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Article

Predicting the Impact of Global Climate Change on the Geographic Distribution of Anemochoric Species in Protected Areas

by
Larissa Alves-de-Lima
1,
Douglas Fernandes Rodrigues Alves
1,
Diego Vinicius Anjos
2,
Fernando Anco Valdivia
3,4 and
Helena Maura Torezan-Silingardi
1,4,*
1
Instituto de Biologia, Universidade Federal de Uberlândia, Uberlândia 38405-315, MG, Brazil
2
Departamento de Ciências Biológicas, Universidade Regional do Cariri, Crato 63105-000, CE, Brazil
3
Museo de História Natural (MUSA), Universidad Nacional de San Augustin de Arequipa, Área de entomologia, Arequipá 0051-54, Peru
4
Faculdade de Filosofia, Departamento de Biologia, Ciências e Letras de Ribeirão Preto, Universidade de São Paulo, Ribeirão Preto 14040-901, SP, Brazil
*
Author to whom correspondence should be addressed.
Atmosphere 2025, 16(4), 453; https://doi.org/10.3390/atmos16040453
Submission received: 25 March 2025 / Revised: 1 April 2025 / Accepted: 3 April 2025 / Published: 14 April 2025
(This article belongs to the Special Issue Vegetation and Climate Relationships (3rd Edition))
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Versions Notes

Abstract

:
Protected areas are crucial sanctuaries for biodiversity, strictly prohibiting the direct exploitation of natural resources and helping to maintain viable populations and communities. However, even species within these areas face challenges from climate changes. This study compared the present distribution of five woody species (Aspidosperma tomentosum, Kielmeyera coriacea, Peixotoa tomentosa, Qualea multiflora, and Senna velutina) with their projected distribution (in 2080–2100) in 30 protected Brazilian national parks. Our objectives were to evaluate ecological niche models; determine which bioclimatic variables best explain the geographic distribution of species; assess the current distribution of these species; predict changes under distinct future climatic scenarios; and analyze the potential species richness within Brazilian national parks. We overlayed binarized maps of each species and extracted statistical metrics—mean potential, standard deviation, minimum, and maximum potential—using the ‘extract’ function (raster package, v.3.5-2) in the R platform. The results revealed the dynamic nature of species distribution, each one of them being affected by a specific group of factors. All species exhibited changes in their ecological niche or distribution areas in future projections, be it losing areas (A. tomentosum: 26.27–100%; K. coriacea: 38.39–100%; P. tomentosa: 40.46–96.66%; Q. multiflora: 7.34–100%; Senna velutina: 4.52–99.99%) or gaining areas (Q. multiflora: up to 92.21%, and S. velutina: up to 22.07%). We conclude that the potential species richness within Brazilian national parks will be lower in the future. This information is crucial for biodiversity conservation efforts, offering insights into species habitat dynamics and emphasizing the need for adaptive conservation strategies. This study reinforces the urgency of preserving natural ecosystems to ensure a sustainable future for their flora and fauna.

1. Introduction

Climate change is increasingly recognized as a threat to global biodiversity, impacting the structure and functionality of ecosystems [1,2]. Climate projections based on greenhouse gas emissions indicate that by the end of the century, global temperature can increase by up to 3.1 °C [3]. However, in the case of South America, the temperature rise is even more significant, potentially reaching up to 4 °C [4]. Especially for the Brazilian Savanna, known as the Cerrado, estimates suggest temperature increases ranging from 2° to 6 °C [5]. Additionally, precipitation is expected to decrease by up to 70% compared to current levels, along with changes in the distribution of rainfall throughout the year [5,6]. These changes will impact various species of animals [7,8,9], plants [10,11,12], and seaweeds [13,14] in diverse ways and different scales [15], potentially leading to the extinction of many species [9,16].
Climatic changes can have a significant impact on plants, affecting genetic diversity [17], biotic interactions [18], phenology [18,19,20], reproductive success [18], and the geographic distribution of the species [21,22,23]. Climatic forecasts indicate that global warming will likely result in a reduced geographic distribution for several terrestrial plant species due to physiological limitations [19,23,24]. However, some species also could migrate and expand their distribution, seeking out new areas with climate conditions that are better suited for their survival, maintenance, and reproduction [25].
In the case of the Cerrado, multiple models suggest that climate change may potentially threaten plant biodiversity, leading to a significant reduction in the abundance and diversity of species [5,9,23,26]. Changes in the ecosystem’s structure can disrupt the balance within communities, affecting ecosystem services such as pollination, nutrient cycling, hydrological cycle regulation, and carbon sequestration [5].
In addition to climate change, the processes of deforestation and the increasing frequency of wildfires elevate the risk of extinction for various plant populations [27,28,29,30]. The Brazilian Cerrado has already experienced deforestation in approximately 61% of its natural areas [31] and possesses legal protection covering only 2.20% of its native territories [28], many of these protected areas belong to conservation units such as the natural parks. Due to its intense deforestation and high species richness, the Cerrado is designated as a biodiversity hotspot [32], and conservation units or protected areas play a pivotal role in mitigating the impacts of deforestation on these populations [33].
The ecological niche refers to the combination of conditions and resources that a species may exploit. It can be further divided into two principal components: the fundamental niche, which encompasses the environmental conditions that enable a species to survive, and the realized niche, which considers biological interactions, such as competition with other species [34]. The ecological niche model (ENM) has proven to be a reliable tool to predict future scenarios; however, it is not a consensus [22,35]. The ENM is based on the fundamental niche, functioning with a duality of geographic and environmental space. It utilizes data from geographic occurrences and climatic variables’ values while considering the species’ current limitations, without addressing possible adaptations [36,37]. The ENM has been widely employed to assess the influence of climate change on the geographic distribution of species, guiding conservation planning efforts by identifying climatically suitable areas with potential extinction risks [38,39,40,41,42].
The objectives of this study were as follows: (i) to evaluate ecological niche models, (ii) to determine which bioclimatic variables best explains the geographic distribution of five woody plant species, (iii) to assess the current distribution of these species, (iv) predict changes under distinct future climatic scenarios, and (v) to analyze potential species richness within the protected areas from Brazilian national parks.

