# Predicted population declines driven by climate change can be mitigated with a new protected area in endemic tetrapods from Madagascar

Nicolas Dubos, Pauline Jitten, Frederique Montfort, Clovis Grinand, Eddie

Fanantenana Rakotondrasoa, Marie Nourtier

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► To cite this version:

Nicolas Dubos, Pauline Jitten, Frederique Montfort, Clovis Grinand, Eddie Fanantenana Rakotondrasoa, et al.. Predicted population declines driven by climate change can be mitigated with a new protected area in endemic tetrapods from Madagascar. Journal for Nature Conservation, 2025, 86, pp.126925. 10.1016/j.jnc.2025.126925. hal-05208081

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# Predicted population declines driven by climate change can be mitigated with a new protected area in endemic tetrapods from Madagascar

Nicolas Dubos<sup>a,b,*</sup>, Pauline Jitten<sup>b,c</sup>, Frederique Montfort<sup>c</sup>, Clovis Grinand<sup>c</sup>, Eddie Fanantenana Rakotondrasoa<sup>d</sup>, Marie Nourtier<sup>c</sup>

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## ARTICLE INFO

**Keywords:**

Climate change

Deforestation

Ecological Niche Modelling

Extinction risk

IUCN status

Protected areas

## ABSTRACT

Protected areas represent a major tool for biodiversity conservation in the face of environmental change. However, species will need to shift their distribution to track their suitable climate, which calls the need to create new protected areas and take into account those changes. We modelled the suitable environment of 11 tetrapods (three reptiles, three amphibians, and five mammals) that dwell in the eastern forest corridor of Madagascar with an Ecological niche modelling approach adapted to small sample size and sample bias. We took into account the habitat (forest cover), two climate data sources (Worldclim and CHELSA), two scenarios of projected future climate change and three global circulation models, and quantified the change in suitable environments at the presence points and at the location of a candidate protected area in SE Madagascar (Beampingaratsy forest). We predict a decline in climate suitability for all species at their current locations. We propose to elevate the IUCN conservation status of five species to a threatened category, relying on criterion A3 (projected population decline). We also found that suitability will increase at the location of the candidate protected area for eight species. Granting a strong protection status in this region will ensure an ecological continuity between two National Parks, and contribute to help species track their suitable environments in the future.

## 1. Introduction

The current global networks of protected areas are projected to be insufficient in the future due to climate and land cover changes (Bosso et al., 2018; Dubos et al., 2022; Hoveka et al., 2020; Leroy et al., 2014). That is because the climate may no longer be suitable at the current locations of protected areas, leading to local extinctions and potential distribution shifts. Because of habitat fragmentation induced by land use change, species might not be able to track their suitable conditions, which will eventually result in a decrease in local diversity. There is a need to identify future priority areas for conservation and ensure habitat connectivity to maintain a sufficient level of protection in the face of climate and land cover changes (Guisan et al., 2013). Priority areas should include either regions that are predicted to be suitable to local species in the future as well as ecological corridors which will enable species to shift towards those suitable climates.

Suitable areas can be identified with projections of species distributions using ecological niche modelling (ENM or species distribution models, SDMs). ENMs quantify the relationship between the presence of a species and environmental predictors. They can be used to identify the most suitable conditions for a given species, and forecast potential future geographical shifts in these conditions. These models can be trained with presence-only data (the absence data is generated), and produce maps of environmental suitability or relative likelihood of presence (Guillera-Arroita et al., 2015). These maps rely on the assumption that species are at equilibrium (i.e. their distribution is limited by biotic interactions), and model projections represent their realized niche. Presence data are subject to sample bias and Wallacean shortfalls, which can be accounted for with statistical corrections (Dubos et al., 2022; Phillips et al., 2009). They are commonly used for testing ecological hypotheses (Anderson et al., 2009; Dubos et al., 2022), assessing alien species invasion risks (Bellard et al., 2013; Dubos et al., 2023; Lanner et al., 2022),

