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Article

The Vulnerability of Malagasy Protected Areas in the Face of Climate Change

by
Qi Lai
1,* and
Carl Beierkuhnlein
1,2,3,4,*
1
Department of Biogeography, University of Bayreuth, Universitaetsstr. 30, 95447 Bayreuth, Germany
2
Bayreuth Center of Ecology and Environmental Research (BayCEER), University of Bayreuth, Universitaetsstr. 30, 95447 Bayreuth, Germany
3
Geographical Institute of the University of Bayreuth (GIB), Universitaetsstr. 30, 95447 Bayreuth, Germany
4
Departamento de Botánica, Universidad de Granada, 18071 Granada, Spain
*
Authors to whom correspondence should be addressed.
Diversity 2024, 16(11), 661; https://doi.org/10.3390/d16110661
Submission received: 30 August 2024 / Revised: 20 October 2024 / Accepted: 24 October 2024 / Published: 27 October 2024
(This article belongs to the Special Issue 2024 Feature Papers by Diversity’s Editorial Board Members)
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Abstract

:
This study examines the vulnerability of Madagascar’s protected areas (PAs) to climate change, focusing on climate change velocity, and its impact on biodiversity. We analyzed current and near future climate data using principal component analysis (PCA) and climate change velocity metrics to predict shifts in climatic conditions from the present to the near future, while under the mild and extreme emission scenarios (SSP 126, SSP 585). Forward velocities, which are characterized by the minimum distances that must be overcome by species to keep in track with their appropriate comparative climate, are most pronounced in western and southern Madagascar. In contrast, the backward velocity, which uses future climatic conditions in grid cells in comparison to current conditions, is more common in the eastern regions of the island, and hints at the minimum distance that organisms would have to overcome in colonizing a new habitat. Even though the correlations between PA size and climate change velocity are weak, there is a tendency for larger PAs to exhibit more stable climatic conditions. Conservation strategies must prioritize enhancing the resilience of PAs through adaptive management to mitigate climate impacts. Our findings provide crucial insights for policymakers and conservation planners to develop climate-smart strategies that ensure the long-term efficacy of Madagascar’s PA network.

