Drought index predictability for historical and future periods across the Southern plain of Nepal Himalaya.

Drought episodes across the Himalayas are inevitable due to rapidly increasing atmospheric temperatures and uncertainties in rainfall patterns. Tarai of Nepal is a tropical region located in the foothills of the Central Himalaya as a country's food granary with a contribution of over 50% to the entire country's agricultural production. However, there is a lack of detailed studies exploring the spatiotemporal occurrence of drought in these regions under the changing climate. In this study, we used the ensemble of nine climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) under two shared socio-economic pathways (SSPs), namely SSP245 (an intermediate development pathway) and SSP585 (a high development pathway), to assess anticipated drought during the mid-century. We used bias-corrected gridded data from the Worldclim to project drought events by the end of the mid-century based on the historical period (1989-2018). We computed historical and projected Thornthwaite moisture index (TMI) to evaluate soil moisture conditions on a seasonal scale for the Tarai region's Eastern, Central, and Western parts. The model ensemble projected a significant increase in precipitation and temperature for the entire Tarai by the end of mid-century. However, the winter and spring seasons are projected to suffer precipitation deficiency and a temperature rise. Our results indicated that the Eastern Tarai would likely experience a decrease in winter precipitation. We emphasize that the presented spatiotemporal pattern of the MI will be instrumental in addressing the irrigation facility's needs, choice, and rotation of crops under the changing climate scenarios and in improving our mitigation measures and adaptation plans for sustainability of the agriculture in drought-prone areas.

Microclimate buffering and thermal tolerance across elevations in a tropical butterfly.

Microclimatic variability in tropical forests plays a key role in shaping species distributions and their ability to cope with environmental change, especially for ectotherms. Nonetheless, currently available climatic datasets lack data from the forest interior and, furthermore, our knowledge of thermal tolerance among tropical ectotherms is limited. We therefore studied natural variation in the microclimate experienced by tropical butterflies in the genus Heliconius across their Andean range in a single year. We found that the forest strongly buffers temperature and humidity in the understorey, especially in the lowlands, where temperatures are more extreme. There were systematic differences between our yearly records and macroclimate databases (WorldClim2), with lower interpolated minimum temperatures and maximum temperatures higher than expected. We then assessed thermal tolerance of 10 Heliconius butterfly species in the wild and found that populations at high elevations had significantly lower heat tolerance than those at lower elevations. However, when we reared populations of the widespread H. erato from high and low elevations in a common-garden environment, the difference in heat tolerance across elevations was reduced, indicating plasticity in this trait. Microclimate buffering is not currently captured in publicly available datasets, but could be crucial for enabling upland shifting of species sensitive to heat such as highland Heliconius Plasticity in thermal tolerance may alleviate the effects of global warming on some widespread ectotherm species, but more research is needed to understand the long-term consequences of plasticity on populations and species.

Assessing climate boundary shifting under climate change scenarios across Nepal.

This study assesses the climate boundary shifts from the historical time to near/mid future by using a slightly modified Köppen-Geiger (KG) classification scheme and presents comprehensive pictures of historical (1960-1990) and projected near/mid future (1950s: 2040-2060/1970s: 2060-2080) climate classes across Nepal. Ensembles of three selected general circulation models (GCMs) under two Representative Concentration Pathways (RCP 4.5 and RCP 8.5) were used for projected future analysis. During the 1950s, annual average temperature is expected to increase by 2.5 °C under RCP 8.5. Similarly, during the 1970s, it is even anticipated to rise by 3.6 °C under RCP 8.5. The rate of temperature rise is higher in the non-monsoon period than in monsoon period. During the 1970s, annual precipitation is projected to increase by 8.1% under RCP 8.5. Even though the precipitation is anticipated to increase in the future in annual scale, winter seasons are estimated to be drier by more than 15%. This study shows significant increments of tropical (Am and Aw) and arid (BSk) climate types and reductions of temperate (Cwa and Cwb) and polar (ET and EF). Noticeably, the reduction of the areal coverage of polar frost (EF) is considerably high. In general, about 50% of the country's area is covered by the temperate climate (Cwa and Cwb) in baseline scenario and it is expected to reduce to 45% under RCP 4.5 and 42.5% under RCP 8.5 during the 1950s, and 42% under RCP 4.5 and 39% under RCP 8.5 during the 1970s. Importantly, the degree of climate boundary shifts is quite higher under RCP 8.5 than RCP 4.5, and likewise, the degree is higher during the 1970s than the 1950s. We believe this study to facilitate the identification of regions in which impacts of climate change are notable for crop production, soil management, and disaster risk reduction, requiring a more detailed assessment of adaptation measures. The assessment of climate boundary shifting can serve as valuable information for stakeholders of many disciplines like water, climate, transport, energy, environment, disaster, development, agriculture, and tourism.

