Species Distribution Model MaxEnt Research Articles

How can dry tropical forests respond to climate change? Predictions for key Non-Timber Forest Product species show different trends in India.

The present study provides an assessment of the distribution of key Non-Timber Forest Product species in India, namely Aegle marmelos (L.) Correa, Buchanania lanzan Spreng., Madhuca longifolia (J. Koenig ex L.) J. F. Macbr., Phyllanthus emblica L. and Terminalia bellirica (Gaertn.) Roxb. The suitable habitat was analyzed under current climate scenarios and subsequently, the future distribution (2050s and 2070s) was mapped under RCP 2.6 and 8.5 scenarios, along with the past distribution (mid-Holocene, ~ 6000calyear BP) using the MaxEnt species distribution model. The distribution of all species is primarily driven by key bioclimatic factors, including annual precipitation (Bio_12), mean annual temperature (Bio_1), isothermality (Bio_3) and precipitation of the coldest quarter (Bio_19). The results indicate that the present distribution of these species is mainly centred in the Western Ghats regions, Central Highlands, North-eastern India and Siwalik hills. The current study suggests that under the future climate change, the suitable habitat for A. marmelos and T. bellirica is expected to increase while for B. lanzan, M. longifolia and P. emblica, it is projected to decline. A. marmelos and T. bellirica are anticipated to exhibit resilience to future climate changes and are expected to be minimally affected, while B. lanzan, M. longifolia and P. emblica are highly sensitive to high temperature and alteration in rainfall pattern expected under future climate changes. The projections of habitat suitability areas can be used as a valuable foundation for developing conservation and restoration strategies aimed at alleviating the climate change impacts on NTFP species.

Investigating Endemic Species Conservation Hotspots Based on Species Distribution Models in Swat Region, Hindu Kush Pakistan

For the effective conservation of plant species, it is imperative to manage habitat loss and fragmentation by establishing a protected area network that comprehensively encompasses their habitat. In this study, we identified the suitable habitats for some endemic plant species in the Swat District, Pakistan, to conduct a thorough analysis of proposed protected areas (hotspot areas). Utilizing data from a recent botanical survey, environmental parameters from World-Clim, and various other sources, we determined the habitat of each species using the MaxEnt species distribution model. The modeled habitat suitability for each species were combined to generate maps illustrating endemic richness (hotspots), as well as to prioritize areas based on conservation value and habitat suitability. Future climate projections spanning from 2000 to 2050 were acquired from the WorldClim database Nineteen bioclimatic variables were assessed, incorporating data from three global climate models: CCSM4, HadGEM2-AO, and MPI-ESM-LR. We modeled seven endemic species from the Swat District and, by combining their maps, found three hotspot areas which show more environmental suitability under future climatic conditions. The future model forecasts a significant decrease in population density by the year 2050. Currently, these species exhibit their highest density along the eastern and southern borders of the valley, particularly in the Karakar, Elum border, Mankial Kalam, and Gabral Kalam areas. The anticipated alterations in density and distribution could have a considerable impact not only on the tree species but also on the associated sub flora.

Modelling lantana camara invasion in the inkomati catchment in Mpumalanga, South Africa

Lantana Camara ranks among the most notorious and hazardous invasive species on the planet. There has been extensive research focusing on the remote sensing of Lantana Camara (L. Camara). However, the factors that facilitate its spread, especially in savanna rangelands, are not yet fully comprehended. The present research investigated the recent spatial distribution of L. Camara in the Inkomati catchment, Mpumalanga, South Africa. The research used the Random Forest classification algorithm, with Sentinel-2 remotely sensed data within Google Earth Engine. The study also modelled areas vulnerable to L. Camara invasion using the Maxent species distribution model. The study found that L. Camara covered approximately 34.86% of the entire study area, with a user's and a producer's accuracies of 91% and 84%, respectively. Additionally, elevation was identified as the most influential topographic variable on the species spatial distribution and invasion, while Topographic Wetness Index had the least influence. The model developed using topographic variables had the highest accuracy of 0.88. On the other hand, the predictive model that utilised Sentinel 2 bands had the least accuracy of 0.81. The red edge and NIR bands with Random Forest allowed for an accurate assessment of L. Camara distribution. The study therefore provides the basis for the control or further expansion of L. Camara species in the province.

