How to include the impact of climate change in the extinction risk assessment of policy plant species?

https://doi.org/10.1016/j.jnc.2018.06.004Get rights and content

Abstract

Climate change can have significant impacts on the survival of plant species. However, it is seldom included in the assessment of the extinction risk according to IUCN Red List criteria. Lack of data and uncertainties of predictions make difficult such inclusion. In our paper we present an approach, in which the effect of climate change on plant species spatial distribution is used to prioritize conservation within IUCN categories. We used, as a case study, 37 Italian policy species, relevant for conservation, and listed in the Habitat Directive and Bern Convention, and for which a Red List (RL) assessment was available. A stochastic SDM incorporating data on plant dispersal, generation length, and habitat fragmentation was used to predict a range shift due to climate change according to two climatic scenarios (RCP 2.6 and 8.5). No species was predicted to become extinct in the considered timespans (2050 and 2070) due to climate change, and only two were characterized by critical decline probabilities. However, all taxa were potentially affected by climate change through a reduction of their range. In all RL categories, species with the highest predicted reduction of range were those from lowlands, where fragmentation of natural habitats has occurred in the last decades. In these cases, despite some limitations, assisted migration can be considered a suitable conservation option.

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.

References (74)

  • E. Agrillo et al.

    Nationwide georeferenced vegetation databases – Sapienza University of Rome: State of the art, basic statistics and future perspective

    Phytocoenologia

    (2017)
  • H.R. Akçakaya et al.

    Use and misuse of the IUCN Red List Criteria in projecting climate change impacts on biodiversity

    Global Change Biology

    (2006)
  • H.R. Akçakaya et al.

    Preventing species extinctions resulting from climate change

    Nature Climate Change

    (2014)
  • M.B. Araùjo et al.

    Climate change threatens European conservation areas

    Ecology Letters

    (2011)
  • F. Attorre et al.

    Evaluating the effects of climate change on tree species abundance and distribution in the Italian peninsula

    Applied Vegetation Science

    (2011)
  • F. Attorre et al.

    Classifying and mapping potential distribution of forest types using a finite mixture model

    Folia Geobotanica

    (2014)
  • M. Barbet-Massin et al.

    Selecting pseudo-absences for species distribution models: How, where and how many?

    Methods in Ecology and Evolution

    (2012)
  • F. Bartolucci et al.

    An updated checklist of the vascular flora native to Italy

    Plant Biosystems

    (2018)
  • G. Bedini et al.

    Wikiplantbase #Toscana, breaking the dormancy of floristic data

    Plant Biosystems

    (2016)
  • B.M. Benito et al.

    Forecasting plant range collapse in a Mediterranean hotspot: When dispersal uncertainties matter

    Diversity and Distributions

    (2014)
  • M. Benito Garzón et al.

    Effects of climate change on the distributions of Iberian forests

    Applied Vegetation Science

    (2008)
  • A.E. Beresford et al.

    The contributions of the EU nature directives to the CBD and other multilateral environmental agreements

    Conservation Letters

    (2016)
  • M. Bilz et al.

    European red list of vascular plants

    (2011)
  • S.L. Borana et al.

    Prediction of land cover changes of Jodhpur city using cellular automata Markov modelling techniques

    International Journal of Engineering Science and Computing

    (2017)
  • L. Breiman

    Random forests

    Machine Learning

    (2001)
  • A. Carta et al.

    The avoidance of self–interference in the Tuscan endemic spring geophyte Crocus etruscus Parl. (Iridaceae)

    Plant Biosystems

    (2016)
  • A. Carta et al.

    Seed dormancy and germination in three Crocus ser. Verni species (Iridaceae): Implications for evolution of dormancy within the genus

    Plant Biology

    (2014)
  • A. Carta et al.

    Flower bouquet variation in four species of Crocus ser. Verni (Iridaceae)

    Journal of Chemical Ecology

    (2015)
  • A. Carta et al.

    Seed morphology and genome size in two Tuscan Crocus (Iridaceae) endemics: C. etruscus and C. ilvensis

    Caryologia

    (2015)
  • S. Condé et al.

    EU biodiversity baseline. EEA technical report 12/2010

    (2010)
  • O. Cotto et al.

    A dynamic eco-evolutionary model predicts slow response of alpine plants to climate warming

    Nature Communications

    (2017)
  • D. Draper Munt et al.