1. Introduction

Anthropogenic climate change poses an increasingly key challenge to biodiversity conservation [1]. This applies most of all to tropical biodiversity hotspots where options for coping strategies are dependent on the socio-economic situation. Uncertainties about realized scenarios for mitigation and the resulting climate change projections, along with the variability of biotic responses, increase the difficulty of climate-smart conservation planning. Protected areas (PAs) are the main instruments in nature conservation, but they may conflict with the spatial need for land use in developing countries with strong population increases and a lack of alternatives to subsistence farming. This is why conservation planning requires clear priorities and strategies for maintaining biodiversity and supporting nature’s contribution to its people [2,3,4].
Changes in species diversity can result in alterations to ecosystem functioning and services [5,6,7]. Predicting how biodiversity will respond to climate change is required to guide climate-proof conservation management on how and where to modify biodiversity conservation strategies [7,8,9,10]. PAs represent an indispensable tool for the preservation of biodiversity. Conservation objectives must be prioritized given that conservation resources, such as the workforce and funds, are limited [3,11]. However, climate change is a growing threat to PAs as it modifies abiotic habitat conditions with unknown consequences for future species distribution and ecosystem integrity. Climate change has the potential to render habitats within PAs unsuitable for species that are the focus of conservation endeavors [12]. Consequently, species are forced to adapt. The manner of adaptation depends on their adaptive capacity, dispersal ability, and biotic interactions [4,13]. It is possible that threatened species may be driven out of PAs into unprotected surroundings to track suitable habitat conditions under climate change [1,14]. This may result in an increase in extinction risks as non-protected landscapes are highly degraded.
Several studies predict that PAs may not retain the species diversity they were meant to protect [15,16,17]. Protected areas potentially even lose species diversity [18,19]. The geography of PAs is mainly based on the occurrence of threatened biodiversity and human land use, but neglects the threats from climate change [20]. Protected areas are spatially fixed and adding new sites or relocating existing sites to cover suitable climate conditions for migrating species are limited to human-dominated landscapes. Consequently, developing climate-smart management of existing protected area networks should be a primary task for conservationists [21]. Gaining insight into the climate change exposure of protected area networks is a fundamental step to facilitating and prioritizing climate-proof conservation management [3,22].
Madagascar stands out as one of the “hottest” conservation hotspots due to its high biodiversity and high degree of endemism [23,24,25]. Madagascar’s diverse environmental conditions and the island’s long-term isolation have led to the formation of an exceptional flora and fauna. With about 8.6% of its total flora listed on the CITES, its natural vegetation is the most remarkable worldwide. Madagascar also plays an outstanding role in the global biodiversity of amphibia and primates [26,27].
This large island with its unique biota and ecosystems is also known for its severe land use and land cover changes. Madagascar has suffered from the ongoing loss of its original primary vegetation [23]. It lost more than 40% of its forest cover from 1950 to 2014 [28,29]. The annual rate of deforestation in the eastern rainforest alone increased from 0.5% to 0.9% between the years 2005–2011 and 2010–2013, respectively, with a high level of confidence [30]. This is why the protection of forests is a key concern in nature conservation [31].
Protected areas are expected to safeguard species and habitats in the long term. However, the looming impact of climate change could not be foreseen when the network of Malagasy protected areas was established [32]. Hence, PAs are prone to missing their conservation objectives due to climate change impacts, and their performance in protecting biodiversity under a changing climate remains to be interrogated [18,33,34]. To ensure the enduring effectiveness of these PAs, their conservation management must adapt to potential climate change impacts.
Climate change velocity offers a metric for use in conservation prioritization that is not dependent on specific biotic data [22,35,36,37]. The forward climate change velocity is determined by measuring the distance between a grid cell’s current climate classification and the nearest cell with the same classification under anticipated future conditions [22,35,36]. This calculation provides an insight into the minimum migration or dispersion distance required for organisms to maintain pace with their climatic habitat as it shifts due to climate change [22]. Conversely, the backward climate change velocity is computed by assessing the distance between a grid cell’s anticipated future climate classification and the nearest cell with the same classification under existing conditions [22,35,36]. This measure represents the minimum migration or dispersion distance organisms must traverse to reach a specific cell to maintain their climatic habitat [22].
This study intends to improve our understanding of the current and future threats to Malagasy PAs and their biota and ecosystems in the face of climate change. We aim to support the design of adaptation and coping strategies at a larger landscape-to-regional scale for decision makers and planners who are responsible for a large set of PAs or are charged with maintaining their functions in the future. However, all this must be done in accordance with local communities and indigenous people in order to be effective.
This study hypothesizes that: (1) PAs are more likely to exhibit lower climate change velocities compared to areas outside their boundaries, thereby sufficiently protecting biodiversity from the impacts of climate change. (2) Larger PAs are expected to provide more stable climatic conditions, by effectively safeguarding biodiversity from climate change due to their size and the diversity of habitats they encompass.
Therefore, we provide as an outcome an evaluation of priorities for the conservation of PAs in Madagascar and deliver suggestions for additional PAs to improve the network and its functionality.

2. Materials and Methods

2.1. Data Sources

Data and shape files on protected areas were retrieved from the World Database on Protected Areas [38]. For the modeling, we used the data obtained by April 2024 (Figure A1). Madagascar has 171 protected areas in total, but only 73 of them are supported by management effectiveness evaluations. Although there were previous protection efforts, most PAs were installed between 2010 and 2015; therefore the remaining forests are a main target of nature conservation in Madagascar.
Climate data and bioclimatic variables for Madagascar were obtained from CHELSA [39,40].
The ecoregions for Madagascar were sourced from the World Wildlife Fund Global 200 Ecoregions [41], which were cropped to the Madagascar administration boundary obtained from the GADM Database of Global Administrative Areas, version 4.1 [42]. However, these ecoregions do not explain the real vegetation or ecosystems, because these may have been degraded strongly. Nevertheless, they formed a baseline for orientation and strategies.