Modelling Habitat Suitability and Distribution of the Endemic Mindanao Horned Frog (Pelobatrachus stejnegeri) and its Response to Changing Climate

Climate change is already affecting biodiversity, with special concern to endemics whose range is restricted and limited. This study focuses on the Mindanao horned frog (Pelobatrachus stejnegeri), an endemic species to the Philippines, susceptible to climate-induced habitat changes. Using MaxEnt species distribution model (SDM), the current and future (year 2050 projections) habitat suitability and distribution of P. stejnegeri were modelled. Results showed that annual mean temperature, elevation, and annual precipitation were the environmental variables having the highest influence on P. stejnegeri's distribution. The model predicts a significant range contraction under representative concentration pathways (RCP) future scenarios (RCP 2.6 and RCP 8.5), with a more pronounced decrease in distribution (31.72%) under the high emission scenario (RCP 8.5). These findings emphasize the vulnerability of P. stejnegeri to climate change and highlight the importance of integrating SDM into conservation and management strategies to protect endemic species under changing climatic conditions.

Prosopis juliflora (Swartz) DC. invasion in protected areas of India under climate change

Globally invasive alien plants pose a serious threat to biodiversity and are invading protected areas. However, information regarding the potential risk of invasive alien species in protected areas is very limited in India. Therefore, the aim of the study was to examine the potential risks of P. juliflora invasions under projected climate change. We used P. juliflora climate suitability maps based on our previous study using the MaxEnt species distribution model and investigated how P. juliflora invasion may change inside and outside the protected areas by 2050 under two climate change scenarios, representative concentration pathways (RCP) 2.6 and 8.5. The findings of the study suggest that the overall area of P. juliflora suitability in the protected areas will increase by 2050. Further, we found that several protected areas containing relatively high vertebrate diversity are located in climatically suitable areas for P. juliflora. The study findings encourage management efforts to manage the spread of P. juliflora invasion in the selected protected areas of India to protect and conserve the threatened vertebrates' habitats.

Lidar-based MaxEnt models to support conservation planning for endangered Red-cockaded Woodpeckers in urbanizing environments

Sensitive and intensively managed species require carefully thought-out management plans to promote and maintain specific habitat conditions. Urban features and land-use change must be assimilated into these habitat management plans, as they become increasingly present globally. As a case study, several MaxEnt species distribution models were developed that could enable habitat management efforts for the endangered Red-cockaded Woodpecker (RCW) in moderate to increasingly urbanized environments. Model development began with a classification of fine-scale, lidar-based habitat indicators with the area normalized at the stand level and developed around known habitat characteristics of RCW. Other explanatory rasters included distance to different urban features, and experimentation with spectral layers outside the visible light spectrum. Models were trained using presence data from a relatively small but comprehensively surveyed population in Montgomery County, Texas, and three compartments that were recently pedestrian surveyed for RCWs on the Sam Houston National Forest. The former is experiencing moderate levels of urbanization, and the latter is in earlier stages. The best performing model predicted RCW presence 94% of the time at a 0.4 probability threshold and resulted in an area under the curve (AUC) of 0.88. Successful model development required a specific combination of steps and data processing, including the use of lidar-based habitat indicators created using data fusion and machine learning classification, land-use features, and non-visible spectra. These methods can provide valuable insights into strategic habitat planning for the RCW and other sensitive species in urbanizing landscapes. This study reinforces what habitat characteristics promote RCW success, while providing valuable insights to guide management activities around urbanization. These could include mapping suitable recruitment areas that remain unoccupied, spatially identifying where habitat quality was lacking or sufficient, and predicting the impact of future land-use change. This case study demonstrates that species distribution modelling can be successfully applied at subpopulation and fine scales, and for the practical purpose of enabling habitat and conservation planning where anthropogenic activities are adding challenging complexities.