Forecasting climate change impacts on the distribution of wetland habitat in the Midwestern United states.

Shifting precipitation patterns brought on by climate change threaten to alter the future distribution of wetlands. We developed a set of models to understand the role climate plays in determining wetland formation on a landscape scale and to forecast changes in wetland distribution for the Midwestern United States. These models combined 35 climate variables with 21 geographic and anthropogenic factors thought to encapsulate other major drivers of wetland distribution for the Midwest. All models successfully recreated a majority of the variation in current wetland area within the Midwest, and showed that wetland area was significantly associated with climate, even when controlling for landscape context. Inferential (linear) models identified a consistent negative association between wetland area and isothermality. This is likely the result of regular inundation in areas where precipitation accumulates as snow, then melts faster than drainage capacity. Moisture index seasonality was identified as a key factor distinguishing between emergent and forested wetland types, where forested wetland area at the landscape scale is associated with a greater seasonal variation in water table depth. Forecasting models (neural networks) predicted an increase in potential wetland area in the coming century, with areas conducive to forested wetland formation expanding more rapidly than areas conducive to emergent wetlands. Local cluster analyses identified Iowa and Northeastern Missouri as areas of anticipated wetland expansion, indicating both a risk to crop production within the Midwest Corn Belt and an opportunity for wetland conservation, while Northern Minnesota and Michigan are potentially at risk of wetland losses under a future climate.

Climate reconstruction analysis using coexistence likelihood estimation (CRACLE): a method for the estimation of climate using vegetation.

UNLABELLED: • PREMISE OF THE STUDY: Plant distributions have long been understood to be correlated with the environmental conditions to which species are adapted. Climate is one of the major components driving species distributions. Therefore, it is expected that the plants coexisting in a community are reflective of the local environment, particularly climate.• METHODS: Presented here is a method for the estimation of climate from local plant species coexistence data. The method, Climate Reconstruction Analysis using Coexistence Likelihood Estimation (CRACLE), is a likelihood-based method that employs specimen collection data at a global scale for the inference of species climate tolerance. CRACLE calculates the maximum joint likelihood of coexistence given individual species climate tolerance characterization to estimate the expected climate.• KEY RESULTS: Plant distribution data for more than 4000 species were used to show that this method accurately infers expected climate profiles for 165 sites with diverse climatic conditions. Estimates differ from the WorldClim global climate model by less than 1.5°C on average for mean annual temperature and less than ∼250 mm for mean annual precipitation. This is a significant improvement upon other plant-based climate-proxy methods.• CONCLUSIONS: CRACLE validates long hypothesized interactions between climate and local associations of plant species. Furthermore, CRACLE successfully estimates climate that is consistent with the widely used WorldClim model and therefore may be applied to the quantitative estimation of paleoclimate in future studies.

Habitat suitability maps for Australian flora and fauna under CMIP6 climate scenarios.