2.2. Data Analyses

For our study in Madagascar, the current climate data (representing the average conditions for the period spanning from 1981 to 2010) provided comprehensive information on 19 bioclimatic variables. Projected climate data for future scenarios covering the years from 2061 to 2080 (referred to as 2070 plus minus 10 years) were also obtained at the same resolution. These future climate scenarios were selected from a low-emission scenario (SSP 126) and a high-emission scenario (SSP 585), which were derived from five Global Climate Models (GCMs), including GFDL-ESM4, UKESML-01L, MPI-ESM1-2-HR, IPSL-CM6A-LR, and MRI-ESM2-0, at the spatial resolution of 30 arc-second (approximately 1 km2).
To analyze the relationships between current and future climate conditions, principal component analyses (PCAs) were conducted for each combination of current and future climate datasets. This process aimed to condense the original 19 bioclimatic variables into independent components (see Table A1).
In our study, climate change velocity metrics were established through the assessment of distances between grid cells featuring analogous climate classifications under both current and projected future climate scenarios. The forward climate change velocity takes the current conditions in a given grid cell and compares it with future conditions and vice versa the backward velocity takes future projections as a baseline. Initially, each grid cell of 30 arc-seconds within the Madagascar study area was assigned a specific climate class. These classifications were delineated by dividing up the principal component analysis (PCA) climate space, which was derived from 19 bioclimatic variables and encompassed all the grid cells across Madagascar. By partitioning each PCA axis into bins, unique combinations of these bins along the principal components constituted the various climate classes. Given the sensitivity of climatic velocity to the delineation of climate classes, a meticulous sensitivity analysis was conducted to determine the optimal bin width for PCA delimitation. This process was not influenced by different GCMs or SSPs. Consequently, a sensitivity analysis was performed using the MPI-ESM1-2-HR GCM and SSP 585 to streamline the computational efficiency. This analysis was the basis for the selection of an appropriate bin width to reduce the risk of an overestimation or underestimation of the predicted climate change effects. Thus, we evaluated the relationship between the number of bins along the first PCA axis and the resulting climate classes, the proportion of climate classes without future analogs, and the median distance between current and future climate analogs. Ultimately, a moderate amount of climate classes were selected, which were defined by five bins on the first PCA axis (equating to a bin width of 3.98 PCA units). This choice resulted in 156 climate classes across Madagascar, which was deemed optimal for optimizing the information content of the velocity maps. The forward and backward climate change velocities were then computed using the R code from Lai et al. (2022) [22]. The unit for climate change velocity was expressed in kilometers per year (km/year) and calibrated to the grid cell size and study period. This unit represented the rate at which climate analogs shift spatially, indicating how quickly the species or ecosystems must migrate to remain within the same climate class over time [22].

3. Results

3.1. Distance-Based Climate Change Velocity Across Madagascar

The mean climate change velocity includes both the forward and backward velocities, which represent the rates at which climate conditions are progressing or reverting, respectively (Figure 1). Regions with the highest forward climate change velocity were primarily located in the western and southern parts of Madagascar. Central Madagascar and the eastern coastal areas showed moderate forward climate change velocities, and some parts of the northeastern areas exhibited the lowest forward velocities. High backward velocity areas were less common but were present in some regions, mostly in the east. In the western and southern parts of Madagascar, where the forward velocity tended to be high, the backward velocity was comparatively low. The findings were generally consistent across the two different SSP scenarios, with lower overall velocities observed under SSP 126 (low-emission scenario), and the highest velocities under SSP 585 (high-emission scenario).