2. Materials and Methods

2.1. Species Data

The Cerrado biome is home to approximately 14,000 plant species [43], with about 30% of them exhibiting anemochory as their dispersal mechanism. Anemochory is the second most common dispersion syndrome in the Cerrado [44,45]. For our study, we selected five common anemochoric species, each one from a representative Cerrado family (Table 1): Aspidosperma tomentosum Mart. & Zucc., Kielmeyera coriacea Mart. & Zucc., Peixotoa tomentosa A. Juss., Qualea multiflora Mart., and Senna velutina (Vogel) H.S. Irwin & Barnebby. These five species were chosen as they are easily found in Cerrado areas, considering that such species potentially occur in most of the protected areas mentioned in this study under current climate conditions. We compiled records of the geographic distribution of the five species from two URL sources, both on 17 January 2023: Species Link (CRIA 2014, https://specieslink.net/) and Global Biodiversity Information Facility (GBIF, https://www.gbif.org/) as other similar study [23]. To enhance data quality, we conducted a complementary search for synonyms using Reflora—Flora do Brasil 2020 for all five species. This effort resulted in a total of 11,996 points, including 2547 for A. tomentosum, 3081 for K. coriacea, 911 for P. tomentosa, 4086 for Q. multiflora and 1371 for S. velutina. We filtered the species’ location records by removing duplicate points, points lacking geographic coordinates, and points with improbable occurrences (examples: points in oceanic or Antarctic regions). Subsequently, the points were adjusted to a resolution of 5 min, which is approximately 81 km2. These occurrence points exclusively represent the realized niche and do not account for the effective niche, as they only include locations where the species have been found.

2.2. Environmental Data

Bioclimatic variables were obtained from WorldClim version 2.0, with a 5-min resolution, and a total of 19 variables were initially considered for the study (Table S1). To mitigate multicollinearity among these variables, Pearson’s correlation was used to assess all possible pairwise correlations. Variables with a correlation equal to or greater than 75% (|r| ≥ 0.75) were identified as highly correlated and subjected to further filtering [46]. In cases of high correlation, only the variable deemed biologically significant for the five anemochorous species was retained. This approach ensured that the final set of variables accurately reflected the ecological needs of the species, minimizing redundancy and maintaining biological relevance. After the filtering process, nine variables were identified as applicable to all five species: (1) annual mean temperature, (2) mean diurnal range, (3) isothermality, (4) temperature seasonality, (5) annual precipitation, (6) precipitation of the driest month, (7) precipitation seasonality, (8) precipitation of the warmest quarter, and (9) precipitation of the coldest quarter (Table 2).

2.3. Model Evaluation Methods

To evaluate each model, we used the area under the curve (AUC) of the receiver operating characteristic (ROC), which compares the model’s adequacy values with the observed data [19,47]. We further confirmed the results using the true skill statistic (TSS). AUC values range from 0 to 1, where values up to 0.6 indicate that the model has no predictive power, while a value of 1 indicates a perfect model for predicting species distribution [48]. To interpret the AUC values, we employed the following scale: AUC > 0.9 = excellent; 0.89 > AUC < 0.80 = good; 0.79 > AUC < 0.70 = fair; 0.69 > AUC < 0.60 = poor; AUC < 0.60 = failure [49]. TSS values range from −1 to 1, where values below 0 indicate a random model, and values above 0 represent an acceptable model [50].

2.4. Methodology to Detect the Best Bioclimatic Variables to Explain the Future Geographic Distribution of Species

To elucidate geographic distribution, we used the percentage contribution of the variables, as provided by MaxEnt. We considered the set of variables with higher importance values, collectively accounting for at least 70% of the model’s influence.

2.5. Methodology to Describe the Current Distribution of the Species

The Ecological Niche Models (ENMs) were constructed using the Maximum Entropy Model (MaxEnt) incorporated within R 4.0. MaxEnt employs maximum entropy techniques, relying solely on presence points [49,51]. We used the ENMeval package to assess the model’s fit parameters [52]. We designed 10 replicates of the model and calculated the average of these models. To optimize the modeling process, we incorporated two parameters into the model to enhance its predictive power: feature class (fc) and a regularization multiplier (rm). Feature class represents a mathematical transformation of the various covariates used in model construction, allowing for the modeling of complex relationships [53,54]. The regularization multiplier is a parameter that introduces news constraints, primarily aimed at preventing excessive complexity and overfitting, thereby controlling the intensity of the chosen feature classes for the model [53,55]. The available resource classes included linear (L), product (P), quadratic (Q), and hinge (H), as well as their combinations LQ, LQH, LQHP, and LQHPT. The multiplier values ranged from 0.5 to 4 (0.5, 1, 1.5, 2, 2.5, 3, 3.5, and 4). For each model, we generated 10.000 background points. The best models were selected based on the lowest Akaike Information Criterion (AIC). We employed the geographical block method to access the performance of each model [46,47,52]. For future predictions (2080–2100), we tested three climate projections: BCC-CSM2-MR—Beijing Climate Center Climate System Model [56], CNRM-ESM2-1—Evaluation of CNRM Earth System Model [57], and MIROC6—Model for Interdisciplinary Research on Climate [58]. These were evaluated along with four greenhouse gas emission scenarios: RCP2.6 (490 ppmv), RCP4.5 (650 ppmv), RCP6.0 (850 ppmv), and RCP8.5 (1370 ppmv) [3]. MIROC6 represents a milder model in terms of greenhouse gas emissions, whereas BCC-CSM2-MR is moderate and CNRM-ESM2-1 is more extreme [59].

2.6. Methodology to Investigate the Possible Changes in the Future

To analyze the area held, gained, and lost, we performed the calculations using the area function of the ‘raster’ package [60]. For each species, we conducted 12 analyses, working with the tree-tested models for future predictions and considering the four greenhouse gas emission scenarios. The matrix data were transformed into binary values: 0 for absence and 1 for presence [61]. The raster clogging limit was determined based on the tenth percentile of training presence (TPTP). TPTP excludes all regions with habitat suitability values lower than the 10% threshold based on current occurrence records. TPTP has been extensively used [62,63,64] and demonstrated superior results when compared to other thresholds, such as minimum training presence (MTP) [65]. TPTP is considered a conservative method [66,67].