BACKGROUND: Spatial information about the location and suitability of areas for native plant and animal species under different climate futures is an important input to land use and conservation planning and management. Australia, renowned for its abundant species diversity and endemism, often relies on modeled data to assess species distributions due to the country's vast size and the challenges associated with conducting on-ground surveys on such a large scale. The objective of this article is to develop habitat suitability maps for Australian flora and fauna under different climate futures. RESULTS: Using MaxEnt, we produced Australia-wide habitat suitability maps under RCP2.6-SSP1, RCP4.5-SSP2, RCP7.0-SSP3, and RCP8.5-SSP5 climate futures for 1,382 terrestrial vertebrates and 9,251 vascular plants vascular plants at 5 km2 for open access. This represents 60% of all Australian mammal species, 77% of amphibian species, 50% of reptile species, 71% of bird species, and 44% of vascular plant species. We also include tabular data, which include summaries of total quality-weighted habitat area of species under different climate scenarios and time periods. CONCLUSIONS: The spatial data supplied can help identify important and sensitive locations for species under various climate futures. Additionally, the supplied tabular data can provide insights into the impacts of climate change on biodiversity in Australia. These habitat suitability maps can be used as input data for landscape and conservation planning or species management, particularly under different climate change scenarios in Australia.

A Protocol to Retrieve and Curate Spatial and Climatic Data from Online Biodiversity Databases Using R.

Ecological and evolutionary studies often require high quality biodiversity data. This information is readily available through the many online databases that have compiled biodiversity data from herbaria, museums, and human observations. However, the process of preparing this information for analysis is complex and time consuming. In this study, we have developed a protocol in R language to process spatial data (download, merge, clean, and correct) and extract climatic data, using some genera of the ginseng family (Araliaceae) as an example. The protocol provides an automated way to process spatial and climatic data for numerous taxa independently and from multiple online databases. The script uses GBIF, BIEN, and WorldClim as the online data sources, but can be easily adapted to include other online databases. The script also uses genera as the sampling unit but provides a way to use species as the target. The cleaning process includes a filter to remove occurrences outside the natural range of the taxa, gardens, and other human environments, as well as erroneous locations and a spatial correction for misplaced occurrences (i.e., occurrences within a distance buffer from the coastal boundary). Additionally, each step of the protocol can be run independently. Thus, the protocol can begin with data cleaning, if the database has already been compiled, or with climatic data extraction, if the database has already been parsed. Each line of the R script is commented so that it can also be run by users with little knowledge of R.

Comparing the Performance of CMCC-BioClimInd and WorldClim Datasets in Predicting Global Invasive Plant Distributions.

Species distribution modeling (SDM) has been widely used to predict the distribution of invasive plant species based on bioclimatic variables. However, the specific selection of these variables may affect the performance of SDM. This investigation elucidates a new bioclimate variable dataset (i.e., CMCC-BioClimInd) for its use in SDM. The predictive performance of SDM that includes WorldClim and CMCC-BioClimInd was evaluated by AUC and omission rate and the explanatory power of both datasets was assessed by the jackknife method. Furthermore, the ODMAP protocol was used to record CMCC-BioClimInd to ensure reproducibility. The results indicated that CMCC-BioClimInd effectively simulates invasive plant species' distribution. Based on the contribution rate of CMCC-BioClimInd to the distribution of invasive plant species, it was inferred that the modified and simplified continentality and Kira warmth index from CMCC-BioClimInd had a strong explanatory power. Under the 35 bioclimatic variables of CMCC-BioClimInd, alien invasive plant species are mainly distributed in equatorial, tropical, and subtropical regions. We tested a new bioclimate variable dataset to simulate the distribution of invasive plant species worldwide. This method has great potential to improve the efficiency of species distribution modeling, thereby providing a new perspective for risk assessment and management of global invasive plant species.

Blessing and curse of bioclimatic variables: A comparison of different calculation schemes and datasets for species distribution modeling within the extended Mediterranean area.