    Effects of climate change on threatened Spanish medicinal and aromatic species: predicting future trends and defining conservation guidelines

    Israel Journal of Plant Sciences

    (2016)
  • R. Engler et al.

    Predicting future distributions of mountain plants under climate change: Does dispersal capacity matter?

    Ecography

    (2009)
  • A. Falcucci et al.

    Changes in land-use/land-cover patterns in Italy and their implications for biodiversity conservation

    Landscape Ecology

    (2007)
  • G. Fenu et al.

    Conserving plant diversity in Europe: Outcomes, criticisms and perspectives of the Habitats Directive application in Italy

    Biodiversity and Conservation

    (2017)
  • A. Ferrarini et al.

    Planning for assisted colonization of plants in a warming world

    Scientific Reports

    (2016)
  • M. Fois et al.

    Current and future effectiveness of the Natura 2000 network for protecting plant species in Sardinia: A nice and complex strategy in its raw state?

    Journal of Environmental Planning and Management

    (2018)
  • Cited by (25)

    • Prioritizing conservation of biodiversity in an alpine region: Distribution pattern and conservation status of seed plants in the Qinghai-Tibetan Plateau

      2021, Global Ecology and Conservation
      Citation Excerpt :

      When plant migration cannot keep pace with climate change, population dynamics and species distribution might be profoundly impacted (Ackerly et al., 2010; Barber et al., 2016). As the temperature and precipitation continue to rise in the QTP, woody plants are likely to migrate into the interior of the plateau and gradually replace herbs, thereby posing a significant challenge to the effectiveness of current protected areas (Lovejoy, 2006; Gao et al., 2016; Attorre et al., 2018). Species distribution models that combine species occurrence records with environmental variables have been widely used for the prediction of potential species distribution range shifts in many regions (Remya et al., 2015; Li et al., 2021).

    • Phylogenetically informed spatial planning as a tool to prioritise areas for threatened plant conservation within a Mediterranean biodiversity hotspot

      2019, Science of the Total Environment
      Citation Excerpt :

      Indeed, this country includes a large peninsula and adjacent islands and islets placed in the heart of the Mediterranean Basin, and most of the southern European Alps, altogether sheltering 8195 native vascular plants (Bartolucci et al., 2018), 1340 of which (16.3%) are strictly endemic to the country (Bartolucci et al., 2018), including four endemic genera: Eokochia (Amaranthaceae), Rhizobotrya (Brassicaceae), Petagnaea, and Siculosciadium (Apiaceae). The conservation status of 2321 (28.3%) vascular plants was recently assessed (MATTM, 2019), including all Italian endemics (Orsenigo et al., 2018), species protected in Europe (Rossi et al., 2016; Attorre et al., 2018) and species typical of the most threatened habitats, such as wetlands and coastal areas (Rossi et al., 2014; Fenu et al., 2017). Among them, 995 taxa (including 550 Italian endemics) were assigned to an IUCN threat category or were Near Threatened (NT), while the remaining qualified as Least Concern (LC, 43%) or Data Deficient (DD, 17%).

    • Using MaxEnt modeling to predict the potential distribution of the endemic plant Rosa arabica Crép. in Egypt

      2019, Ecological Informatics
      Citation Excerpt :

      Many endemic taxa are included in the IUCN Red List of the threatened species as they are in danger of global extinction because of their narrow geographic distribution and extremely habitat-restricted (Crisp et al., 2001; Orsenigo et al., 2018). Hence, protecting and conserving such species is important, through addressing the potential distribution of suitable habitats and finding the environmental factors which drive the presence and persistence under current and future conditions (Attorre et al., 2018; Brooks et al., 2002; Primack, 2006). The first step to initiate conservation processes for these taxa is to identify the current geographic distribution, population status and threats that expose them to the risk of extinction (Crisp et al., 2001).

    • Opportunity cost of a private reserve of natural heritage, Cerrado biome – Brazil

      2019, Land Use Policy
      Citation Excerpt :

      Also, from this reduction, global warming may increase (Zhao and Running, 2009). In this context, the climatic changes related to this warming, can result in negative impacts on the survival of plant species (Niu et al., 2014; Orsenigo et al., 2014; Attorre et al., 2018). Regarding the costs of soybean and corn production, the information available in Fig. 3 was considered.

    View all citing articles on Scopus
    View full text