2.7. Methodology to Analise the Conservation Units with the Greatest Potential for Future Species Richness

To assess the potential richness of species, we selected the park category as a basis since they are strictly protected areas, as the Brazilian law does not allow the direct use of natural resources [64]. For the analysis, we overlaid the binarized maps of the five species and extracted the mean potential, standard deviation, and minimum and maximum potential using the extract function from the raster package [60]. We focused on 30 parks located within the smallest polygon area, and we conducted 13 analyses: present, BCC-CSM2 RCP2.6, BCC-CSM2 RCP4.5, BCC-CSM2 RCP6.0, BCC-CSM2 RCP8.5, CNRM-ESM2-1 RCP2.6, CNRM-ESM2-1 RCP4.5, CNRM-ESM2-1 RCP6.0, CNRM-ESM2-1 RCP8.5, MIROC6 RCP2.6, MIROC6 RCP4.5, MIROC6-370, and MIROC6 RCP8.5 (Table 3).

3. Results

3.1. Model Evaluation Results

The model was considered excellent to P. tomentosa (AUC = 0.906 ± 0.004) and considered good to A. tomentosum (AUC = 0.857 ± 0.001), K. coriacea (AUC = 0.864 ± 0.002), Q. multiflora (AUC = 0.883 ± 0.001), and S. velutina (AUC = 0.805 ± 0.006). The true skill statistic values (TSSs) confirm that the models are acceptable (Table 4).

3.2. The Best Bioclimatic Variables to Explain the Future Geographic Distribution of Species Results

The most important bioclimatic variables that explained the geographic distribution varied among species (Table 5). For example, 80.70% of A. tomentosum’s distribution was explained by the precipitation of the coldest quarter (41.46%), precipitation of the warmest quarter (16.16%), annual precipitation (11.85%), and temperature seasonality (11.23%). For K. coriacea, 75.01% of its distribution was explained by the precipitation of the coldest quarter (26.87%), precipitation of the warmest quarter (19.04%), annual precipitation (16.15%), and temperature seasonality (12.95%). Meanwhile, 79.95% of P. tomentosa’s distribution was explained by the annual mean temperature (61.78%), the precipitation of the warmest quarter (10.23%), and precipitation seasonality (7.94%). Approximately 77.34% of Q. multiflora’s distribution was explained by the precipitation of the warmest quarter (31.00%), annual precipitation (20.00%), annual mean temperature (17.78%), and temperature seasonality (8.56). In contrast, 78.06% of S. velutina’s distribution was explained by the precipitation of the coldest quarter (47.90%), temperature seasonality (17.30%), and precipitation of the warmest quarter (12.86%).

3.3. The Current Distribution of the Species Results

The five species were distributed across a total of 2061 location points, with 497 belonging to A. tomentosum, 549 to K. coriacea, 108 to P. tomentosa, 606 to Q. multiflora, and 301 to S. velutina (Table 4). Our study identified areas considered climatically suitable for the survival of A. tomentosum (Figure 1a) and K. coriacea (Figure 1b). These areas were similar, spanning latitudes between −30° and −0° and longitudes between −70° and −35°, with a total extent of 3,288,314 hectares (ha) for A. tomentosum and 3,421,897 ha for K. coriacea (Table 6). Peixotoa tomentosa was located within the latitude range of −25° and −5°, and longitude range of −60° and −40°, covering an area of 1,621,865 ha (Figure 1c, Table 6). The habitat of Qualea multiflora spanned latitudes between −25° and −10° and longitudes between −75° and −40°, occupying 2,933,077 ha (Figure 1d, Table 6). Lastly, S. velutina was found within a latitude range from −25° to −0° and a longitude range from −65° to −25°, encompassing a total area of 3,281,471 ha (Figure 1e, Table 6).

3.4. Predictions About the Possible Changes in the Future

All five species exhibited changes in their distribution areas in the future projections (Table 6). Aspidosperma tomentosum had a loss ranging from 26.27% to 100% of its current distribution area, with a maximum gain of 2.50% (Figure S1). Kielmeyera coriacea experienced a loss ranging from 38.39% to 100% and a maximum gain of 1.61% (Figure S2). Peixotoa tomentosa’s distribution decreased by 40.46% to 96.66%, with the most significant gain being 0.16% (Figure 2). Qualea multiflora showed losses ranging from 7.34% to 100% and a substantial gain of up to 92.21% (Figure 3). Senna velutina exhibited a loss ranging from 4.52% to 99.99% of its area, with the most significant gain being 22.07% (Figure S3).

3.5. Results About the Conservation Units with the Greatest Potential for Future Species Richness

In the current scenario, and considering the spatial projection of ecological niche models, the protected areas inside parks with the greatest potential for conservation of the five species are Parque Nacional da Chapada dos Veadeiros, Parque Nacional da Serra da Canastra, Parque Nacional da Serra do Cipó, Parque Nacional do Gandarela, Parque Nacional das Emas, Parque Nacional das Sempre-Vivas, Parque Nacional de Brasília, Parque Nacional de Caparaó, and Parque Nacional de Itatiaia (Table 7). When considering future projections, the five parks with the greatest potential for species richness are Parque Nacional do Gandarela and Parque Nacional da Serra do Cipó, both with a potential richness score of five in all projections except CNRM-ESM2-1-585. They are followed by Parque Nacional da Serra da Canastra, Parque Nacional das Sempre-Vivas, and Parque Nacional de Caparaó.