Bioclimatic variables (BCVs) are the most widely used predictors within the field of species distribution modeling, but recent studies imply that BCVs alone are not sufficient to describe these limits. Unfortunately, the most popular database, WorldClim, offers only a limited selection of bioclimatological predictors; thus, other climatological datasets should be considered, and, for data consistency, the BCVs should also be derived from the respective datasets. Here, we investigate how well the BCVs are represented by different datasets for the extended Mediterranean area within the period 1970-2020, how different calculation schemes affect the representation of BCVs, and how deviations among the datasets differ regionally. We consider different calculation schemes for quarters/months, the annual mean temperature (BCV-1), and the maximum temperature of the warmest month (BCV-5). Additionally, we analyzed the effect of different temporal resolutions for BCV-1 and BCV-5. Differences resulting from different calculation schemes are presented for ERA5-Land. Selected BCVs are analyzed to show differences between WorldClim, ERA5-Land, E-OBS, and CRU. Our results show that (a) differences between the two calculation schemes for BCV-1 diminish as the temporal resolution decreases, while the differences for BCV-5 increase; (b) with respect to the definition of the respective month/quarter, intra-annual shifts induced by the calculation schemes can have substantially different effects on the BCVs; (c) all datasets represent the different BCVs similarly, but with partly large differences in some subregions; and (d) the largest differences occur when specific month/quarters are defined by precipitation. In summary, (a) since the definition of BCVs matches different calculation schemes, transparent communication of the BCVs calculation schemes is required; (b) the calculation, integration, or elimination of BCVs has to be examined carefully for each dataset, region, period, or species; and (c) the evaluated datasets provide, except in some areas, a consistent representation of BCVs within the extended Mediterranean region.

Worldclim 2.1 versus Worldclim 1.4: Climatic niche and grid resolution affect between-version mismatches in Habitat Suitability Models predictions across Europe.

The influence of climate on the distribution of taxa has been extensively investigated in the last two decades through Habitat Suitability Models (HSMs). In this context, the Worldclim database represents an invaluable data source as it provides worldwide climate surfaces for both historical and future time horizons. Thousands of HSMs-based papers have been published taking advantage of Worldclim 1.4, the first online version of this repository. In 2017, Worldclim 2.1 was released. Here, we evaluated spatially explicit prediction mismatch at continental scale, focusing on Europe, between HSMs fitted using climate surfaces from the two Worldclim versions (between-version differences). To this aim, we simulated occurrence probability and presence-absence across Europe of four virtual species (VS) with differing climate-occurrence relationships. For each VS, we fitted HSMs upon uncorrelated bioclimatic variables derived from each Worldclim version at three grid resolutions. For each factor combination, HSMs attaining sufficient discrimination performance on spatially independent test data were projected across Europe under current conditions and various future scenarios, and importance scores of the single variables were computed. HSMs failed in accurately retrieving the simulated climate-occurrence relationships for the climate-tolerant VS and the one occurring under a narrow combination of climatic conditions. Under current climate, noticeable between-version prediction mismatch emerged across most of Europe for these two VSs, whose simulated suitability mainly depended upon diurnal or yearly variability in temperature; differently, between-version differences were more clustered toward areas showing extreme values, like mountainous massifs or southern regions, for VSs responding to average temperature and precipitation trends. Under future climate, the chosen emission scenarios and Global Climate Models did not evidently influence between-version prediction discrepancies, while grid resolution synergistically interacted with VSs' niche characteristics in determining extent of such differences. Our findings could help in re-evaluating previous biodiversity-related works relying on geographical predictions from Worldclim-based HSMs.

Prediction for Global Peste des Petits Ruminants Outbreaks Based on a Combination of Random Forest Algorithms and Meteorological Data.

Peste des Petits Ruminants (PPR) is an acute and highly contagious transboundary disease caused by the PPR virus (PPRV). The virus infects goats, sheep and some wild relatives of small domestic ruminants, such as antelopes. PPR is listed by the World Organization for Animal Health as an animal disease that must be reported promptly. In this paper, PPR outbreak data combined with WorldClim database meteorological data were used to build a PPR prediction model. Using feature selection methods, eight sets of features were selected: bio3, bio10, bio15, bio18, prec7, prec8, prec12, and alt for modeling. Then different machine learning algorithms were used to build models, among which the random forest (RF) algorithm was found to have the best modeling effect. The ACC value of prediction accuracy for the model on the training set can reach 99.10%, while the ACC on the test sets was 99.10%. Therefore, RF algorithms and eight features were finally selected to build the model in order to build the online prediction system. In addition, we adopt single-factor modeling and correlation analysis of modeling variables to explore the impact of each variable on modeling results. It was found that bio18 (the warmest quarterly precipitation), prec7 (the precipitation in July), and prec8 (the precipitation in August) contributed significantly to the model, and the outbreak of the epidemic may have an important relationship with precipitation. Eventually, we used the final qualitative prediction model to establish a global online prediction system for the PPR epidemic.