3.2. Climate Change Velocity Inside and Outside Malagasy Protected Areas Per Ecoregion

The PA coverage across different ecoregions in Madagascar revealed significant variability in conservation efforts (Figure 2). The 171 PAs did not represent all ecoregions equally. The total PA coverage in Madagascar’s terrestrial area was relatively low, encompassing less than 15% of the country’s land area. The donut chart provides a visual representation of the distribution and protection status of seven ecoregions in Madagascar. The outer ring illustrates the percentage of Madagascar’s terrestrial area which is covered by each ecoregion. The largest ecoregion was the Subhumid Forests covering 33.61% of the land, while the smallest was the Ericoid Thickets covering only 0.22%. The inner ring indicates the percentage of PAs within each ecoregion. Notably, the Ericoid Thickets ecoregion had the highest coverage of PAs at 45.47%, while the Succulent Woodlands had the lowest at 10.93%, although in this ecoregion it had the most PA numbers.
The number of PAs per ecoregion in Madagascar did not proportionately reflect the coverage within each ecoregion, as many of these PAs were quite small and were counted even if only a tiny portion of the protected area was present in that ecoregion.
Across all ecoregions, the mean climate change velocity increased with the severity of the applied climate change scenario i.e., the socio-economic pathway such as SSP126 or SSP585 (Figure 3). Under SSP 126, the Lowland Forest and Subhumid Forest ecoregions had the highest velocity values and the widest distribution, indicating considerable variability within these ecoregions. The Ericoid Thickets and Succulent Woodlands displayed higher median velocities compared to other ecoregions, with significant differences noted between these and other ecoregions (p < 0.01). Mangroves needed to be seen separately because they are more controlled by water temperature and sea level rises, which could not be considered here.
Under SSP 585, the overall trend of higher velocities persisted with the Dry Deciduous Forests showing a notable increase in velocity, making it significantly different from other ecoregions (p < 0.001). The Subhumid and Lowland Forests continued to have the highest velocities, with Ericoid Thickets also showing elevated velocities, although less extreme. Across all SSP scenarios the differences between the ecoregions remained significant, emphasizing the varied impact of climate change across different habitat types.
The backward velocity trends showed fewer regions with significant reversion of climate conditions (Figure A2). Under SSP 585, there was a trend of increasing velocity with the enlargement of ecoregion sizes. However, this trend was specific to this scenario and was not observed under other scenarios. The velocity range within the Lowland Forest and Subhumid Forest ecoregions appeared to be quite extensive, reflecting their significant coverage across the island. Similarly, the Dry Deciduous Forests ecoregion exhibited a wide velocity range, given its substantial spatial extent. In contrast, despite its limited coverage on the island, the mangroves ecoregion also displayed a considerable velocity range. This may be attributed to the distribution of mangrove ecoregions along the western coastal sites across different latitudes.
Overall, climate change velocity tended to be lower within PAs across various ecoregions compared to their surrounding matrix, with the highest values typically observed in regions outside the PAs (Figure 4). However, there were notable exceptions, particularly in the Mangrove and Spiny Thicket ecoregions, where biases were more pronounced.
Under SSP 126, the climate change velocities were generally lower compared to SSP 585. Notably, the Lowland Forests exhibited the highest velocities in this scenario. Across all ecoregions the velocities inside the PAs were consistently lower than those outside, with statistically significant differences (p < 0.0001). Despite this variability the pattern of lower velocities inside the PAs compared to outside remained, with all differences being highly significant. The SSP 585 scenario showed the highest velocities among the three scenarios. Significant differences between protected and non-protected areas persisted across all ecoregions. The Subhumid Forests and Mangroves exhibited notably high velocities outside the PAs.
There were inconsistent correlations between the size of the protected areas (in km2) and the projected climate change velocity (in km/year) for different ecoregions under the SSP 585 scenario (Figure 5). This is due to the fundamental differences not only in numbers but also in area sizes of protected areas within ecoregions. There were hints of positive effects in larger area sizes, for instance in the Ericoid Thickets, but these covered only a small part of the island. In arid landscapes with Spiny Thickets and Succulent Woodlands there was no positive effect of larger areas to be seen. The Lowland Forests and the Dry Deciduous Forests displayed ambiguous trends, with no significant correlation between the protected area size and climate change velocity. The Subhumid Forests showed a slight negative correlation, but the data points exhibited considerable variability suggesting that other factors might be superimposed onto climate change velocity in these areas. However, in general, larger PAs exhibited lower climate change velocities.