4. Discussion

Our study indicates that the climatically suitable areas for maintaining the populations of the five anemochoric species, which will be investigated by us in the future (2080–2100), will be located specifically at protected areas inside the Brazilian conservation units, in Parque Nacional da Chapada dos Veadeiros, Parque Nacional da Serra da Canastra, Parque Nacional da Serra do Cipó, Parque Nacional do Gandarela, Parque Nacional das Emas, Parque Nacional das Sempre-Vivas, Parque Nacional de Brasília, Parque Nacional de Caparaó, and Parque Nacional de Itatiaia. These areas are primarily within the Cerrado regions; however, they also extend into the Atlantic Forest, Amazon Forest, and the Caatinga, which is a unique vegetation type found in the northeastern part of Brazil. The Caatinga is characterized by shallow and stony soils, low trees, crooked trunks, and thorny and deciduous leaves during the dry season. Preserving these areas is of extreme importance for maintaining the populations of the observed species in the future. Notably, both the Cerrado and the Atlantic Forest are considered Brazilian ecosystems recognized as biodiversity hotspots worldwide [32]. These ecosystems are known for their rich biological diversity, which is threatened by species extinction and extensive habitat destruction driven by human activities [32]. The Amazon Forest, in particular, has gained international attention due to the exponential increase in deforestation [68], primarily fueled by agricultural expansion [69] and timber exploitation [70]. Preserving these areas is not only vital for conserving these ecosystems but also for safeguarding other species, including many yet to be described [19,23,71]. By protecting species from different families that play a crucial role in the Cerrado, we are also conserving phylogenetic and functional diversity, along with essential ecosystem services. These services include provisioning services that provide resources used by humans, such as water, nutrient cycling, pollination, and carbon sequestration [72].
Aspidosperma tomentosum, K. coriacea, and P. tomentosa experienced significant losses in their distribution areas across all analyzed scenarios, losing up to 100%, 100%, and 96.66%, respectively. Senna velutina also faced substantial losses, with up to 99.99% of its area being affected; however, it could potentially gain up to 22.07% of its area. Plants around the world will be affected by climate change in different regions [9]. Anthropogenic climate change is widely recognized as a major threat to biodiversity and has the potential to lead to the extinction of thousands of species over the next century [9]. Species extinction can be attributed to various factors, including physiological tolerance issues such as intolerance to high temperatures [73]. In the best-case scenario, it is estimated that 38–45% of woody plant species in the Brazilian Cerrado will cease to exist due to these climate changes. In the worst-case scenario, the estimate is as high as 56% of species [9].
Qualea multiflora generally expands its range; however, when it experiences losses, it becomes vulnerable, potentially facing extinction with a 100% reduction in its current distribution area. Notably, Q. multiflora has expanded its range into the Amazon Forest, where there was previously no record of its occurrence [43]. The establishment of a species in a new environment depends on abiotic factors, such as the variables incorporated in the model, as well as biotic factors, including competition [74]. Furthermore, new factors need to be considered, such as anthropogenic activities in the Amazon region, which have resulted in significant deforestation [75]. However, it is worth noting that Q. multi-flora previously existed in the Amazon region but later migrated to central Brazil, gaining areas further south in response to climatic fluctuations at the time [76]. We recommend further investigation into this expansion area of Q. multiflora.
The most relevant bioclimatic variable for A. tomentosum, K. coriacea, and S. velutina distribution was the precipitation of the coldest quarter (Bio 19). During the colder season, precipitation is lower, resulting in a negative relationship between fruiting and precipitation, as these three species exhibit fruit ripening between July and September [44,77]. For P. tomentosa, the most relevant bioclimatic variable was the annual mean temperature (Bio 1). Temperature is positively correlated with the flowering and fruiting of P. tomentosa [77]. For Qualea multiflora, the precipitation of the warmest quarter (Bio 18) was the most relevant variable. Precipitation during the warmest quarter corresponds to summer in the southern hemisphere, coinciding with the flowering period of Q. multiflora, which occurs between November and January [77]. The reproductive phenophase is crucial for maintaining species populations, as flowering marks the initial stage of seed generation, leading to successful seed dispersal and the establishment of new individuals [78]. Following fruiting, seed dispersal further supports population maintenance, especially during the dry season when wind intensities are higher [79].
The conservation units with the greatest potential for species development in the future are Parque Nacional do Gandarela and Parque Nacional da Serra do Cipó, followed by Parque Nacional da Serra da Canastra, Parque Nacional das Sempre-Vivas, and Parque Nacional do Caparaó. Notably, the former two parks encompass a diverse and endangered Brazilian ecosystem [80]. It is worth noting that, in general, most Brazilian parks face some form of threat. When considering the best-case scenarios, the analyzed Brazilian parks have the climatic potential to support the studied populations. However, as these scenarios worsen, we observe a concerning decline in this capacity, which poses a probable high risk of extinction. Future studies on the effects of climate change are of great importance for the main plant families and their most abundant species, particularly for species that can sustain animals as pollinators or fruit dispersers.
However, it is valid to consider the influence of human activity in future projections in addition to natural climate-forming processes. Human activities are conditioned by the forecast of the development of the world economy, especially energy, and they may be used as another crucial initial condition in the modeling process as they induce changes in the chemical composition of the atmosphere. For example, atmosphere modification includes the process of sunlight reaching the surface of the planet and the greenhouse effects. Knowledge of natural climate-forming processes and the future development of the world are the two main factors able to determine climate change. We emphasize the need for further studies focusing on these distinct factors capable of altering climatic conditions and, consequently, the distribution of species.

5. Conclusions

This study reveals significant insights into the future distribution of five important woody species within Brazilian national parks under changing climate scenarios, as it highlights that climate change will likely lead to substantial shifts in ecological niches and the distribution of these species. All species are projected to experience changes, with some, such as Aspidosperma tomentosum, Kielmeyera coriacea, and Peixotoa tomentosa, facing severe distribution losses, potentially up to 100%. Others, like Qualea multiflora and Senna velutina, may expand their ranges, though these gains are limited and often accompanied by losses elsewhere.
This analysis emphasizes the importance of Brazilian protected areas for future species conservation, particularly those in the Cerrado, Atlantic Forest, and Amazon regions. Our results underscore that these protected areas, such as Parque Nacional da Chapada dos Veadeiros and Parque Nacional da Serra do Cipó, will be crucial refuges for maintaining biodiversity. However, the potential species richness in these parks is expected to decline, which highlights the urgency for adaptive management strategies to preserve both species and ecosystems in the face of climate change.
Additionally, our study identifies critical bioclimatic variables, such as temperature and precipitation patterns, which play a central role in the distribution of species. These insights provide a foundation for further investigations into species’ phenological patterns and how they may adapt to future climate conditions. The influence of human activities, including deforestation and land-use change, also warrants further exploration, as they may exacerbate the impacts of climate change.
Ultimately, our findings call for the preservation of the identified conservation units and the need for ongoing research to monitor and mitigate the effects of climate change on biodiversity. This study reinforces the role of protected areas as essential tools for conserving natural ecosystems and highlights the importance of using proactive conservation strategies to safeguard biodiversity for future generations.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/atmos16040453/s1, Table S1. Bioclimatic variables analyzed; Figure S1: Predicted change in Aspidosperma tomentosum Mart. & Zucc. distribution under future climate scenarios; Figure S2: Predicted change in Kilmeyera coriaceae Mart. & Zucc. distribution under future climate scenarios; Table S1: Average species richness potential of conversation units. considering the present and 12 future projections.

