreme temperatures, drought etc.
Genetic level: whether specific CWR populations that contain genetic diversity of interest (e.g. high genetic diversity) are conserved ex situ.
Trait level: whether specific CWR populations that contain a particular trait of interest (e.g. resistance to drought etc.) are adequately conserved ex situ.

Ex situ gap analysis includes the following three main steps, which are very similar to those carried out in an in situ gap analysis:

Select the occurrence data to be used in the analysis.
Identify the CWR that are not conserved ex situ (individual CWR taxon level).
Identify gaps at infra-species level, i.e. ecogeographic diversity, genetic and trait levels.

TOOLKIT MENU WRITTEN TEXT

st=>start: START
select_occurr_data=>operation: SELECT OCCURRENCE DATA
TO USE IN GAP ANALYSIS|process
id_CWR_not_exsitu=>operation: IDENTIFY CWR NOT
CONSERVED EX SITU|process
id_infra_spec_gaps=>operation: IDENTIFY GAPS AT
INFRA-SPECIFIC LEVEL
(ecogeographic / genetic /
trait levels)|process
ecogeo_etc_conserved_exsitu=>condition: Ecogeographic/
genetic/trait diversity
conserved ex situ?|decision
exsitu_gaps_identified_ecogeo_etc=>end: EX SITU GAPS IDENTIFIED
(under-represented
ecogeographic/genetic/
trait diversity)|finish
no_further_exsitu_cons=>end: NO FURTHER EX SITU
CONSERVATION|finish
exsitu_gap_CWR=>end: EX SITU GAP IDENTIFIED
(under-represented CWR)|finish
compare_CWR_occurr_with_exsitu=>operation: Compare CWR occurrences
with those that are conserved
ex situ|process
CWR_adeq_conserved_exsitu=>condition: CWR taxa adequately
conserved ex situ?|decision
st->select_occurr_data->id_CWR_not_exsitu
id_CWR_not_exsitu->compare_CWR_occurr_with_exsitu(right)
id_infra_spec_gaps->ecogeo_etc_conserved_exsitu
compare_CWR_occurr_with_exsitu->CWR_adeq_conserved_exsitu(right)
ecogeo_etc_conserved_exsitu(yes, right)->no_further_exsitu_cons
ecogeo_etc_conserved_exsitu(no)->exsitu_gaps_identified_ecogeo_etc
CWR_adeq_conserved_exsitu(yes)->id_infra_spec_gaps
CWR_adeq_conserved_exsitu(no, right)->exsitu_gap_CWR

Species distribution models

Species distribution models

Species distribution models

Species distribution models (SDM) are a useful tool to predict potential areas of distribution of priority CWR. They have been commonly used to answer questions related to ecology, evolution and conservation (Elith et al. 2006). SDM have been employed to aid conservation decisions (e.g. Dockerty et al. 2003, Midgley et al. 2003), to direct field surveys towards locations where taxa are likely to be found (e.g. Engler et al. 2004), to establish baseline information for predicting a species’ response to landscape alterations and/or climate change (e.g. Huntley et al. 1995, Beaumont and Hughes 2002, Thuiller 2003, Thomas et al. 2004, Hijmans and Graham 2006) and to identify high‐priority sites for ex situ and in situ conservation (e.g. Araújo and Williams 2000, Loiselle et al. 2003, Castañeda-Álvarez et al. 2016).

There is a wide range of methods for modelling species distribution. These include classification and regression trees (CART) (e.g. Breiman et al. 1984), generalized linear models (GLM) (McCullagh and Nelder 1989), generalized additive models (GAM) (Hastie and Tibshirani 1990), climatic envelope models (CEM) (e.g. BIOCLIM) (Busby 1991), Gower‐similarity models (e.g. DOMAIN) (e.g. Carpenter et al. 1993), artificial neural networks (ANN) (e.g. Mastrorillo et al. 1997), ecological niche factor analysis (ENFA) (e.g. Hirzel et al. 2001, freely available from here), generalized dissimilarity models (GDM) (e.g. Ferrier 2002) and maximum entropy models (e.g. MaxEnt by Phillips et al. 2006, freely available from here). These models vary in how they model distribution responses, select relevant climatic parameters, define fitted functions for each parameter, weight different parameter contributions, allow for interactions and predict geographic patterns of occurrence (Guisan and Zimmerman 2000, Burgman et al. 2005). See Brotons et al. (2004), Segurado and Araújo (2004) and Elith et al. (2006) for detailed reviews and comparison of existing modelling methods, and Thuiller et al. (2005) for discussion on the ecological principles and assumptions of each model, as well as their limitations and decisions inherent to the evaluation of these models.

The Interactive Toolkit for Crop Wild Relative Conservation Planning was developed within the framework of the SADC CWR project www.cropwildrelatives.org/sadc-cwr-project (2014-2016),
which was co-funded by the European Union and implemented through ACP-EU Co-operation Programme in Science and Technology (S&T II) by the African, Caribbean and Pacific (ACP) Group of States.
Grant agreement no FED/2013/330-210.

Flowchart

Species distribution models