4. Discussion

The impacts of climate change on Madagascar’s terrestrial protected areas were assessed by analyzing climate change velocity under two emission scenarios. Projections from five GCMs were utilized, which covered both SSP 126 (optimistic) and SSP 585 (pessimistic) scenarios. The overall mean climate change velocity in Madagascar showed significant variation across these scenarios. As expected, SSP 585, representing the highest-emission scenario, exhibited the greatest climate change velocity, indicating the most rapid environmental shifts. This scenario underscores the potential for severe impacts on ecosystems, emphasizing the urgency for mitigation and adaptation strategies to address these drastic changes [43]. In SSP 126, the lower velocities reflected the benefits of a low-emission pathway, which aligns with global climate goals aimed at minimizing ecological disruption. The substantial increase in velocities in SSP 585 underscores the escalating threat posed by higher emission trajectories. In particular, the extreme velocities observed in the Subhumid Forests and Mangroves under SSP 585 in outside PAs signaled urgent conservation needs.
When comparing climate change velocity across different ecoregions, the results indicated varying levels of vulnerability. Some ecoregions, such as the Subhumid Forests ecoregion, were more susceptible to rapid climate changes than others, suggesting that adaptive strategies need to be region-specific to effectively mitigate impacts [4,10]. This variation highlights the complex nature of climate change effects and the need for detailed, localized studies to understand the specific vulnerabilities and adaptive capacities of different ecoregions.
Furthermore, the comparison of climate change velocity for inside and outside PAs within each ecoregion revealed that PAs generally experience lower velocities. This suggests that PAs might serve as refugia against the rapid environmental changes occurring outside their boundaries [44]. However, it is important to note that many PAs will face significant climate pressures, indicating a need for enhanced management and conservation efforts. These findings underscore the critical role of PAs in biodiversity conservation, yet also highlight their vulnerability and the necessity for ongoing support and intervention [12,17,20,21].
Interestingly, an examination of the backward velocity presented in the appendix (Figure A3) revealed that some ecoregions exhibited no significant differences in climate change velocities when comparing inside versus outside PAs. This suggests that the efficacy of PAs in buffering against climate change may vary depending on the direction of climatic shifts and the specific landscape characteristics of ecoregions, respectively [4,20].
By combining the forward and backward climate velocity, we can identify regions poised to experience significant climatic shifts (Figure A4, in red). These regions warrant targeted interventions to enhance their resilience to future climatic changes. Figure A4 also highlights areas which exhibit slower shifts in climatic conditions compared to surrounding regions (in blue). The relative stability in these areas is vital for preserving biodiversity and maintaining ecosystem resilience. Such stability can act as a refuge for species threatened by rapid climate changes elsewhere.
This bivariate map serves as a valuable tool for PA management and planning. By providing insights into both the pace and direction of climate shifts, it helps prioritize areas for conservation efforts and informs strategies to safeguard biodiversity against the impacts of climate change. This approach ensures that conservation resources are allocated effectively; thus supporting long-term ecological integrity and resilience.
These findings emphasize the importance of PAs in mitigating climate change impacts and support the need for robust climate policies that prioritize low-emission pathways and enhanced conservation efforts to safeguard vulnerable ecoregions [45]. However, the lack of significant differences in the backward velocities for some ecoregions suggests that adaptive management strategies may be necessary to comprehensively address the complex dynamics of climate change impacts.
The outstanding role of Madagascar in global biodiversity, along with its high degree of endemism, requires urgent attention in the face of climate change. This study provides future projections that illustrate the intensity of climate change at the scale of ecoregions and their PAs. It can serve as a basis for prioritization and decision making as the remaining time to preserve the unique flora, fauna, and ecosystems of Madagascar is short.
The looming threat of climate change to Madagascar’s biodiversity must not be neglected; however, the rapid and extensive impacts of deforestation may pose a more immediate and substantial risk than climate change alone [28,29,46,47]. Historical analyses reveal extensive forest fragmentation and habitat loss over the past six decades, with deforestation rates potentially exceeding the pace of climate-driven changes [28,29,47]. Ongoing forest loss and fragmentation are expected to continue impacting Madagascar’s ecosystems [30,31]. Recent projections suggest that the combined impact of deforestation and climate change could lead to a 93% reduction in suitable habitats for certain species by 2070, with deforestation alone accounting for a 29–59% reduction [47]. Even if deforestation were halted within protected areas, climate change could still reduce suitable habitats by up to 62% [47]. These findings underscore the necessity of integrating deforestation data into climate models to avoid underestimating the overall threats to biodiversity and ecosystem services.
Future research should therefore prioritize incorporating historical land-use patterns and deforestation trends into climate projections, enabling more comprehensive environmental threat assessments and informed conservation strategies [1,17,48]. Maintaining and enhancing the integrity of protected areas, where deforestation rates are lower, will also be essential to ensure the persistence of biodiversity in rapidly diminishing habitats. By adopting integrated approaches that consider both climate change and deforestation, we can more effectively safeguard Madagascar’s unique biodiversity in the face of these interconnected global environmental challenges.

5. Conclusions

Mitigating the challenges which climate change poses to Malagasy PAs requires comprehensive insight. This study’s findings demonstrate varied impacts across different ecoregions and PAs, highlighting the necessity for tailored management strategies that consider each ecoregion’s specific vulnerabilities. Developing and implementing adaptive management plans tailored to the specific vulnerabilities and needs of different ecoregions will significantly enhance the resilience of these areas, enabling them to better withstand climate-induced changes [4,48]. Additionally, the continuous monitoring of climate change impacts within PAs is crucial for informing adaptive management practices and ensuring timely responses to emerging threats [22].
Strengthening the protection and management of highly vulnerable areas, particularly those experiencing high climate change velocity, is essential for safeguarding biodiversity and preventing irreversible losses [10,22]. As a primary driver of biodiversity loss, deforestation must be incorporated into conservation strategies to address both habitat degradation and the wider consequences of climate change [29,47]. Engaging local communities in conservation efforts is vital for building resilience and promoting sustainable practices that can mitigate climate change impacts while also supporting local livelihoods [46]. Integrating climate change projections and findings into national and regional conservation policies will ensure proactive decision making based on the latest scientific evidence, thereby enhancing the effectiveness of conservation strategies [1,34].
However, challenges such as weak law enforcement and pressure from agricultural expansion persist. Many PAs are surrounded by degraded land, and population growth continues to put a strain on resources, despite legal protection. Forests are protected by law, but this is difficult to implement given the pressures of an increasing human population. Prioritizing the matrix of PAs, along with the human populations living within them, remains essential. It is important to recognize that, in most Malagasy ecoregions, there are very limited semi-natural habitats left for protection. While biomes and ecoregions represent the potential distribution of ecosystems, they do not necessarily reflect the current state of the remaining natural habitats. Particularly, in the Dry Deciduous Forest or in the Lowland Forest ecoregions there are hardly any forests left. Given the loss of natural habitats and shifting biomes due to climate change, conservation strategies must be flexible and adaptable [49].
By addressing these recommendations, policymakers and conservationists can better prepare for and mitigate the impacts of climate change on Madagascar’s valuable PAs, ensuring that these natural treasures continue to thrive despite the challenges posed by a changing climate.