<footnote><sup>a</sup> Mitrani Department of Desert Ecology, Swiss Institute for Dryland Environmental and Energy Research, The Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, 849900 Midreshet Ben-Gurion, Israel</footnote>

<footnote><sup>b</sup> INRAE (UMR TETIS), Maison de la Télédétection, 500 rue Jean-François Breton, 34093 Montpellier Cedex 5, France</footnote>

<footnote><sup>c</sup> Nitidæ, N'Lab, Maison de la Télédétection, 500 rue Jean-François Breton, 34093 Montpellier Cedex 5, France</footnote>

<footnote><sup>d</sup> Nitidæ, Amparihy, Fort Dauphin, Madagascar</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: dubos.research@gmail.com (N. Dubos).</footnote>
and supporting conservation and management efforts (Dubos et al., 2023; Schwartz, 2012). In conservation application, SDMs can provide maps of projected future environmental suitability at the resolution of environmental data. These maps can be used as a tool for extinction risk assessment (Moat et al., 2019), or to assess protection gaps and the potential efficiency of candidate protected areas. Protection gaps will likely worsen in the future because of climate change (Dubos et al., 2022; Leroy et al., 2014), which calls the need to account for future conditions in the design of protected areas. This might be the case in Madagascar, where significant changes in both temperature and precipitation have already been observed (Dubos et al., 2020).

Madagascar is a biodiversity hotspot with 50 to 100 % endemism in vertebrates (depending on the class) that are subject to a rising number of threats, including habitat loss and climate change (Andreone et al., 2021; Goodman, 2023; Grinand & Nourtier, 2022). The island has experienced severe episodes of deforestation induced mainly by unregulated agriculture, with 44 % of loss in natural forest cover between 1953 and 2014 (Vieilledent et al., 2018; Vieilledent et al., 2020) and

only 6% of the forest surface has high landscape-level integrity (Grantham et al., 2020). The majority of Malagasy species (about 80 %) is hosted in its Eastern forest corridor (Armstrong & Goodman, 2022), which makes them all the more exposed to habitat loss and fragmentation as a result of a continued anthropogenic pressure. On top of this, climate change is expected to bring an additional strong pressure on Malagasy species (Brown & Yoder, 2015; Dubos et al., 2022). In response to climate change, species will be forced to shift their distribution either poleward or upward to track their suitable conditions (mostly to evade warming conditions, as already observed in Madagascar amphibians and reptiles; Raxworthy et al., 2008). Therefore, it is of utmost importance to maintain habitat connectivity to enable species to track their suitable conditions. A network of terrestrial protected areas has been set, covering 8.4 % of the surface of the island in 2017 (Gardner et al., 2018), up to 10.8 % in 2023 (https://www.fapbm.org). Although protected area do manage to mitigate deforestation (Eklund et al., 2016), new local extinctions are consistently recorded regardless of the protection status (Piludu et al., 2015). This calls the

<figure><img src="image_5.png" /><figcaption>**Fig. 1.** Study area, with a highlight on the candidate protected area of Beampingaratsy (PA).</figcaption></figure>
need to intensify the protection effort and assess the potential efficiency of candidate areas.

In south-east (SE) Madagascar (Anosy region), a new protected area has been proposed and is yet to be implemented in the Beampingaratsy forest (Rakotomalala et al., 2021). This region is located between the National parks of Midongy du Sud and Andohahela, and could act as an ecological corridor (Fig. 1). In response to climate change, we expect species to shift southwards. However, the region is being threatened by deforestation through slash-and-burn practices. The implementation of a protection status in this region would ensure an ecological continuity that will enable species to track their conditions and connect both National Parks.

We assessed the potential conservation importance of a candidate protected area in Madagascar with a predictive modelling approach. We quantified the climate niche of 11 Malagasy tetrapods (three reptiles, three amphibians, and five mammals) that are observed in SE Madagascar and predict the effect of climate change on their potential distribution, accounting for habitat. We identified which species are the most at risk and provide elements to potentially reconsider their conservation status based on IUCN criterion A3 (projected population decline; Mancini et al., 2024). We further examine the level of suitable environments in the future at the location of the candidate area to assess its potential efficiency in mitigating the effects of climate change. Specifically, we test whether species that are located north the region will need to shift southwards and find suitable environment inside, or south the candidate area by 2070.

