Research Article |
Corresponding author: Jorge Mella-Romero ( jorgemella@ug.uchile.cl ) Academic editor: Lukas Landler
© 2024 Jorge Mella-Romero, Sebastián Maya-Miranda, David Véliz, Javier A. Simonetti.
This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Citation:
Mella-Romero J, Maya-Miranda S, Véliz D, Simonetti JA (2024) Assessing the vulnerability of a sky island lizard to climate and land-use change. Herpetozoa 37: 257-267. https://doi.org/10.3897/herpetozoa.37.e125163
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Under climate change, species are expected to migrate along with their climate envelope. However, many species’ distribution models do not include the human footprint, thus overestimating distributional zones with high probabilities of occurrence. Species inhabiting sky islands (high-elevation landscapes that differ from landscapes in intermediate valleys) are particularly sensitive to climate and land-use change, given their limited ability to migrate. We aimed to assess the suitability of the climatic conditions for a sky island lizard under different climate scenarios and how that could affect its distribution based on (i) its climate envelope and (ii) the human footprint (croplands and buildings). Using climatic variables to develop a species distribution model and the indicator Human Footprint, we predicted the presence probabilities of Liolaemus nigroviridis Müller & Hellmich, 1932 populations under climate change scenarios (current, year 2040, and year 2080). We analyzed the relevant variables for L. nigroviridis’s climate envelope, which we projected to decrease and shift southward by 2080. The species could track its climate envelope in the Andes, but not in the Coastal mountains, given the strong human footprint. We propose assisted migration as a possible adaptive strategy, and show that conservation of sky islands species can be enhanced by integrating climatic and anthropogenic factors.
assisted migration, climate envelope, conservation, human footprint, Liolaemidae, management, Random Forest, species distribution model
Currently, biodiversity is rapidly declining due to climate change and habitat modification such as land-use change (
Species with dispersal ability tend to follow their climate envelope as long as there are no land-use changes preventing their movement (
Sky island species (i.e. species inhabiting patches in elevated zones that differ notably from patches in intermediate valleys;
Herpetozoans are one of the groups most affected by climate and land-use change (
Within this context, we assessed the suitability of the climatic conditions for L. nigroviridis under different climate scenarios and how that may affect its distribution based on (i) its climate envelope and (ii) the human footprint. We hypothesized that L. nigroviridis will show a southward distribution shift and a reduced geographic range (limited to higher altitudes compared to the current distribution), assuming that the human footprint does not affect this movement in its climate envelope.
To determine potential distributional changes based on the species’ climate envelope, we developed an SDM to identify areas with the most suitable climatic conditions for the species and to provide presence probabilities for the future scenarios. Regarding the development of SDMs, studies have demonstrated the effectiveness of machine learning algorithms compared to other methods, such as logistic regression (
We used a total of 199 georeferenced records from 53 different localities gathered from literature reviews, online museum collection catalogues, and web platforms (GBIF and iNaturalist) (
Pseudoabsences (n = 597) were generated using the BIOMOD2 package (version 4.2.5;
For the inclusion of climatic variables in the model, we relied on: (i) the information on the biology/ecology of the study species (thermoregulation in the context of climatic variables); (ii) the background literature on Liolaemus species with similar habitat requirements in terms of variables used for SDMs; (iii) multicollinearity among the climatic variables; and (iv) RF Importance index. These selection criteria were applied to the 19 climatic variables of the WorldClim 2 dataset (
Percentage of change between the current and the 2080 (RCP 8.5) scenarios for each variable. We developed this table using the mean values of the variables, obtained from Worldclim 2 dataset (
Variable | ID | 2080 (8.5)/Current | % of change 2080 (8.5) |
---|---|---|---|
Annual mean temperature | Bio1 (°C) | 1.21 | 21.39 |
Mean diurnal range | Bio2 (°C) | 1.03 | 2.71 |
Isothermality | Bio3 (%) | 1.01 | 0.70 |
Temperature seasonality | Bio4 (ED) | 1.02 | 1.81 |
Max. temperature of warmest month | Bio5 (°C) | 1.11 | 11.44 |
Min. temperature of coldest month | Bio6 (°C) | 1.71 | 70.97 |
