How to include the impact of climate change in the extinction risk assessment of policy plant species?
Introduction
The European Union has one of the most advanced and effective intergovernmental biodiversity policies (Beresford, Buchanan, Sanderson, Jefferson, & Donald, 2016). The “Habitat” Directive 92/43/CEE (hereafter HD) represents the core strategy of nature conservation in Europe, aiming at protecting, maintaining or restoring a “favourable” conservation status for policy species (taxa of flora and fauna included in the Habitat Directive 92/43/EEC and the Bern Convention annexes). However, previous reports at national and European levels demonstrated that several policy species meet an “unfavourable” conservation status (Condé, Jones-Walters, Torre-Marin, & Romao, 2010; Fenu et al., 2017). This is supported by different red lists at national (Moreno Saiz, Domìnguez Lozano, & Sainz Ollero, 2003; Rossi et al., 2016) and EU levels (Bilz, Kell, Maxted, & Lansdown, 2011; García Criado et al., 2017). Among threats affecting the policy species, climate change currently causes minor effect (Bilz et al., 2011; Fenu et al., 2017; Rossi et al., 2016; Thuiller, 2007). Nonetheless, global warming is increasing its negative impacts, with new temperature records set every year (e.g., CNR’s annual climatic reports for Italy http://www.isac.cnr.it/climstor/climate/; NOAA’s Global Climate Report https://www.ncdc.noaa.gov/sotc/global/; Feng et al., 2014; Pauli et al., 2012).
The ways climate change affects plants are various and strongly interact with other factors such as species traits, human disturbance, including habitat fragmentation, magnitude of extreme events, etc. (e.g. Honnay et al., 2002; Niu et al., 2014; Orsenigo, Mondoni, Rossi, & Abeli, 2014). Thus, understanding how climate change will affect policy species is of primary importance to define current conservation policy and effective actions plans, to avoid species extinction, to identify future mismatch between protected areas and species distribution (Araùjo, Alagador, Cabeza, Nogues-Bravo, & Thuiller, 2011; Fois, Bacchetta, Cogoni, & Fenu, 2018), and to mitigate the impact of fragmentation on species range shift.
The effects of climate change on future plant distributions is typically assessed by applying species distribution models (SDMs hereafter) to projected climatic conditions (e.g., Attorre et al., 2011; Benito Garzón, Sánchez de Dios, & Sáinz-Ollero, 2008; Ferrarini, Rossi, Mondoni, & Orsenigo, 2014; Fois et al., 2018). Based on SDMs, several approaches for predicting climate change impacts on species extinction risk, according to the IUCN categories and criteria have been applied (see Draper Munt, Muñoz-Rodríguez, Marques, & Moreno Saiz, 2016; Fois et al., 2018; Thuiller, Lavorel, Araújo, Sykes, & Prentice, 2005). This approach has been questioned by Akçakaya, Butchart, Mace, Stuart, and Hilton-Taylor (2006) because of the misuse of IUCN criteria. Despite this, some authors recently used coupled niche-demographic models to assess the impact of climate change on the extinction risk of a number of reptile and amphibian species according to IUCN categories and criteria (Keith et al., 2014; Stanton, Shoemaker, Pearson, & Akçakaya, 2015). However, applicability of SDMs to red lists is difficult due to model uncertainties, as many biotic and abiotic factors cannot (or are difficult to) be included in these models such as, for instance, soil features, competition and mutualism (i.e. Fordham et al., 2012), as well as genetic adaptation of target species. For this reason, we suggest that the two methods can complement each other: red list categories provide information on both the current and future extinction risk for a target species, while projected SDMs may provide warnings on the magnitude of future extinction risk. For instance, under climate change, species belonging to the same red list category may show different future projected range loss or gain, implying a different urgency in the application of conservation measures. This should lead to more accurate conservation prioritization of group of species.
In our paper, we used such an approach on Italian policy plant species for which a comprehensive assessment of the conservation status has been recently conducted based on IUCN categories and criteria (Rossi et al., 2016). Our approach was based on the application of a cellular automaton model to produce a stochastic distribution of potential dispersal outcomes of range shift of such species in Italy. This model can produce quite realistic dispersal patterns in predicting future plant species distributions, by incorporating movement into SDMs using species-specific demographic information and dispersal limitation as well as habitat heterogeneity (see also Miller, Holloway, & Gillins, 2015 for theoretical background, and Benito, Lorite, Pérez-Pérez, Gómez-Aparicio, & Peñas, 2014; Di Traglia, Attorre, Francesconi, Valenti, & Vitale, 2011; Engler et al., 2009; Summers, Bryan, Crossman, & Mayer, 2012 for practical examples).
Section snippets
Data set
A list of 107 policy vascular plants occurring in Italy was considered. The taxonomic treatment of species and subspecies follows Bartolucci et al. (2018). Taxa occurrence data were obtained from the Italian Red List geodatabase (Rossi et al., 2016), and from a network of databases with georeferenced occurrences (Agrillo et al., 2017; Bedini et al., 2016; Martellos et al., 2011). Taxa with fewer than 30 occurrences were excluded due to the high potential inaccuracy of the model. This
Results
The majority of selected taxa is characteristic of alpine and sub-alpine environments, and generally shows a boleochorous dispersal syndrome (Table 1). RF showed a very high discrimination ability with an out-of-bag cross-validated AUC median for the 37 species of 0.9988 (max 1, min 0.971). The average probability of extinction and critical range decline (future projected range reaching 10% of the original range) is reported in Table 2. No species was predicted to become extinct in the
Discussion
Despite the increasing evidence of the impact of climate change on the survival of many species, such a threat is quantitatively considered in the red list assessment for only a small number of these species (Akçakaya, Butchart, Watson, & Pearson, 2014).
A possible explanation may be provided by the uncertainties in predicting the extinction risk using SDMs and climatic scenarios. By coupling population and distribution models, Stanton et al. (2015) used RL criteria to identify species at risk
Acknowledgements
The authors are grateful to the Italian Ministry for the Environment and Protection of Land and Sea, General Directorate Protection Nature and Sea, for its financial support of the Plant Red List Assessment Program, and to the Secretariat of the Italian Botanical Society for its support during the process. We gratefully acknowledge all the Italian botanists who provided field and unpublished data for their priceless contribution.
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