## 2. Methods

### 2.1. Study site

Beampingaratsy forest is located along the Malagasy mountainous corridor in the eastern part of the country, in the Subtropical Moist Forests biome (Olson et al., 2001). The topography in the region is characterized by the mountainous chain of the East of Madagascar and valleys communicating between low elevation lands until the coast and the vast plains of the western part of the island. Community settlements are mainly located in the valleys. Elevation in the study area varies between 200 m and 1500 m. The humid forest structure and composition vary along the altitudinal gradient. Two types of forests of the eastern corridor can be found in Beampingaratsy forest: (i) East forests and Sambirano (0–800 m) and (ii) Mid-elevation eastern forests (800–1800 m). Forests height and density decrease with altitude, with variable altitude thresholds at the level of the Beampingaratsy forest (Asner et al., 2012). The climate in Madagascar is largely influenced by the long south-north ridge that divides the humid mountainous and littoral region in the east and the dry plateau in the west, with a narrow transition zone in-between. The climate of the Beampingaratsy forest is tropical monsoon according to Köppen-Geiger climate classification (Peel et al., 2007). In Fort Dauphin (main city near Beampingaratsy forest, on the coast), the mean annual rainfall is about 1600 mm (http://www.taolagna.climatemps.com/).

An extensive fauna inventory was conducted in Beampingaratsy forest during the year 2020 in multiple elevation classes (Rakotomalala et al., 2021). A total of 185 species were identified (23 % considered as threatened), including 44 amphibians, 32 reptiles, 78 birds, 16 non-flying mammals, six lemurs, four bats, four carnivores and one species of Artiodactyla. This inventory highlighted the presence of rare species for which the distribution is still poorly known.

### 2.2. Study species and occurrence data

We selected our study species according to four criteria. (1) We chose forest-dwelling species which (2) are present north and/or down the candidate protected area, because their suitable climate is likely to shift southward and/or upward (i.e. shift towards higher elevations;

Raxworthy et al., 2008), which enabled us to test our hypothesis (i.e. the protected area will benefit Malagasy species in the face of climate change). (3) We chose species of high conservation stakes, i.e. species assessed as threatened or nearly threatened by the IUCN. For statistical purposes, (4) we kept species for which at least 10 locality records are available nation-wide in the scientific literature and the complementing surveys. For reptiles and amphibians, we used occurrences data from (Brown et al., 2016). We downloaded lemur data from the national database REBIOMA (Madagascar Biodiversity Network), a database with expertly vetted occurrence data (www.data.rebioma.net). We complemented our samples with original data obtained from the aforementioned surveys carried out in the Beampingaratsy forest in 2020 (Rakotomalala et al., 2021).

We thinned our occurrence data by sampling one occurrence point per grid cell of the climate data to avoid pseudo-replication (Steen et al., 2021). The final set of species (and sample size) included three reptiles: *Brookesia superciliaris* (Kuhl 1820, Chameleonidae; n = 37), *Calumma parsonii* (Cuvier 1825, Chameleonidae; n = 21) and *Sanzinia madagascariensis* (Dumeril & Bibron 1844, Boidae; n = 41); three amphibians: *Gephyromantis leucocephalus* (Angel 1930, Mantellidae; n = 30), *Mantella haraldmeieri* (Busse 1981, Mantellidae; n = 6) and *Platypelis tuberifera* (Methuen 1920, Microhylidae; n = 31); five mammals: *Cheirogaleus major* (Geoffroy 1812, Cheirogaleidae; n = 132), *Daubentonia madagascariensis* (Gmelin 1788, Daubentoniidae; n = 80), *Eulemur collaris* (Geoffroy 1812; Lemuridae; n = 80), *Hapalemur aureus* (Meier 1987, Lemuridae; n = 128), and *Microcebus tanosi* (Rasoloarison et al. 2013, Cheirogaleidae; n = 7).