Prediction for global African swine fever outbreaks based on a combination of random forest algorithms and meteorological data.

African swine fever (ASF) is a virulent infectious disease of pigs. As there is no effective vaccine and treatment method at present, it poses a great threat to the pig industry once it breaks out. In this paper, we used ASF outbreak data and the WorldClim database meteorological data and selected the CfsSubset Evaluator-Best First feature selection method combined with the random forest algorithms to construct an African swine fever outbreak prediction model. Subsequently, we also established a test set for data other than modelling, and the accuracy accuracy value range of the model on the independent test set was 76.02%-84.64%, which indicated that the modelling effect was better and the prediction accuracy was higher than previous estimates. In addition, logistic regression analysis was conducted on 12 features used for modelling and the ROC curves were drawn. The results showed that the bio14 features (precipitation of driest month) had the largest contribution to the outbreak of ASF, and it was speculated that the outbreak of the epidemic was significantly related to precipitation. Finally, we used this qualitative prediction model to build a global online prediction system for ASF outbreaks, in the hope that this study will help to decision-makers who can then take the relevant prevention and control measures in order to prevent the further spread of future epidemics of the disease.

Changes in the Geographic Distribution of the Diana Fritillary (Speyeria diana: Nymphalidae) under Forecasted Predictions of Climate Change.

Climate change is predicted to alter the geographic distribution of a wide variety of taxa, including butterfly species. Research has focused primarily on high latitude species in North America, with no known studies examining responses of taxa in the southeastern United States. The Diana fritillary (Speyeria diana) has experienced a recent range retraction in that region, disappearing from lowland sites and now persisting in two phylogenetically distinct high elevation populations. These findings are consistent with the predicted effects of a warming climate on numerous taxa, including other butterfly species in North America and Europe. We used ecological niche modeling to predict future changes to the distribution of S. diana under several climate models. To evaluate how climate change might influence the geographic distribution of this butterfly, we developed ecological niche models using Maxent. We used two global circulation models, the community climate system model (CCSM) and the model for interdisciplinary research on climate (MIROC), under low and high emissions scenarios to predict the future distribution of S. diana. Models were evaluated using the receiver operating characteristics area under curve (AUC) test and the true skill statistics (TSS) (mean AUC = 0.91 ± 0.0028 SE, TSS = 0.87 ± 0.0032 SE for representative concentration pathway (RCP) = 4.5; and mean AUC = 0.87 ± 0.0031 SE, TSS = 0.84 ± 0.0032 SE for RCP = 8.5), which both indicate that the models we produced were significantly better than random (0.5). The four modeled climate scenarios resulted in an average loss of 91% of suitable habitat for S. diana by 2050. Populations in the southern Appalachian Mountains were predicted to suffer the most severe fragmentation and reduction in suitable habitat, threatening an important source of genetic diversity for the species. The geographic and genetic isolation of populations in the west suggest that those populations are equally as vulnerable to decline in the future, warranting ongoing conservation of those populations as well. Our results suggest that the Diana fritillary is under threat of decline by 2050 across its entire distribution from climate change, and is likely to be negatively affected by other human-induced factors as well.

Spatiotemporal environmental heterogeneity and the maintenance of the tailspot polymorphism in the variable platyfish (Xiphophorus variatus).