Analyzing Habitat Suitability of Bird Species in Urban Areas to Changes in Biotope Level: Case Study of Seoul

Background and objective: Rapid urbanization has led to habitat degradation and fragmentation in urban ecosystems, negatively impacting biodiversity. Various natural spaces and green areas, particularly urban biotopes, are essential to foster an environment for diverse species within urban areas. The increase in biotope grades is a crucial factor in enhancing biodiversity and expanding wildlife habitats in urban areas, thereby improving the overall health and sustainability of urban ecosystems. This study focuses on environmentally sensitive bird species, applying scenarios of biotope grade elevation to derive efficient methods for improving biotope grades. The goal is to support projects and policy decisions to increase urban biodiversity and sustain ecosystems within the city.Methods: The analysis model employed the MaxEnt species distribution model, and 11 environmental variables tailored to bird characteristics were considered. Additionally, four scenarios for upward adjustments in biotope grades were examined: (1) current biotope grades, (2) upgrading second-grade areas to first-grade, (3) upgrading third-grade areas to second-grade, and (4) upgrading second and third-grade areas to first-grade. The study aimed to derive the efficiency of potential habitats in response to biotope grade increases under each scenario.Results: The results of the biotope grade upward scenarios showed an increase in efficiency of 81% for Scenario(2), 38.6% for Scenario (3), and 54.6% for Scenario (4). This reveals the efficiency of potential habitat expansion for urban birds with improved biotope grades. The findings underscore the significant role of biotope grade enhancement in securing bird habitats within the city.Conclusion: Focusing on birds in Seoul, this study concluded that higher biotope ratings significantly increase potential habitats. Limitations of the study include the omission of specific bird species when considering environmental variables and the lack of reflection of habitat characteristics like meteorological factors. Future research aims to develop a more in-depth and accurate model for evaluating the efficiency of biotope rating change scenarios.

What explains the high island endemicity of Philippine Rafflesia? A species distribution modeling analysis of three threatened parasitic plant species and their hosts.

Rafflesia are rare holoparasitic plants. In the Philippines, all but one species are found only on single islands. This study aimed to better understand the factors contributing to this distributional pattern. Specifically, we sought to determine whether narrow environmental tolerances of host and/or parasite species might explain their island endemicity. We used Maxent species distribution modeling to identify areas with suitable habitat for R. lagascae, R. lobata, and R. speciosa and their Tetrastigma host species. These analyses were carried out for current climate conditions and two future climate change scenarios. Although species distribution models indicated suitable environmental conditions for the Tetrastigma host species in many parts of the Philippines, considerably fewer areas were inferred to have suitable conditions for the three Rafflesia species. Some of these areas are on islands from which they have not been reported. All three species will face significant threats as a result of climate change. Our results suggest that limited inter-island dispersibility and/or specific environmental requirements are likely responsible for the current pattern of island endemicity of the three Rafflesia species, rather than environmental requirements of their Tetrastigma host species.

Application of MaxEnt modeling to evaluate the climate change effects on the geographic distribution of Lippia javanica (Burm.f.) Spreng in Africa.

Lippia javanica is a typical indigenous plant species mostly found in the higher elevation or mountainous regions in southern, central, and eastern Africa. The ongoing utilization of the species for ethnobotanical applications and traditional uses, coupled with the changing climate, increases the risk of a potential reduction in its geographic distribution range in the region. Herein, we utilized the MaxEnt species distribution modelling to build the L. javanica distribution models in tropical and subtropical African regions for current and future climates. The MaxEnt models were calibrated and fitted using 286 occurrence records and six environmental variables. Temperatures, including temperature seasonality [Bio 4] and the maximum temperature of the warmest month [Bio 5], were observed to be the most significant determinants of L. javanica's distribution. The current projected range for L. javanica was estimated to be 2,118,457 km2. Future model predictions indicated that L. javanica may increase its geographic distribution in western areas of the continent and regions around the equator; however, much of the geographic range in southern Africa may shift southwards, causing the species to lose portions of the northern limits of the habitat range. These current findings can help increase the conservation of L. javanica and other species and combat localized species loss induced by climate change and human pressure. We also emphasize the importance of more investigations and enhanced surveillance of traditionally used plant species in regions that are acutely susceptible to climate change.