Author Contributions

Conceptualization, Q.L. and C.B.; methodology, Q.L.; formal analysis, Q.L.; resources, C.B.; data curation, Q.L.; writing—original draft preparation, Q.L. and C.B.; writing—review and editing, C.B. and Q.L.; visualization, Q.L.; and supervision, C.B. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Data Availability Statement

All data used in this study are available as open access data, as detailed in the Methods section, including the specific links and versions. The results generated from this data are provided within the manuscript and its appendix. For further details or inquiries, interested parties may contact the corresponding author.

Acknowledgments

We would like to express our gratitude to Jörg Ganzhorn, Hamburg, Samuel Hoffmann, Hof, and to Anna Walentowitz, Bayreuth, who provided invaluable suggestions and advice throughout the development of this work. Their insights and guidance have significantly contributed to the quality and depth of this research. Also, we would like to acknowledge this work in memory of our late colleague, N. Ange Raharivololoniaina, whose passion and love for her homeland, Madagascar, inspired us to pursue this research that was initiated by her. Her dedication and spirit continue to motivate us.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A

Table A1. Ranked Contribution of Climate Variables to the First Five Principal Components and Corresponding Explained Variance. Variables are listed in descending order based on their contributions to PC1, and abbreviation definitions for the variables are provided below the table.
Table A1. Ranked Contribution of Climate Variables to the First Five Principal Components and Corresponding Explained Variance. Variables are listed in descending order based on their contributions to PC1, and abbreviation definitions for the variables are provided below the table.
VariablePC1PC2PC3PC4PC5
bio5c−0.212408977−0.079635162−0.063504340.201013123−0.007483132
bio5f−0.210254306−0.071025493−0.0815368840.217047379−0.01570473
bio18c0.201609974−0.0522842090.119946810.087286444−0.100895281
bio18f0.198566583−0.0678884750.1062670640.067548996−0.072145488
bio1f−0.196488305−0.154587877−0.0414079660.072229316−0.062983136
bio1c−0.193929176−0.160712604−0.0416313550.067036597−0.05284399
bio15f−0.1934000010.0773611430.168676488−0.082053318−0.117422531
bio11f−0.18936105−0.1737915210.0102841080.0296202230.007294249
bio11c−0.186057937−0.1798282640.0111678410.0253751490.012618865
bio10f−0.185912585−0.154480715−0.112709440.104912655−0.118266073
bio10c−0.184996175−0.154971955−0.1189844350.09887224−0.103548134
bio15c−0.1844964650.0737787250.196946986−0.095771028−0.139640471
bio8f−0.183946256−0.150462412−0.1314637830.095340176−0.122163124
bio8c−0.182323711−0.151549365−0.136964230.095194031−0.106107532
bio17f0.178692396−0.155990043−0.1302407630.1134361710.179811248
bio17c0.176864816−0.15848659−0.1340787910.1168547150.154160122
bio14f0.174843791−0.16063727−0.1306839750.1242312970.198951175
bio14c0.174592596−0.161752798−0.133829150.1265730920.164212052
bio12c0.172886334−0.1653224430.1226731730.181604127−0.008241253
bio12f0.168490858−0.1710061470.122490660.193289811−0.006233409
bio9f−0.168230569−0.204466822−0.0178264450.0406710920.030893864
bio19f0.167765966−0.168896003−0.1342212750.1209056890.180593997
bio9c−0.165643502−0.208187257−0.0143293360.032229330.042329753
bio19c0.16545317−0.170346398−0.1354976180.1255902540.182272039
bio3f−0.1607154950.0969820630.2251873370.0731933680.455977365
bio3c−0.1510578250.1094467560.2356327460.0636974280.451687578
bio6f−0.142357077−0.232627644−0.028344759−0.0875377960.035987047
bio6c−0.133420766−0.239768046−0.025702463−0.0978779660.041250052
bio2c−0.1248402190.2116117530.0573946370.3249585450.120374388
bio4f0.1215328490.155968509−0.2611558610.137722883−0.261987527
bio2f−0.1155916110.2188994410.051891410.3312402860.141015674
bio4c0.1070602970.161238986−0.2758318810.141649885−0.251496833
bio16c0.104973203−0.1354735980.3141759580.150294956−0.161476579
bio16f0.102428763−0.1465127030.3015855060.180587188−0.140939898
bio7c−0.0831824290.229490579−0.0443590.386647423−0.066057912
bio13c0.072482035−0.130279010.3497577620.113239291−0.196501446
bio13f0.064922425−0.1396583370.3428033480.155906057−0.163818143
bio7f−0.0548242250.241725708−0.0591424050.387291753−0.069841388
Explained Variance0.4843087640.2926025650.1406649110.0463326830.014126612
Cumulative
Variance		0.4843087640.77691130.91757620.96390890.9780355