Genetic variation is critical for adaptive evolution. Despite its importance, there is still limited evidence in support of some prominent theoretical models explaining the maintenance of genetic polymorphism within populations. We examined 84 populations of Xiphophorus variatus, a livebearing fish with a genetic polymorphism associated with physiological performance, to test: (1) whether niche differentiation explains broad-scale maintenance of polymorphism, (2) whether polymorphism is maintained among populations by local adaptation and migration, or (3) whether heterogeneity in explicit environmental variables could be linked to levels of polymorphism within populations. We found no evidence of climatic niche differentiation that could generate or maintain broad geographic variation in polymorphism. Subsequently, hierarchical partitioning of genetic richness and partial mantel tests revealed that 76% of the observed genetic richness was partitioned within populations with no effect of geographic distance on polymorphism. These results strongly suggest a lack of migration-selection balance in the maintenance of polymorphism, and model selection confirmed a significant relationship between environmental heterogeneity and genetic richness within populations. Few studies have demonstrated such effects at this scale, and additional studies in other taxa should examine the generality of gene-by-environment interactions across populations to better understand the dynamics and scale of balancing selection.

Species distribution modelling for Rhipicephalus microplus (Acari: Ixodidae) in Benin, West Africa: comparing datasets and modelling algorithms.

Rhipicephalus microplus is one of the most widely distributed and economically important ticks, transmitting Babesia bigemina, B. bovis and Anaplasma marginale. It was recently introduced to West Africa on live animals originating from Brazil. Knowing the precise environmental suitability for the tick would allow veterinary health officials to draft vector control strategies for different regions of the country. To test the performance of modelling algorithms and different sets of environmental explanatory variables, species distribution models for this tick species in Benin were developed using generalized linear models, linear discriminant analysis and random forests. The training data for these models were a dataset containing reported absence or presence in 104 farms, randomly selected across Benin. These farms were sampled at the end of the rainy season, which corresponds with an annual peak in tick abundance. Two environmental datasets for the country of Benin were compared: one based on interpolated climate data (WorldClim) and one based on remotely sensed images (MODIS). The pixel size for both environmental datasets was 1 km. Highly suitable areas occurred mainly along the warmer and humid coast extending northwards to central Benin. The northern hot and drier areas were found to be unsuitable. The models developed and tested on data from the entire country were generally found to perform well, having an AUC value greater than 0.92. Although statistically significant, only small differences in accuracy measures were found between the modelling algorithms, or between the environmental datasets. The resulting risk maps differed nonetheless. Models based on interpolated climate suggested gradual variations in habitat suitability, while those based on remotely sensed data indicated a sharper contrast between suitable and unsuitable areas, and a patchy distribution of the suitable areas. Remotely sensed data yielded more spatial detail in the predictions. When computing accuracy measures on a subset of data along the invasion front, the modelling technique Random Forest outperformed the other modelling approaches, and results with MODIS-derived variables were better than those using WorldClim data. The high environmental suitability for R. microplus in the southern half of Benin raises concern at the regional level for animal health, including its potential to substantially alter transmission risk of Babesia bovis. The northern part of Benin appeared overall of low environmental suitability. Continuous surveillance in the transition zone however remains relevant, in relation to important cattle movements in the region, and to the invasive character of R. microplus.

The interplay between local ecology, divergent selection, and genetic drift in population divergence of a sexually antagonistic female trait.

Genetically polymorphic species offer the possibility to study maintenance of genetic variation and the potential role for genetic drift in population divergence. Indirect inference of the selection regimes operating on polymorphic traits can be achieved by comparing population divergence in neutral genetic markers with population divergence in trait frequencies. Such an approach could further be combined with ecological data to better understand agents of selection. Here, we infer the selective regimes acting on a polymorphic mating trait in an insect group; the dorsal structures (either rough or smooth) of female diving beetles. Our recent work suggests that the rough structures have a sexually antagonistic function in reducing male mating attempts. For two species (Dytiscus lapponicus and Graphoderus zonatus), we could not reject genetic drift as an explanation for population divergence in morph frequencies, whereas for the third (Hygrotus impressopunctatus) we found that divergent selection pulls morph frequencies apart across populations. Furthermore, population morph frequencies in H. impressopunctatus were significantly related to local bioclimatic factors, providing an additional line of evidence for local adaptation in this species. These data, therefore, suggest that local ecological factors and sexual conflict interact over larger spatial scales to shape population divergence in the polymorphism.