Author Contributions

Conceptualization, L.A.-d.-L. and D.F.R.A.; methodology L.A.-d.-L., D.F.R.A. and F.A.V.; software D.F.R.A.; validation D.F.R.A.; formal analysis, D.F.R.A.; investigation L.A.-d.-L., D.F.R.A. and F.A.V.; data curation L.A.-d.-L., D.F.R.A., D.V.A. and H.M.T.-S.; writing—original draft preparation L.A.-d.-L. and D.V.A.; writing—review and editing L.A.-d.-L., D.F.R.A. and H.M.T.-S.; supervision H.M.T.-S. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Fundação de Amparo à Pesquisa do Estado de Minas Gerais—FAPEMIG Financial code 11589 (LAL); Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brazil (CAPES)—Finance code 001 (LAL); and Conselho Nacional de Desenvolvimento Científico e Tecnológico—Brazil (CNPQ)—Finance code 403647/2021-5 (HMTS).

Data Availability Statement

No new data were created. We used data on the geographic distribution of the species from 17 January 2023, compiled from the collection of the online websites Species Link (CRIA 2014, https://specieslink.net/) and Global Biodiversity Information Facility (GBIF, https://www.gbif.org/).

Acknowledgments

Special thanks to an introductory niche modeling course for getting us started on this project and to two anonymous reviewers for their significant improvements in the text.

Conflicts of Interest

The authors declare no conflicts of interest.