Note: Variable definitions: bio1 = Annual Mean Temperature; bio2 = Mean Diurnal Range (Mean of monthly (max temp − min temp)); bio3 = Isothermality (bio2/bio7) (×100); bio4 = Temperature Seasonality (standard deviation ×100); bio5 = Max Temperature of Warmest Month; bio6 = Min Temperature of Coldest Month; bio7 = Temperature Annual Range (bio5-bio6); bio8 = Mean Temperature of Wettest Quarter; bio9 = Mean Temperature of Driest Quarter; bio10 = Mean Temperature of Warmest Quarter; bio11 = Mean Temperature of Coldest Quarter; bio12 = Annual Precipitation; bio13 = Precipitation of Wettest Month; bio14 = Precipitation of Driest Month; bio15 = Precipitation Seasonality (Coefficient of Variation); bio16 = Precipitation of Wettest Quarter; bio17 = Precipitation of Driest Quarter; bio18 = Precipitation of Warmest Quarter; bio19 = Precipitation of Coldest Quarter; f = future climate; and c = current climate.
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Figure A1. Madagascar Protected Areas. The legend and coloration of areas indicate different categories of protection status [38] (Modified according to UNEP-WCMC and IUCN (2024).
Figure A1. Madagascar Protected Areas. The legend and coloration of areas indicate different categories of protection status [38] (Modified according to UNEP-WCMC and IUCN (2024).
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Figure A2. Mean backward climate change velocity between 2070 and today per ecoregion in Madagascar under SSP 126 and SSP 585. Boxes are arranged in ascending order based on the extent of ecoregions, from smallest to largest. The statistical significance of differences between inside and outside protected areas is indicated by asterisks, where *** denotes p < 0.001.
Figure A2. Mean backward climate change velocity between 2070 and today per ecoregion in Madagascar under SSP 126 and SSP 585. Boxes are arranged in ascending order based on the extent of ecoregions, from smallest to largest. The statistical significance of differences between inside and outside protected areas is indicated by asterisks, where *** denotes p < 0.001.
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Figure A3. Mean backward climate change velocity inside and outside protected areas across ecoregions in Madagascar under SSP 126, and SSP 585. Boxes are arranged in ascending order based on the extent of ecoregions, from smallest to largest; box types with a solid outline and dashed outline indicate ‘’inside protected areas”, and “outside protected areas”, accordingly. The statistical significance of differences between inside and outside protected areas is indicated by asterisks, where ns denotes p > 0.05, and **** denotes p < 0.0001.
Figure A3. Mean backward climate change velocity inside and outside protected areas across ecoregions in Madagascar under SSP 126, and SSP 585. Boxes are arranged in ascending order based on the extent of ecoregions, from smallest to largest; box types with a solid outline and dashed outline indicate ‘’inside protected areas”, and “outside protected areas”, accordingly. The statistical significance of differences between inside and outside protected areas is indicated by asterisks, where ns denotes p > 0.05, and **** denotes p < 0.0001.
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Figure A4. Bivariable map of climate change velocity between today and 2070 in Madagascar under (a) SSP 126, (b) SSP 585.
Figure A4. Bivariable map of climate change velocity between today and 2070 in Madagascar under (a) SSP 126, (b) SSP 585.
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Figure 1. Mean climate change velocity in Madagascar comparing current and future (2070) conditions, derived from five GCMs (GFDL-ESM4, UKESML-01L, MPI-ESM1-2-HR, IPSL-CM6A-LR, and MRI-ESM2-0) under SSP 126 and SSP 585 scenarios. (a) backward velocity under SSP 126, (b) forward velocity under SSP 126, (c) backward velocity under SSP 585, (d) forward velocity under SSP 585.
Figure 1. Mean climate change velocity in Madagascar comparing current and future (2070) conditions, derived from five GCMs (GFDL-ESM4, UKESML-01L, MPI-ESM1-2-HR, IPSL-CM6A-LR, and MRI-ESM2-0) under SSP 126 and SSP 585 scenarios. (a) backward velocity under SSP 126, (b) forward velocity under SSP 126, (c) backward velocity under SSP 585, (d) forward velocity under SSP 585.
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Figure 2. Spatial distribution and proportion of Malagasy ecoregions linked to the protected area coverage per ecoregion. The left chart illustrates the percentage distribution of each ecoregion across Madagascar (outer ring) and the proportion of PAs within each ecoregion (inner ring). The central map depicts the spatial distribution of these ecoregions across the island. The bar graph shows the percentage of land area protected within each ecoregion, highlighting disparities in coverage. Protected areas that span multiple ecoregions are counted in each ecoregion they present.