### 2.3. Ecological niche modelling

**Overview** – we used an ecological niche modelling approach with a technique adapted to small samples (ensemble of small models), using a highly performing algorithm (RandomForest down sampled), correcting for sample bias (combination of target group and accessibility approaches), with a spatial partitioning for model evaluation (to better assess transferability compared to random partitioning), with a robust predictor selection process, and taking into account a wide panel of climate data (two sources, two scenarios and three GCMs) and habitat type.

**Climate data and predictor selection** – We used 19 bioclimatic variables (description available at https://www.worldclim.org/data/bioclim.html; see also (Booth et al., 2014) at 30 arc seconds (approximately 1 km) resolution for the current and future (2070) climate from two sources: CHELSA version 1.2 (Karger et al., 2017; https://chelsa-climate.org) and Worldclim global climate data version 2.1 (Fick & Hijmans, 2017; version 1.4 for future projections because version 2.1 was not available at the time of model computing). These data sources used different methods and periods to compute the climatologies and result in different maps. Worldclim is based on weather station interpolated data with elevation and distance to the coast as predictors in addition to satellite data (Fick & Hijmans 2017). CHELSA is based on statistical downscaling for temperature, and precipitation estimations incorporate orographic factors (i.e., wind fields, valley exposition, boundary layer height; Karger et al. 2017). We used an automated predictor selection process in four steps: (1) removing irrelevant predictors (i.e. predictor for which the causal relationship with the species presence is not clear), (2) removing collinear predictors, (3) keeping predictors with the clearest causal relationship, and (4) statistical selection based on relative importance. (1) We discarded bio3 (isothermality) because of a lack of variability within our study area and because the relationship with species presence was unclear. We kept the remaining 18 because they all may explain species distribution, mostly through direct effects on temperature and water balance, or indirect effects on ecosystem productivity and food availability (Dubos et al., 2018). (2) For each climate data source and for each species-specific background (details below), we selected one predictor variable per group of inter-correlated variables to
avoid collinearity (Pearson's r > 0.7; (Dormann et al., 2013) using the removeCollinearity function of the virtualspecies R package (Leroy et al., 2016). (3) When mean values were collinear with extremes, we selected the variables representing extreme conditions (e.g., warmest/driest condition of a given period) because these are more likely to drive mortality and local extirpation and be causally related to the presence of the species (Maxwell et al., 2019; Mazzotti et al., 2016). (4) We selected four predictors with the highest relative importance (assessed by the GINI index of the RandomForest R package), individually for each species. Relative importance was assessed with centred and scaled predictors. For future projections, we used three Global Circulation Models (GCMs; i.e., BCC-CSM1-1, MIROC5, and HadGEM2-AO) and two greenhouse gas emission scenarios (one optimistic SSP2 and the most pessimistic SSP5) to consider a wide panel of possible environmental change by 2070.

**Forest data and pre-filtering** – Species distribution is explained by both climate and habitat characteristics. Our study species are strictly forest dwellers. We used data on forest cover of 2015, 30 m resolution, from (Vieilledent et al., 2018). In the case where a species is absent despite a suitable climate and the absence is likely explained by unsuitable habitat, the climate niche may be misidentified. We used a mask of forest cover on climate data before model training to optimize computing time and data storage while preventing the downplay of suitable climates in non-forested areas. This approach also allows to account for habitat type without harming statistical parsimony while being biologically more realistic.

**Background selection** – It is recommended to use a restricted background to mitigate the effect of sample bias, follow the hypotheses of past dispersal and account for the Equilibrium hypothesis (Acevedo et al., 2012; Dubos et al., 2022; Vollering et al., 2019). For each species individually, we defined the background as a rectangle of minimal surface that includes all presence points, which we extend by 10 % in each cardinal direction.