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Atmosphere 16 00453 g001
Figure 1. Environmental suitability maps for each species varied from no suitability (represented in blue) to very good suitability (in red). Each image represents a single species: (a) Aspidosperma tomentosum, (b) Kielmeyera coriacea, (c) Peixotoa tomentosa, (d) Qualea multiflora, and (e) Senna velutina at present. The blue dots represent the selected points for each model.
Figure 1. Environmental suitability maps for each species varied from no suitability (represented in blue) to very good suitability (in red). Each image represents a single species: (a) Aspidosperma tomentosum, (b) Kielmeyera coriacea, (c) Peixotoa tomentosa, (d) Qualea multiflora, and (e) Senna velutina at present. The blue dots represent the selected points for each model.
Atmosphere 16 00453 g001
Atmosphere 16 00453 g002
Figure 2. Predicted new distribution of Peixotoa tomentosa under future climate scenarios. Peixotoa tomentosa is expected to maintain its individuals in the yellow areas, but to lose in the red areas. This species is not expected to expand its distribution area.
Figure 2. Predicted new distribution of Peixotoa tomentosa under future climate scenarios. Peixotoa tomentosa is expected to maintain its individuals in the yellow areas, but to lose in the red areas. This species is not expected to expand its distribution area.
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Atmosphere 16 00453 g003
Figure 3. Predicted new distribution of Qualea multiflora under future climate scenarios. Qualea multiflora is expected to maintain its individuals in the yellow areas, but to lose in the red areas and to expand its distribution in the blue areas.
Figure 3. Predicted new distribution of Qualea multiflora under future climate scenarios. Qualea multiflora is expected to maintain its individuals in the yellow areas, but to lose in the red areas and to expand its distribution in the blue areas.
Atmosphere 16 00453 g003
Table 1. Characterization of the studied species lifestyle, fruit type, and restriction area.
Table 1. Characterization of the studied species lifestyle, fruit type, and restriction area.
SpeciesFamilyLifestyleFruit TypeEndemic to Brazil
Aspidosperma tomentosumApocynaceae Juss.TreeDry follicleNo
Kielmeyera coriaceaCalophyllaceae J. AgardhShrub, TreeSepticidal capsuleNo
Peixotoa tomentosaMalpighiaceae Juss.ShrubSamaroidYes
Qualea multifloraVochysiaceae A. St.-HilShrub, TreeLoculicidal capsuleNo
Senna velutinaFabaceae Lindl.ShrubSamaroidNo
Table 2. Nine bioclimatic variables included in the models.
Table 2. Nine bioclimatic variables included in the models.
WorldClim Code Bioclimatic Variables
BIO1Annual mean temperature
BIO2Mean diurnal range [mean of monthly (max temp − min temp)]
BIO3Isothermality
BIO4Temperature seasonality (standard deviation × 100)
BIO12Annual precipitation
BIO14Precipitation of driest month
BIO15Precipitation seasonality (coefficient of variation)
BIO18Precipitation of warmest quarter
BIO19Precipitation of coldest quarter
Table 3. Conservation units (protected areas inside National Parks) used, Brazilian states of localization, biome, and area (hectares).
Table 3. Conservation units (protected areas inside National Parks) used, Brazilian states of localization, biome, and area (hectares).
Conservation UnitsStatesBiomeSize (ha)
Parque Nacional Cavernas do PeruaçuMGCerrado56,449,004
Parque Nacional da Chapada da DiamantinaBACaatinga152,143,914
Parque Nacional da Chapada das MesasMACerrado159,953,781
Parque Nacional da Chapada dos GuimarãesMTCerrado32,646,832
Parque Nacional da Chapada dos VeadeirosGOCerrado240,586,563
Parque Nacional da Serra da BocainaRJ/SPAtlantic Forest106,566,425
Parque Nacional da Serra da BodoquenaMSCerrado76,973,530
Parque Nacional da Serra da CanastraMGCerrado197,971,961
Parque Nacional da Serra da CapivaraPICaatinga100,764,194
Parque Nacional da Serra das ConfusõesPICerrado823,854,539
Parque Nacional da Serra do CipóMGCerrado31,639,535
Parque Nacional da Serra do GandarelaMGAtlantic Forest31,270,826
Parque Nacional da Serra do PardoPAAmazonic Forest445,413,447
Parque Nacional da Serra dos OrgãosRJAtlantic Forest20,020,746
Parque Nacional das EmasMS/GOCerrado132,787,860
Parque Nacional das Nascentes do Rio ParnaíbaMA/PI/BACerrado749,774,175
Parque Nacional das Sempre-VivasMGCerrado124,155,899
Parque Nacional de Boa NovaBAAtlantic Forest12,065,470
Parque Nacional de BrasíliaDFCerrado42,355,542
Parque Nacional de CaparaóES/MGAtlantic Forest31,763,291
Parque Nacional de ItatiaiaMG/RJAtlantic Forest28,086,350
Parque Nacional do AcariAMAmazônia896,410,954
Parque Nacional do AraguaiaTOCerrado555,524,438
Parque Nacional do Boqueirão Da OnçaBACaatinga346,908,103
Parque Nacional do JamanximPAAmazonic Forest862,895,273
Parque Nacional do JuruenaAM/MTAmazonic Forest1,958,014,417
Parque Nacional do Pantanal Mato-GrossenseMS/MTPantanal135,922,646
Parque Nacional do Rio NovoPAAmazonic Forest538,157,147
Parque Nacional dos Campos FerruginososPAAmazonic Forest79,086,038
Parque Nacional Grande Sertão VeredasBA/MGCerrado230,856,143
Table 4. Ecological niche models of five Cerrado plant species. Occurrence records (N) used to perform the model parameters (feature: L: linear, Q: quadratic, H: hinge, Rm: regression models, AUC: area under the curve, TSS: true skill statistic, SD: standard deviation, P: product and their combinations; rm (0.5, 1, 1.5, 2, 2.5, 3, 3.5, and 4), and model performance with AUC train, AUC test average, AUC test variation, TSS, and logistic threshold (tenth percentile training presence—TPTP).
Table 4. Ecological niche models of five Cerrado plant species. Occurrence records (N) used to perform the model parameters (feature: L: linear, Q: quadratic, H: hinge, Rm: regression models, AUC: area under the curve, TSS: true skill statistic, SD: standard deviation, P: product and their combinations; rm (0.5, 1, 1.5, 2, 2.5, 3, 3.5, and 4), and model performance with AUC train, AUC test average, AUC test variation, TSS, and logistic threshold (tenth percentile training presence—TPTP).
SpeciesNFeatureRmAUCTSSLogistic Threshold
MeanSDMeanSDMeanSD
Aspidosperma tomentosum497LQ10.8570.0010.5380.0200.3610.018
Kielmeyera coriácea549LQ10.8640.0020.5010.0120.3290.022
Peixotoa tomentosa108LQ10.9060.0040.4710.0690.0710.023
Qualea multiflora606LQH10.8830.0010.5680.0090.2950.021