Temperature annual range | Bio7 (°C) | 1.03 | 2.88 |
Mean Temperature of wettest quarter | Bio8 (°C) | 1.25 | 25.24 |
Mean temperature of driest quarter | Bio9 (°C) | 1.20 | 19.53 |
Mean temperature of warmest quarter | Bio10 (°C) | 1.16 | 16.28 |
Mean temperature of coldest quarter | Bio11 (°C) | 1.30 | 30.23 |
Annual precipitation | Bio12 (mm) | 0.71 | -28.90 |
Precipitation of wettest month | Bio13 (mm) | 0.64 | -35.62 |
Precipitation of driest month | Bio14 (mm) | 1.00 | 0.00 |
Precipitation seasonality | Bio15 (Coef.) | 0.92 | -8.48 |
Precipitation of wettest quarter | Bio16 (mm) | 0.67 | -33.15 |
Precipitation of driest quarter | Bio17 (mm) | 1.00 | 0.00 |
Precipitation of warmest quarter | Bio18 (mm) | 1.00 | 0.00 |
Precipitation of coldest quarter | Bio19 (mm) | 0.67 | -33.33 |
Liolaemus nigroviridis is a reptile species that, like other members of the genus Liolaemus, depends on ambient temperature for thermoregulation (
Then, to refine the selection of variables, we evaluated the multicollinearity and importance of the climatic variables in two stages. In the first step, we used the Variance Inflation Factor (VIF) from R package USDM (
Therefore, according to the previously mentioned criteria, we decided to model with the variables mean diurnal range (Bio2), seasonality of temperature (Bio4), minimum temperature of coldest month (Bio6), annual precipitation (Bio12), precipitation of driest quarter (Bio17), and precipitation of warmest quarter (Bio18).
To describe the change in the climatic variables, we calculated the rate of change (expressed in %) between the current model and the 2080 (RCP 8.5) scenario for each variable obtained from the WorldClim 2 dataset (
To determine habitat suitability of the climate envelope under current and future conditions, we used the six raster layers (Bio2, Bio4, Bio6, Bio12, Bio17, Bio18) out of the 19 climatic variables at a 30-second (1 km) resolution from the WorldClim 2 dataset (
All SDM visualizations for present and future projections were performed using R modeling package BIOMOD2. We employed a five-repeat scheme (Run1 to Run5), focusing on an RF algorithm with 2,000 trees (see e.g.
With the best model (according to metrics), we developed projected habitat suitability maps for L. nigroviridis under present and future climatic conditions in RCP 4.5 and RCP 8.5 scenarios. Then, these projections were visualized as distribution maps to show the geographical zones of high and low suitability probability of species presence (Fig.
SDMs for Liolaemus nigroviridis. Habitat projections for L. nigroviridis generated under climatic layers (GCM: MPI-ESM1-2-HR); human footprint layer (WorldCover); Chile regional division (Biblioteca del Congreso Nacional de Chile); 199 presence points (panel A,
To analyze potential changes in suitable habitat distribution for L. nigroviridis, we used the BIOMOD_RANGESIZE function from BIOMOD2 (following the method described by
Percentage change of the Liolaemus nigroviridis climate envelope in different scenarios (areas with probability of occurrence > 0.6). Calculations based on surface area (km2) using the function BIOMOD_RANGESIZE of R software.
Scenario | Area (km2) | %Area Gain | %Area Loss | %Total Change |
---|---|---|---|---|
Current | 3,576.9 | - | - | - |
2040 RCP 4.5 | 3,538.2 | 26.4 | 27.5 | -1.1 |
2040 RCP 8.5 | 3,465.7 | 33.2 | 36.3 | -3.1 |
2080 RCP 4.5 | 3,336.3 | 29.4 | 36.1 | -6.7 |
2080 RCP 8.5 | 2,058.0 | 16.6 | 59.1 | -42.5 |
While an SDM based solely on climatic variables can effectively depict a species’ distribution on a broad scale, it may overestimate its regional distribution by including areas with unsuitable habitats due to land use. Many studies that model habitat suitability do not consider this factor (
To produce the maps, habitat projections for L. nigroviridis generated under an RF algorithm at current and future scenarios (GCM: MPI-ESM1-2-HR, https://www.worldclim.org/), with a human footprint layer (WorldCover, http://https://viewer.esa-worldcover.org/worldcover/), Chile regional divisions (Biblioteca del Congreso Nacional de Chile, https://www.bcn.cl/), and 199 occurrences (
When analyzing the climatic variables (Table
The best-performing model obtained a TSS of 0.86 and a ROC of 0.98. For this type of algorithm (i.e. machine learning: RF), a value of TSS and ROC > 0.85 is considered indicative of good performance (
The BIOMOD_RANGESIZE function indicated that under the 2080 (8.5) scenario, the areas with probable presence of the species (i.e. > 0.6) would decrease by 42.5% compared to the current scenario (3,577 km2 to 2,058 km2) (Table
There was no climate envelope for the species in the Coquimbo Region under the 2080 (8.5) scenario (regional extinction, purple rectangle of Fig.