Global patterns of aboveground carbon stock and sequestration in mangroves

ABSTRACT In order to contribute to understand the factors that control the provisioning of the ecosystem service of carbon storage by mangroves, data on carbon stock and sequestration in the aboveground biomass (AGB) from 73 articles were averaged and tested for the dependence on latitude, climatic parameters, physiographic types and age. Global means of carbon stock (78.0 ± 64.5 tC.ha-1) and sequestration (2.9 ± 2.2 tC.ha-1.yr-1) showed that mangroves are among the forest ecosystems with greater capacity of carbon storage in AGB per area. On the global scale, carbon stock increases toward the equator (R²=0.22) and is dependent on 13 climatic parameters, which can be integrated in the following predictive equation: Carbon Stock in AGB = -16.342 + (8.341 x Isothermality) + (0.021 x Annual Precipitation) [R²=0.34; p < 0.05]. It was shown that almost 70% of carbon stock variability is explained by age. Carbon stock and sequestration also vary according to physiographic types, indicating the importance of hydroperiod and edaphic parameters to the local variability of carbon stock. By demonstrating the contribution of local and regional-global factors to carbon stock, this study provides information to the forecast of the effects of future climate changes and local anthropogenic forcings on this ecosystem service.

Prediciendo la distribución de Polylepis: bosques Andinos vulnerables y cada vez más importantes

Los bosques de Polylepis son recursos vitales para la conservación de la biodiversidad y funciones hidrológicas, la cual se verá alterada por el cambio climático a nivel mundial desafiando la sostenibilidad de las comunidades locales. Sin embargo, estos ecosistemas andinos de gran altitud son cada vez más vulnerables debido a la presión antropogénica como la fragmentación, deforestación y el incremento en el ganado. La importancia para predecir la distribución de bosques nativos ha aumentado para contrarrestar los efectos negativos del cambio climático a través de la conservación y la reforestación. El objetivo de este estudio fue desarrollar y analizar los modelos de distribución de dos especies, Polylepis sericea y P. besseri, que forman bosques extensos a lo largo de los Andes. Este estudio utilizó el programa Maxent, el clima y capas ambientales de una resolución de 1 Km. El modelo de distribución previsto para P. sericea indica que la especie podría estar situada en una variedad de hábitats a lo largo de la Cordillera de los Andes, mientras que P. besseri se limitaba a las grandes alturas del sur de Perú y Bolivia. Para ambas especies, los metros de elevación y la temperatura son los factores más importantes para la distribución prevista. El perfeccionamiento del modelo de Polylepis y otras especies andinas utilizando datos de satélites cada vez más disponibles al público demuestran el potencial para ayudar a definir las áreas de diversidad y mejorar las estrategias de conservación en los Andes.

Polylepis woodlands are a vital resource for preserving biodiversity and hydrological functions, which will be altered by climate change and challenge the sustainability of local human communities. However, these high-altitude Andean ecosystems are becoming increasingly vulnerable due to anthropogenic pressure including fragmentation, deforestation and the increase in livestock. Predicting the distribution of native woodlands has become increasingly important to counteract the negative effects of climate change through reforestation and conservation. The objective of this study was to develop and analyze the distribution models of two species that form extensive woodlands along the Andes, namely Polylepis sericea and P. weberbaueri. This study utilized the program Maxent, climate and remotely sensed environmental layers at 1 Km resolution. The predicted distribution model for P. sericea indicated that the species could be located in a variety of habitats along the Andean Cordillera, while P. weberbaueri was restricted to the high elevations of southern Peru and Bolivia. For both species, elevation and temperature metrics were the most significant factors for predicted distribution. Further model refinement of Polylepis and other Andean species using increasingly available satellite data demonstrate the potential to help define areas of diversity and improve conservation strategies for the Andes.