Senna velutina301LQ10.8050.0060.4420.0180.3750.021
Table 5. Contribution of bioclimatic variables to the distribution of each species. Code refers to the bioclimatic variables considered in the statistical analysis.
Table 5. Contribution of bioclimatic variables to the distribution of each species. Code refers to the bioclimatic variables considered in the statistical analysis.
SpeciesCodeBioclimatic VariablesContribution (%)
MeanSD
Aspidosperma tomentosumBio19Precipitation of coldest quarter 41.460.167
Bio18Precipitation of warmest quarter16.160.140
Bio12Annual precipitation11.850.290
Bio4Temperature seasonality11.230.069
Bio1Annual mean temperature7.230.117
Bio2Mean diurnal range6.900.084
Bio3Isothermality4.870.120
Bio15Precipitation seasonality0.230.021
Bio14Precipitation of driest month0.070.007
Kielmeyera coriaceaBio19Precipitation of coldest quarter 26.870.390
Bio18Precipitation of warmest quarter19.040.127
Bio12Annual precipitation16.150.254
Bio4Temperature seasonality12.950.183
Bio1Annual mean temperature9.560.267
Bio3Isothermality6.450.135
Bio2Mean diurnal range5.750.129
Bio15Precipitation seasonality3.130.125
Bio14Precipitation of driest month0.10.004
Peixotoa tomentosaBio1Annual mean temperature61.780.284
Bio18Precipitation of warmest quarter10.230.267
Bio15Precipitation seasonality7.940.428
Bio14Precipitation of driest month6.780.230
Bio19Precipitation of coldest quarter5.610.089
Bio3Isothermality2.460.087
Bio4Temperature seasonality2.780.112
Bio12Annual precipitation1.880.091
Bio2Mean diurnal range0.490.060
Qualea multufloraBio18Precipitation of warmest quarter31.005.880
Bio12Annual precipitation20.000.248
Bio1Annual mean temperature17.780.305
Bio4Temperature seasonality 8.560.224
Bio3Isothermality7.120.339
Bio19Precipitation of coldest quarter5.860.397
Bio14Precipitation of driest month5.820.269
Bio15Precipitation seasonality1.950.100
Bio2Mean diurnal range1.910.059
Senna velutinaBio19Precipitation of coldest quarter47.910.547
Bio4Temperature seasonality17.300.205
Bio18Precipitation of warmest quarter12.860.399
Bio12Annual precipitation7.460.272
Bio14Precipitation of driest month7.030.265
Bio3Isothermality4.370.212
Bio2Mean diurnal range1.930.264
Bio15Precipitation seasonality1.010.064
Bio1Annual mean temperature0.120.032
Table 6. Predicted change in the distribution of Aspidosperma tomentosum, Kielmeyera coriacea, Peixotoa tomentosa, Qualea multiflora, and Senna velutina, considering the BCC-CSM2-MR (Beijing Climate Center Climate System Model), CNRM-ESM2-1 (Evaluation of CNRM Earth System Model), and MIROC6 (Model for Interdisciplinary Research on Climate and greenhouse gas release scenarios) projections and considering RCP2.6, RCP4.5, RCP6.0, and RCP8.5 in 2080–2010.
Table 6. Predicted change in the distribution of Aspidosperma tomentosum, Kielmeyera coriacea, Peixotoa tomentosa, Qualea multiflora, and Senna velutina, considering the BCC-CSM2-MR (Beijing Climate Center Climate System Model), CNRM-ESM2-1 (Evaluation of CNRM Earth System Model), and MIROC6 (Model for Interdisciplinary Research on Climate and greenhouse gas release scenarios) projections and considering RCP2.6, RCP4.5, RCP6.0, and RCP8.5 in 2080–2010.
SpeciesProjectionArea in the Presente (ha)Area in the Future (ha)Loss (ha)Gain (ha)Area Maintained in the Furute (ha)Lost + Kept (ha)Maintained (%)Loss (%)Gain (%)
Aspidosperma tomentosumBCC CSM2 MR RCP2.63,288,3142,506,820863,86582,3712,424,4493,288,31473.72926.2712.505
BCC CSM2 MR RCP4.53,288,3141,742,6971,583,90038,2831,704,4143,288,31451.83248.1681.164
BCC CSM2 MR RCP6.03,288,3141,031,5932,277,41820,6971,010,8963,288,31430.74269.2580.629
BCC CSM2 MR RCP8.53,288,3141,116,7822,188,52416,9921,099,7903,288,31433.44566.5550.517
CNRM ESM2 1 RCP2.63,288,3142,112,9201,183,16077662,105,1543,288,31464.01935.9810.236
CNRM ESM2 1 RCP4.53,288,3141,846,5041,451,22494131,837,0903,288,31455.86744.1330.286
CNRM ESM2 1 RCP6.03,288,3141,027,3612,277,73916,7861,010,5753,288,31430.73269.2680.510
CNRM ESM2 1 RCP8.53,288,31403,288,314003,288,3140.000100.0000.000
MIROC6 RCP2.63,288,3142,101,9601,204,04617,6922,084,2683,288,31463.38436.6160.538
MIROC6 RCP4.53,288,3141,698,9581,595,59362371,692,7213,28831451.47748.5230.190
MIROC6 RCP6.03,288,3141,333,2481,962,02569591,326,2893,28831440.33359.6670.212
MIROC6 RCP8.53,288,314939,0402,364,79015,516923,5243,28831428.08571.9150.472
Kielmeyera coriaceaBCC CSM2 MR RCP2.63,421,8972,163,3491,313,79755,2492,108,1003,421,89761.60638.3941.615
BCC CSM2 MR RCP4.53,421,8971,223,3862,251,57253,0611,170,3253,421,89734.20165.7991.551
BCC CSM2 MR RCP6.03,421,897621,0122,839,65138,766582,2473,421,89717.01582.9851.133
BCC CSM2 MR RCP8.53,421,897604,8092,862,59145,502559,3073,421,89716.34583.6551.330
CNRM ESM2 1 RCP2.63,421,8971,642,6571,797,44618,2061,624,4513,421,89747.47252.5280.532
CNRM ESM2 1 RCP4.53,421,897895,8512,548,42722,380873,4703,421,89725.52674.4740.654
CNRM ESM2 1 RCP6.03,421,897292,2783,154,46024,841267,4373,421,8977.81592.1850.726
CNRM ESM2 1 RCP8.53,421,89703,421,897003,421,8970.000100.0000.000
MIROC6 RCP2.63,421,8972,108,7521,327,68614,5402,094,2123,421,89761.20038.8000.425
MIROC6 RCP4.53,421,8971,409,0192,029,36316,4841,392,5353,421,89740.69559.3050.482
MIROC6 RCP6.03,421,897974,4812,468,15720,741953,7403,421,89727.87272.1280.606
MIROC6 RCP8.53,421,897648,3242,806,41132,837615,4863,421,89717.98782.0130.960
Peixotoa tomentosaBCC CSM2 MR RCP2.61,621,865807,214816,2441593805,6221,621,86549.67350.3270.098
BCC CSM2 MR RCP4.51,621,865526,7931,097,8002728524,0661,621,86532.31367.6870.168
BCC CSM2 MR RCP6.01,621,865350,5161,271,3490350,5161,621,86521.61278.3880.000
BCC CSM2 MR RCP8.51,621,865254,0361,367,91585253,9511,621,86515.65884.3420.005
CNRM ESM2 1 RCP2.61,621,865604,0881,017,7770604,0881,621,86537.24662.7540.000
CNRM ESM2 1 RCP4.51,621,865381,1301,240,7350381,1301,621,86523.50076.5000.000
CNRM ESM2 1 RCP6.01,621,865166,8311,455,0340166,8311,621,86510.28689.7140.000
CNRM ESM2 1 RCP8.51,621,86554,1521,567,713054,1521,621,8653.33996.6610.000
MIROC6 RCP2.61,621,865966,140656,216491965,6491,621,86559.53940.4610.030
MIROC6 RCP4.51,621,865590,8591,031,0060590,8591,621,86536.43163.5690.000
MIROC6 RCP6.01,621,865357,4941,264,3710357,4941,621,86522.04277.9580.000
MIROC6 RCP8.51,621,865287,0421,334,8230287,0421,621,86517.69882.3020.000
Qualea multifloraBCC CSM2 MR RCP2.62,933,0773,297,260739,9841,104,1662,193,0932,933,07774.77125.22937.645