Southward movement of the climate envelope for Liolaemus nigroviridis. Habitat projections for L. nigroviridis generated under climatic layers (GCM: MPI-ESM1-2-HR) for the years 2040 (panel A) and 2080 (panel B) under RCP 8.5 conditions. In both panels, the blue outline indicates the current distribution with a probability presence > 0.6, as generated by the model (based on the Random Forest algorithm). The red vertical lines represent the climate envelope expansion towards the south in the Andean mountain range. The pink rectangle indicates the area that remains towards the future in the Coastal mountain range (near Coltauco). These maps were produced in the R environment (R Core Team, version 4.3.2) using the packages: BIOMOD2 version 4.2.4 (Thuiller et al. 2023) and TERRA version 1.7.65 (
The human footprint (croplands and buildings) in the Coastal mountain range was greater than that in the Andean mountain range (Fig.
In this study, we assessed the suitability of the climatic conditions for a sky island lizard under different climate scenarios and how that may affect its distribution based on its climate envelope and human footprint (croplands and buildings). For this purpose, we hypothesized that L. nigroviridis will undergo a distributional shift toward the south, with a smaller geographical range limited to higher altitudes compared to its current distribution, given the pressures of climate and land-use change. We corroborated our hypothesis regarding the latitudinal (southward) movement of the species’ climate envelope into the future and the decrease of the same, but not regarding the longitudinal (altitudinal) movement and the human footprint, which would affect a potential future migration of the species in the Coastal mountain range, but not in the Andean mountain range.
Habitat suitability for L. nigroviridis (in terms of high probability of occurrence: > 0.6) decreased by 42.5% in the most catastrophic future scenario (2080; 8.5). The reduction of the species’ climate envelope in the future was remarkable, with no high probability of the species’ presence in the Coquimbo Region (Fig.
The decrease and latitudinal movement of the species’ climate envelope in the future can be explained by the most relevant climatic variables (according to the biology of L. nigroviridis and RF Importance index): temperature seasonality, annual precipitation, precipitation of the driest quarter, and precipitation of the wettest month. These variables are closely associated with precipitation/humidity and its seasonality. All the variables related to temperature showed an increase between the current scenario and 2080 (8.5), while the variables related to rainfall and humidity showed a decrease in the same time range (except those associated with rainfall in warm months) (Table
Precipitation and humidity directly influence the existence of high Andean shrubs, which play a significant role in the thermal ecology of Liolaemus species (
Additionally, humidity can significantly impact insect abundance (L. nigroviridis is mainly insectivorous;
Despite the hypothesis that biota in South America’s southern cone may need to seek higher altitudes to escape high temperatures caused by climate change (see e.g.
Thus, our analysis suggests that L. nigroviridis will have a more restricted climate envelope in future scenarios and, therefore, would seek to track the current climatic conditions to the south, especially in the Andes, where there is a low human footprint. The populations of these mountains have geographical accidents as dispersal barriers, such as river basins (
Assisted migrations have recently been proposed as adaptive strategies to climate change (
Our work has the scope to suggest the vulnerability to climate change of other reptile species present in the area (e.g. Liolaemus bellii, Liolaemus leopardinus, and Pristidactylus volcanensis;
JM-R thanks to ANID; CONICYT-PCHA, Doctorado Nacional/2019-21190472 for financing his postgraduate studies. The authors thank two reviewers, who improved the manuscript with their comments.