**Modelling technique** – We modelled and projected climatic niches using a single highly performing tuned algorithm (Valavi et al., 2021; Valavi et al., 2023), Random Forest down-sampled (hereafter RF down-sampled, i.e. RF parametrised to deal with a large number of background samples and few presence records; (Prasad et al., 2006). We set RF down-sampled to run for 1000 bootstrap samples/trees. We used an Ensemble of Small Models approach (Breiner et al., 2015), which performs well with single-algorithms (Breiner et al., 2018). This approach is adapted to small samples, enabling to obtain a high explanatory power with a high statistical parsimony. It consists in running combinations of bivariate models (all combinations of two predictors among the four selected; n = 6). After discarding poorly performing models (see Model evaluation section), projections are averaged across all replicates.

**Pseudo-absence selection and sample bias correction** – We generated pseudo-absences with the randomPoints function of the dismo package. The number of pseudo-absences depended on the extent of the background (n = 1000 points if the background is larger than 2000 pixels, otherwise n = 250 points). We generated five sets of pseudo-absence for each model. We applied a sample bias correction technique consisting in a selection of pseudo-absence points that imitates the spatial bias of presence points (Phillips et al., 2009). To do so, we used a combination of target group and accessibility approaches. We used a map of all locality records of the corresponding taxa (at the level of the Class). We used a kernel function to obtain smoothed gradients around those locality records, and weighted the resulting map with an index of travel time since the closest city (to account for field accessibility; Weiss et al., 2018). We used the resulting map as a probability weight for pseudo-absence selection.

**Model evaluation** – We used block-cross validation approach which better assesses model transferability compared to random partitioning (Valavi et al., 2019, 2023). We spatially partitioned our data into four blocks. The size of the blocks depended on the extent of the species distribution, ranging between 0.6 and 100 km. Model performance was

assessed with the Boyce index (Hirzel et al., 2006), because other discrimination metrics such as AUC and TSS require absence data and may be misleading with pseudo-absences (Leroy et al., 2018) or in case of sample bias (Dubos et al., 2022). An index of 1 suggests that suitability models predicted perfectly the presence points, a zero value suggests that models are no better than random and a negative value implies a counter prediction. We discarded all models with a Boyce index < 0.2. We present projections of the mean suitability obtained across all fairly performing models (total number of models per species: 4 block-cross validation runs × 5 pseudo-absence runs × 6 ensemble of small models × 2 sources of climate data = 240 models; for future projections, potentially 240 × 2 scenarios × 3 GCMs = 1440 projections per species in the case where no poorly performing model is discarded).

**Accounting for uncertainty** – Model outcome varies with the choice of methodology and data (Baker et al., 2016; Dubos, Augros, et al., 2022; Dubos et al., 2023). To provide clear guidelines to field operators and account for uncertainty, we provide a single map per species and period (one for current and one for future conditions). Individually for each species and period, we computed the mean and standard deviation (SD) of all projections, and provide an average map penalised by its variability (mean – SD). The resulting map can be interpreted as a consensus suitability, showing the locations that are consistently identified as suitable across model replicates.

**Quantifying the effect of climate change** – We quantified the projected change in climate suitability (1) at the presence points, (2) across Madagascar forests and (3) inside of the Beampingaratsy candidate protected area. We computed the difference between the summed suitability scores of current and future predictions, expressed in percentage of change. This metric is similar to the Species Range Change (SRC, Buisson et al., 2010; Dubos et al., 2022a) and provide information on the direction and the magnitude of the change at a given location. We used the projected change in suitability at the presence points to inform potential population declines and provide recommendation for IUCN conservation status based on criterion A3 relative to projected population declines by 2070, which corresponds to at least three generation length for our study species as recommended in the IUCN guidelines (IUCN Standards and Petitions Committee, 2024; Mancini et al., 2024). We do not provide recommendations for species that are already classified into threatened category based on other criteria when the projected decline corresponds to a weaker category.

### 3. Results

Out of 240 models per species, a large proportion was poorly transferable (Boyce index < 0.2 assessed with a block-cross validation process). We discarded 96 ± 78 SD models in average per species and only kept the fairly transferable models. Across the 11 species and two climate data sources, the most selected predictors were Bio19 (winter precipitation, selected 19 times), Bio11 (mean winter temperature, n = 15) and Bio18 (summer precipitation, n = 12), followed by Bio7 (temperature annual range, n = 7), Bio4 (temperature seasonality), Bio16 (precipitation of wettest quarter), Bio5 (Max summer temperature), Bio10 (mean summer temperature), and Bio15 (precipitation seasonality) (n < 6).