BCC CSM2 MR RCP4.52,933,0774,093,110698,6921,858,7252,234,3852,933,07776.17923.82163.371
BCC CSM2 MR RCP6.02,933,0774,140,277400,5961,607,7952,532,4812,933,07786.34213.65854.816
BCC CSM2 MR RCP8.52,933,0774,580,950215,3001,863,1732,717,7772,933,07792.6607.34063.523
CNRM ESM2 1 RCP2.62,933,0774,108,590712,2331,887,7462,220,8442,933,07775.71724.28364.361
CNRM ESM2 1 RCP4.52,933,0774,968,018491,7792,526,7202,441,2982,933,07783.23316.76786.146
CNRM ESM2 1 RCP6.02,933,0775,276,745361,0752,704,7432,572,0022,933,07787.69012.31092.215
CNRM ESM2 1 RCP8.52,933,07702,933,077002,933,0770.000100.0000.000
MIROC6 RCP2.62,933,0773,336,496769,3481,172,7672,163,7292,933,07773.77026.23039.984
MIROC6 RCP4.52,933,0774,028,839729,9241,825,6862,203,1532,933,07775.11424.88662.245
MIROC6 RCP6.02,933,0774,695,596640,5472,403,0662,292,5312,933,07778.16121.83981.930
MIROC6 RCP8.52,933,0774,821,525531,0042,419,4522,402,0732,933,07781.89618.10482.489
Senna velutinaBCC CSM2 MR RCP2.63,281,4713,355,999180,081254,6093,101,3903,281,47194.5125.4887.759
BCC CSM2 MR RCP4.53,281,4713,351,891264,772335,1913,016,6993,281,47191.9318.06910.215
BCC CSM2 MR RCP6.03,281,4713,378,962377,371474,8612,904,1003,281,47188.50011.50014.471
BCC CSM2 MR RCP8.53,281,4713,688,871317,028724,4292,964,4433,281,47190.3399.66122.076
CNRM ESM2 1 RCP2.63,281,4713,120,622249,38788,5383,032,0853,281,47192.4007.6002.698
CNRM ESM2 1 RCP4.53,281,4712,981,696417,034117,2582,864,4383,281,47187.29112.7093.573
CNRM ESM2 1 RCP6.03,281,4712,506,217892,436117,1812,389,0363,281,47172.80427.1963.571
CNRM ESM2 1 RCP8.53,281,471823,281,3890823,281,4710.00399.9970.000
MIROC6 RCP2.63,281,4713,321,539148,366188,4333,133,1063,281,47195.4794.5215.742
MIROC6 RCP4.53,281,4712,809,890517,82746,2452,763,6443,281,47184.22015.7801.409
MIROC6 RCP6.03,281,4712,702,376637,16158,0662,644,3103,281,47180.58319.4171.770
MIROC6 RCP8.53,281,4712,329,345967,57215,4452,313,9003,281,47170.51429.4860.471
Table 7. Average species richness potential of conversation units (protected areas), considering the present and 12 future projections. Each Parque Nacional is indicated initially as ‘PN’.
Table 7. Average species richness potential of conversation units (protected areas), considering the present and 12 future projections. Each Parque Nacional is indicated initially as ‘PN’.
Conservation UnitsPresentBCC-CSM2 RCP2.6BCC-CSM2 RCP4.5BCC-CSM2 RCP6.0BCC-CSM2 RCP8.5CNRM-ESM2-1 RCP2.6CNRM-ESM2-1 RCP4.5CNRM-ESM2-1 RCP6.0CNRM-ESM2-1 RCP8.5MIROC6 RCP2.6MIROC6 RCP4.5MIROC6 RCP6.0MIROC6 RCP8.5
PN Cavernas do Peruaçu3.7142.8572.7140.8571.8572.7142.2861.2860.0003.0002.8572.2861.857
PN da Chapada da Diamantina4.4213.8953.3161.5261.3163.5262.3680.8420.0004.8423.7892.1052.474
PN da Chapada das Mesas1.1900.0000.8570.2380.1900.7621.1431.0000.0000.5711.0480.8100.952
PN da Chapada dos Guimarães4.8004.0002.6002.0002.0004.0003.4002.4000.0004.4003.4001.6002.000
PN da Chapada dos Veadeiros5.0004.7334.2673.5673.4674.4674.0003.2670.0004.9004.3004.0003.800
PN da Serra da Bocaina4.0834.3334.3334.1674.2503.8333.3333.0830.6674.1673.5003.3333.000
PN da Serra da Bodoquena2.8892.6670.8891.2222.0000.1110.0000.4440.0001.0000.0000.0000.111
PN da Serra da Canastra5.0005.0005.0004.5224.5655.0004.9134.7830.0005.0005.0005.0005.000
PN da Serra da Capivara0.5830.5000.2500.0000.0000.9170.9170.2500.0001.0000.5000.1670.000
PN da Serra das Confusões1.4211.0000.8320.2210.1581.0000.9370.3470.0000.8420.2740.0110.021
PN da Serra do Cipó5.0005.0005.0005.0005.0005.0005.0005.0000.6675.0005.0005.0005.000
PN da Serra do Gandarela5.0005.0005.0005.0005.0005.0005.0005.0001.0005.0005.0005.0005.000
PN da Serra do Pardo0.0000.0000.5190.0000.3080.0191.0001.0000.0000.0000.9421.0001.000
PN da Serra dos Orgãos4.0005.0005.0005.0005.0004.0004.0003.5000.5004.0004.0004.0004.000
PN das Emas5.0004.0004.0001.0001.3134.0004.0002.0000.0005.0004.0003.9381.000
PN das Nascentes do Rio Parnaíba3.2112.6221.2441.2331.1892.6781.6671.1330.0002.5221.7001.5561.689
PN das Sempre-Vivas5.0005.0005.0004.8244.8245.0005.0004.3530.0005.0005.0004.6474.824
PN de Boa Nova3.0003.0001.8180.7270.9092.0002.0000.2730.0002.8181.9090.7270.455
PN de Brasília5.0005.0004.4004.0004.0004.8004.0004.0000.0005.0004.6004.0004.000
PN de Caparaó5.0005.0005.0005.0005.0005.0005.0004.3331.0005.0005.0004.0004.000
PN de Itatiaia5.0005.0005.0004.3335.0005.0004.3334.0001.0004.3334.0004.0003.333
PN do Acari0.0000.5001.0000.0490.0001.0001.0001.0000.0001.0001.0001.0001.000
PN do Araguaia3.2172.0002.0002.0002.0001.9712.0001.8120.0002.1011.4641.0001.000
PN do Boqueirão da Onça0.4670.2440.0000.0000.0000.0000.0000.0000.0000.0220.0000.0000.000
PN do Jamanxim0.0000.4200.9500.5100.0000.9701.0001.0000.0000.7001.0001.0001.000
PN do Juruena0.1961.0001.0002.0002.0001.0001.0001.0610.0000.9871.0001.0001.000
PN do Pantanal Mato-Grossense2.0632.8752.0002.0002.0002.0003.0001.0000.0002.0002.0002.2501.375
PN do Rio Novo0.0000.9851.0002.0002.0001.0001.0001.0000.0000.8151.0001.0001.000
PN dos Campos Ferruginosos0.0000.0000.0000.2500.1250.0001.0001.0000.0000.0000.1251.0001.000
PN Grande Sertão Veredas5.0005.0004.0001.5862.2074.5523.6553.0000.0004.7243.9312.8621.862
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Alves-de-Lima, L.; Alves, D.F.R.; Anjos, D.V.; Valdivia, F.A.; Torezan-Silingardi, H.M. Predicting the Impact of Global Climate Change on the Geographic Distribution of Anemochoric Species in Protected Areas. Atmosphere 2025, 16, 453. https://doi.org/10.3390/atmos16040453

AMA Style

Alves-de-Lima L, Alves DFR, Anjos DV, Valdivia FA, Torezan-Silingardi HM. Predicting the Impact of Global Climate Change on the Geographic Distribution of Anemochoric Species in Protected Areas. Atmosphere. 2025; 16(4):453. https://doi.org/10.3390/atmos16040453

Chicago/Turabian Style

Alves-de-Lima, Larissa, Douglas Fernandes Rodrigues Alves, Diego Vinicius Anjos, Fernando Anco Valdivia, and Helena Maura Torezan-Silingardi. 2025. "Predicting the Impact of Global Climate Change on the Geographic Distribution of Anemochoric Species in Protected Areas" Atmosphere 16, no. 4: 453. https://doi.org/10.3390/atmos16040453

APA Style

Alves-de-Lima, L., Alves, D. F. R., Anjos, D. V., Valdivia, F. A., & Torezan-Silingardi, H. M. (2025). Predicting the Impact of Global Climate Change on the Geographic Distribution of Anemochoric Species in Protected Areas. Atmosphere, 16(4), 453. https://doi.org/10.3390/atmos16040453

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