Modelling landscape vulnerability to the Bracken fern (Pteridium aquilinum) invasion in a remnant urban Sandstone Sourveldt grassland ecosystem

The Bracken fern (Pteridium aquilinum) is one of the world's most pervasive alien invasive plants, exerting adverse effects on the sustainability and balance of remnant urban green ecosystems and thereby threatening the provision of valuable ecological goods and services in urban landscapes. To identify vulnerable landscapes and future distribution of the fern, this study aimed to model landscape vulnerability to the Bracken fern invasion using bio-physical, climatic, and remotely sensed data using Maxent species distribution model. Furthermore, the study sought to evaluate the value of variable selection on Maxent's modelling performance. Vulnerability to invasion was modelled based on four scenarios of different bio-climatic, physical and remotely sensed data inputs and model performance assessed using the area under the receiver operator characteristic curve (AUC). The results show that Model 1 (topographic variables) and Model 2 (topographic variables and Sentinel-2 spectral bands) predicted the western part of the eThekwini Municipal as highly suitable for the Bracken fern invasion, while Model 3 (topographic and bio-climatic variables) and Model 4 (topographic variables, Sentinel-2 spectral bands and bio-climatic variables), i.e., all variables predicted the central and western parts of the area as highly vulnerable to the Bracken fern invasion. Results also showed that elevation and rainfall wettest month (Bio13) were the most influential variables in determining the potential spread of the Bracken fern invasion and the model built using solely topographic and all the variables had the lowest (AUC of 0.895) and highest (AUC of 0.978) performance. The findings of this study are valuable to planning conservation initiatives in the often-vulnerable urban green ecology and managing invasive species in the remnant urban green spaces.

Forest herb species with similar European geographic ranges may respond differently to climate change

Many phenological studies have shown that spring geophytes are very sensitive to climate change, responding by shifting flowering and fruiting dates. However, there is a gap in knowledge about climatic drivers of their distributions and range shifts under climate change. Here we aimed to estimate climate niche shifts for four widely distributed and common geophytes of the nemoral zone of Europe (Anemone nemorosa, Anemone ranunculoides, Convallaria majalis and Maianthemum bifolium) and to assess the threat level under various climate change scenarios. Using MaxEnt species distribution models and future climate change scenarios we found that the precipitation of the warmest quarter was the most important factor shaping their ranges. All species studied will experience more loss in the 2061–2080 period than in 2041–2060, and under more pessimistic scenarios. M. bifolium will experience the highest loss, followed by A. nemorosa, A. ranunculoides, and the smallest for C. majalis. A. ranunculoides will gain the most, while M. bifolium will have the smallest potential range expansion. Studied species may respond differently to climate change despite similar current distributions and climatic variables affecting their potential distribution. Even slight differences in climatic niches could reduce the overlap of future ranges compared to present. We expect that due to high dependence on the warmest quarter precipitation, summer droughts in the future may be particularly severe for species that prefer moist soils. The lack of adaptation to long-distance migration and limited availability of appropriate soils may limit their migration and lead to a decline in biodiversity and changes in European forests.