At their current locations, we predict that all 11 species will experience an important decline in climate suitability by 2070 in all scenarios and for both climate data sources (Fig. 2). Overall, projections obtained with CHELSA climate data were slightly more pessimistic than those obtained with Worldclim. The climate data source affected more the results than the scenario. At the presence points, climate suitability will decline by 51.4 % ± 11.9 SD on average (Table 1). Following the IUCN criterion A3 (projected population decline), we propose that the conservation status of five species should be elevated to a threatened category (i.e. Vulnerable, Endangered or Critically Endangered): *Brookesia superciliaris*, *Caluma parsonii*, *Gephyromantis leucocephalus*, *Platypelis tubifera* and *Sanzinia madagascariensis*). We do not provide
<figure><img src="image_6.png" /><figcaption>**Fig. 2.** Climate suitability at the current population locations of 11 tetrapod species from Madagascar. Suitability values were obtained from ecological niche models projected on current and future (2070) conditions for two scenarios (SSP2 and SSP5) and two climate data sources. Each point corresponds to an occupied grid cell. Boxes are composed of the first decile, the lower hinge, the median, the upper hinge and the ninth decile.</figcaption></figure>

recommendations for two species that are already classified into
threatened category (i.e. *Daubentonia madagascariensis*, which is classi-
fied as Endangered based on criteria A2cd + 3 cd + 4 cd; and *Hapalemur*
*aureus*, classified as Critically Endangered based on criterion C2a(i);
Louis et al., 2020; Wright et al., 2020). Although we do not provide
recommendations for *Hapalemur aureus*, we predicted an alarming

decline in climate suitability (71.7 % by 2070).

Across the rest of Madagascar forests, we found a surprisingly
consistent increase in climate suitability for nine species (Fig. 3). Only
two lemur species showed a decrease in suitability (Cheirogaleus major
with CHELSA data and Hapalemur aureus with Worldclim data).

The candidate protected area will become more suitable in the future
<caption>**Table 1**</caption>

Study species and their sample size, current IUCN status, projected decline in climate suitability at the presence points by 2070 and recommendation for status update following IUCN criterion A3 (projected population decline). Species for which the protection status should be elevated to a threatened category are shown in bold.

<table><thead><tr><th></th><th>Sample size</th><th>IUCN status</th><th>Projected population decline (%)</th><th>Recommended status</th></tr></thead><tbody><tr><td><strong>Brookesia superciliaris</strong></td><td>37</td><td>LC</td><td>44.7</td><td>VU</td></tr><tr><td><strong>Calumna parsonii</strong></td><td>21</td><td>NT</td><td>44.8</td><td>VU</td></tr><tr><td>Cheirogaleus major</td><td>132</td><td>VU</td><td>48.3</td><td>VU</td></tr><tr><td>Daubentonia madagascariensis</td><td>80</td><td>EN</td><td>36.4</td><td>—</td></tr><tr><td>Eulemur collaris</td><td>80</td><td>EN</td><td>50</td><td>EN</td></tr><tr><td><strong>Gephyromantis leucocephalus</strong></td><td>30</td><td>NT</td><td>62.6</td><td>EN</td></tr><tr><td>Hapalemur aureus</td><td>128</td><td>CR</td><td>71.7</td><td>—</td></tr><tr><td>Mantella haraldmeieri</td><td>6</td><td>EN</td><td>53.7</td><td>EN</td></tr><tr><td>Microcebus tanosi</td><td>7</td><td>EN</td><td>64.5</td><td>EN</td></tr><tr><td><strong>Platypelis tuberifera</strong></td><td>31</td><td>LC</td><td>55.7</td><td>EN</td></tr><tr><td>Sanzinia madagascariensis</td><td>41</td><td>LC</td><td>32.6</td><td>VU</td></tr></tbody></table>

for nearly all study species (Figs. 4-6, Fig. S1). Only *Gephyromantis leucocephalus*, *Eulemur collaris* and *Hapalemur aureus* (for Worldclim data only) will find a less suitable climate inside of the protected area.