Figure 2. Spatial distribution and proportion of Malagasy ecoregions linked to the protected area coverage per ecoregion. The left chart illustrates the percentage distribution of each ecoregion across Madagascar (outer ring) and the proportion of PAs within each ecoregion (inner ring). The central map depicts the spatial distribution of these ecoregions across the island. The bar graph shows the percentage of land area protected within each ecoregion, highlighting disparities in coverage. Protected areas that span multiple ecoregions are counted in each ecoregion they present.
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Figure 3. Mean forward climate change velocity per ecoregion in Madagascar under SSP 126 and SSP 585 Boxes are arranged in ascending order based on the extent of ecoregions, from smallest to largest. The statistical significance of differences between ecoregions within each scenario is indicated by asterisks, where * denotes p < 0.05, ** denotes p < 0.01, and *** denotes p < 0.001.
Figure 3. Mean forward climate change velocity per ecoregion in Madagascar under SSP 126 and SSP 585 Boxes are arranged in ascending order based on the extent of ecoregions, from smallest to largest. The statistical significance of differences between ecoregions within each scenario is indicated by asterisks, where * denotes p < 0.05, ** denotes p < 0.01, and *** denotes p < 0.001.
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Figure 4. Mean forward climate change velocity inside and outside protected areas across ecoregions in Madagascar under SSP 126 and SSP 585. Boxes are arranged in ascending order based on the extent of ecoregions, from smallest to largest; box types with a solid outline and dashed outline indicate ‘’inside protected areas”, and “outside protected areas”, accordingly. The statistical significance of differences between inside and outside protected areas is indicated by asterisks, where *** denotes p < 0.001, and **** denotes p < 0.0001.
Figure 4. Mean forward climate change velocity inside and outside protected areas across ecoregions in Madagascar under SSP 126 and SSP 585. Boxes are arranged in ascending order based on the extent of ecoregions, from smallest to largest; box types with a solid outline and dashed outline indicate ‘’inside protected areas”, and “outside protected areas”, accordingly. The statistical significance of differences between inside and outside protected areas is indicated by asterisks, where *** denotes p < 0.001, and **** denotes p < 0.0001.
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Figure 5. Relationship between protected area size and climate change velocity projections under SSP 585 for the ecoregions (a) Ericoid Thickets, (b) Mangroves, (c) Spiny Thickets, (d) Succulent Woodlands, (e) Lowland Forests, (f) Dry Deciduous Forests, (g) Subhumid Forests of Madagascar. Each point represents an individual PA. Dashed lines indicate the linear regression trends for each ecoregion. PAs that extend across multiple ecoregions are represented multiple times, once in each corresponding ecoregion. The size of each PA is calculated based on its total area, rather than the area confined to each specific ecoregion.
Figure 5. Relationship between protected area size and climate change velocity projections under SSP 585 for the ecoregions (a) Ericoid Thickets, (b) Mangroves, (c) Spiny Thickets, (d) Succulent Woodlands, (e) Lowland Forests, (f) Dry Deciduous Forests, (g) Subhumid Forests of Madagascar. Each point represents an individual PA. Dashed lines indicate the linear regression trends for each ecoregion. PAs that extend across multiple ecoregions are represented multiple times, once in each corresponding ecoregion. The size of each PA is calculated based on its total area, rather than the area confined to each specific ecoregion.
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Lai, Q.; Beierkuhnlein, C. The Vulnerability of Malagasy Protected Areas in the Face of Climate Change. Diversity 2024, 16, 661. https://doi.org/10.3390/d16110661

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Lai Q, Beierkuhnlein C. The Vulnerability of Malagasy Protected Areas in the Face of Climate Change. Diversity. 2024; 16(11):661. https://doi.org/10.3390/d16110661

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Lai, Qi, and Carl Beierkuhnlein. 2024. "The Vulnerability of Malagasy Protected Areas in the Face of Climate Change" Diversity 16, no. 11: 661. https://doi.org/10.3390/d16110661

APA Style

Lai, Q., & Beierkuhnlein, C. (2024). The Vulnerability of Malagasy Protected Areas in the Face of Climate Change. Diversity, 16(11), 661. https://doi.org/10.3390/d16110661

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