## 4. Discussion

We quantified the environmental niche of 11 forest species from

Madagascar, accounting for climate, habitat, sample bias and small sample sizes. All species will experience a decline in climate suitability within their current range, five of them should be classified in a threatened category. Although the most suitable areas will decline, we predict that climate suitability will increase throughout Madagascar forests overall. The climate will become more suitable at the location of the candidate protected area in most cases.

### 4.1. The predicted effect of climate change on Malagasy species

At the current population locations, climate change will likely increase both thermal and hydric stress, both during winter and summer. For endotherms, this may affect their body condition and survival, either due to a direct effect on their physiology or indirect effects on ecosystem production and food availability (Dubos et al., 2018). Climate change may also affect lemur health and survival through the expansion of parasite distribution and their associated pathogens (Barrett et al., 2013).

Changes in temperatures and precipitation regimes may affect the activity of amphibians and their reproduction (Dubos et al., 2020), as well as the characteristics of their breeding sites (Gómez-Rodríguez et al., 2010). Air and water temperature may also affect larval development (Benard, 2015; Thurman & Garcia, 2017) and juvenile survival (Bernal-bautista et al., 2017). For amphibian clutches, juveniles and adults, higher temperatures and lower precipitation will increase the risk of desiccation (Porcel et al., 2022; Shoemaker, 1992). In the tropics, ectotherms are more vulnerable to warming because they live closer to the upper bound of their thermal tolerance (Tewksbury et al., 2008), which may also apply to endotherms (Dubos et al., 2019; Freeman et al., 2021). This is particularly concerning for many amphibian species which depend on heavy rainfall episodes for reproduction. In some

<figure><img src="image_7.png" /><figcaption>**Fig. 3.** Change in total climate suitability across Madagascar forests between current and future (2070) conditions for two scenarios and two climate data sources. We removed from the plot the scores of *Microcebus tanosi* for Worldclim projections because they were largely off the chart (> 800 %).</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>**Fig. 4.** Variation in projected climate suitability inside the Beampingaratsy candidate protected area. Boxes are composed of the first decile, the lower hinge, the median, the upper hinge and the ninth decile.</figcaption></figure>

cases, shifting activity periods may mitigate the effect of warming (Kearney et al., 2009). For instance, species can buffer the impact of extreme heat by spending more time in the shade provided by trees (Stoutjesdijk & Barkman, 1992). However, our study species are strictly forest dwellers and therefore are more exposed to warming temperatures. Our results should be seen as more pessimistic than other predictive studies of non-forest species.

## 4.2. On the reliability of our conservation recommendations

We recommend the elevation of the conservation status for five species (all of which are amphibians or reptiles) relying on our projections. In spite of all the caveats related to incomplete data and model uncertainty, our models took into account most of the technical aspects that enable to mitigate uncertainty, included a wide panel of projections of future environmental change and our biological data encompassed wide gradients of environmental conditions, except for three species (*Mantella haraldmeieri*, *Microcebus tanosi*, and *Hapalemur aureus*). For these, the original habitat of the lowest parts of eastern Madagascar were removed, which truncates their climate niche. Therefore, the effect of climate change might have been overestimated. However, some forest patches are still present near the distribution of these species at lower elevations. It is possible that these species are actually adapted to mid-elevation forests and their niche was only slightly truncated. The occurrence data of the other eight species encompassed well the hotter lowlands of the eastern side and the effect of climate change may be well predicted.

The projected declines are consistent with population trends observed over the past decade and imply that many species classified as Least Concern should be classified in a threatened category (Porcel et al., 2025). In Madagascar, species are often classified according to the size

of their distribution range (criterion B) or the size of their populations (criterion C). A standardized method has been recently proposed to consider projected population declines (criterion A) in conservation assessments (Mancini et al., 2024). The inclusion of this criterion enables experts to account for the effect of future climate change in their assessment. In accordance with the IUCN guidelines (IUCN Standards and Petitions Committee, 2024), the authors recommend the use of two algorithms to account for uncertainty driven by the modelling technique (Baker et al., 2016; Buisson et al., 2010). However, not all algorithms perform well (Valavi et al., 2021), and we chose to use one highly performing algorithm, which is sufficient when combined with the ESM approach (Breiner et al., 2018). We discarded all models that were not spatially transferable, using an appropriate method adapted to presence-only data (Valavi et al., 2023). We further accounted for multiple climate data sources, a neglected, yet important source of uncertainty in ENM (Baker et al., 2016; Dubos et al., 2022), and provide a single consensus map as a practical tool for conservationists. We included species with low sample sizes, subject to spatial bias. Although we used the recently advocated methods to account for these aspects, low sample sizes remain the most important factor limiting the reliability of our projections (Bean et al., 2012; Gábor et al., 2019; Stockwell & Peterson, 2002). We encourage the development of further field sampling campaigns to improve species niche modelling and ultimately conservation assessments.

## 4.3. The importance of the Beampingaratsy forest

We found that climate suitability will decrease by 51.4 % on average inside their range, which is a comparable magnitude, although slightly less concerning, found in former studies (59.6 % average range contraction in lemurs, 73 % decrease in suitability for *Mantella*
<figure><img src="image_9.png" /><figcaption>**Fig. 5.** Example of projections of current and future (2070) climate suitability for two Endangered tetrapod species from Madagascar for which climate suitability will increase at the candidate protected area (red line). Consensus projections were obtained by averaging all CHELSA and Worldclim projections and penalizing them with uncertainty (mean – standard deviation).</figcaption></figure>

*aurantiaca*; Brown & Yoder, 2015; Dubos et al., 2022). On the other hand, suitable climates will become more available to most of our study species across the rest of Madagascar forests, although sub-optimal (e.g., maximum current suitability for *Brookesia superciliaris* = 0.85 versus 0.69 in the future; Fig. 5). This does not mean that climate change will benefit those species. Since the habitats are fragmented and species dispersal capacity is limited (1.4 km per year as estimated by experts; Brown & Yoder, 2015), they might not be capable of reaching the newly available environments through natural dispersal, especially in mountainous regions. Moreover, fragmentation is likely to keep increasing in the future as a result of a continued decline of forest cover (–25 % projected by 2050; Rafanoharana et al., 2024). This calls the need to consider habitat restoration and translocation.

The Beampingaratsy massif represents a suitable corridor between the two existing National Parks of Mindongy du Sud and Andohahela. The implementation of a high protection status will help preserving the forests of the massif and ensure an ecological continuity for its dwellers. Such ecological continuity will facilitate southward distribution shifts that are likely to occur through natural dispersal and contribute to the persistence of Malagasy forest species.

CRediT authorship contribution statement

**Nicolas Dubos:** Writing – original draft, Supervision, Methodology, Investigation, Formal analysis, Data curation, Conceptualization.

**Pauline Jitten:** Writing – review & editing, Investigation, Formal
<figure><img src="image_10.png" /><figcaption>**Fig. 6.** The effect of climate change on climate suitability for two Endangered tetrapod species from Madagascar. The maps represent the difference in projected suitability between future (2070) and current conditions.</figcaption></figure>

analysis, Data curation. **Frederique Montfort:** Writing – review & editing, Validation, Investigation, Conceptualization. **Clovis Grinand:** Writing – review & editing, Investigation, Funding acquisition, Conceptualization. **Eddie Fanantenana Rakotondrasoa:** Writing – review & editing, Project administration, Data curation. **Marie Nourt-ier:** Writing – review & editing, Validation, Supervision, Project administration, Investigation, Funding acquisition, Conceptualization.

## Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Acknowledgements

We would like to thank the French Development Agency (Agence Française de Développement, AFD), which funded the Talaky project of NGO Nitidae under which this study was conducted.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.jnc.2025.126925.

## Data availability

Data will be made available on request.

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