Risks, resources, and refugia: Spatial overlap between yellow-eyed penguin foraging distribution and prey, commercial fisheries, and marine protected areas

Conservation of the endangered yellow-eyed penguin (Megadyptes antipodes) in New Zealand has principally sought to manage terrestrial threats, while relatively little has been done to understand or address marine threats, such as reduced prey availability, commercial fisheries interactions, and habitat destruction. We assessed spatial similarities between mainland yellow-eyed penguin marine distribution and resources (prey), risks (fisheries interactions), and areas of refugia (marine protected areas, MPAs). We determined if suitable penguin foraging habitat, based on environmental predictors using a Maxent species distribution model (SDM), also supports a high diversity of key prey species identified using a stacked SDM. We also created a novel index to predict areas of potential commercial fisheries interactions and mapped the overlap of penguin distribution and MPAs. Areas along the middle of the continental shelf had the highest prey diversity and probability of penguin presence, which overlapped with gillnet fisheries in these regions. Suitable penguin habitat also overlaps with trawl fisheries inshore along much of the South Island coast. <1 % of the penguin range overlaps with current MPAs, and the proposed South-East Marine Protected Areas network would protect only 3.6 % of the current penguin foraging distribution. This study takes an ecosystem approach to assess complex interactions between commercial fisheries, marine ecosystems, and MPAs, which is urgently required for marine spatial planning and adaptive ecosystem management of not only this endangered seabird but for southeast New Zealand coastal habitat and biota also.

A new analytical framework for Maxent species distribution models unveils complex spatiotemporal suitability patterns for two migratory seedeaters (Aves: Sporophila) of conservation concern

Maxent species distribution models (SDMs) for rare, migratory taxa can be hindered by methodological and analytical issues. To overcome these problems, we developed an analytical framework to build SDMs for key stages of the annual lifecycle of the Marsh Seedeater and the Black-bellied Seedeater, two Neotropical grassland bird species of conservation concern. Occurrence data were compiled from multiple sources and ten environmental variables were used as predictors. We built SDMs with different spatial partitions, feature classes, and regularization multipliers, and devised a procedure to select models with high discrimination ability, low overfitting, statistically significant performance metrics, and high biological realism. From a total of 992 SDMs, 22 fully met the selection criteria for both species. These selected SDMs were equally or less overfit than non-selected ones and were mostly trained with the checkerboard 2 and n − 1 jackknife partitions. For each modeling scenario, we projected and interpreted composite models that emphasized areas of both consensus and divergence across individual predictions, rather than choosing a single model from the set of final SDMs. In the breeding season, areas of highest suitability occurred in restricted, disjunct sectors of the Campos and Pampas grasslands for the Marsh Seedeater and the highland grasslands of southern Brazil for the Black-bellied Seedeater. In the winter, high suitability for the Marsh Seedeater occurred in the western and northern Cerrado and the Pantanal, while in the spring migration, high suitability for the Black-bellied Seedeater was mostly concentrated in the southeastern portion of the Cerrado. The restricted breeding distribution of the Black-bellied Seedeater suggests that its conservation status should be reviewed. Both species shift their climatic niches and track their habitats throughout the year, responding to a few topographic, land-use, and climatic variables that represent different niche components. By addressing major modeling issues in a three-step model selection procedure, we were able to project precise and biologically interpretable SDMs with the level of complexity required for each modeling scenario. Our framework is readily replicable and can be used to unravel intricate spatial suitability patterns of organisms whose distribution undergoes temporal shifts throughout the year.

Climate change will cause climatic niche contraction of Vaccinium myrtillus L. and V. vitis-idaea L. in Europe

We estimated climate niche shifts and threat levels under various climate change scenarios for Vaccinium myrtillus L. and V. vitis-idaea L. We developed the MaxEnt species distribution models, and predicted future climatic optima for climate change scenarios for 2041–2060 and 2061–2080. The precipitation of the warmest quarter was the most important factor shaping the climatic niches of the studied species. We predicted the largest shifts in climate niches from the present to the 2040–2060 period, with the most pessimistic scenario predicting significant range losses for both species, mainly in Western Europe. Under the most optimistic SSP126 scenario, both species will lose 39 % of their climatic niche for both periods. In the worst-case scenario (SSP585) for 2061–2080, climatic niche contraction will cover 47 % and 39 % of the current climatic niche for V. myrtillus and V. vitis-idaea, respectively. The predicted changes in species distribution could have far-reaching consequences for temperate and boreal forests due to their crucial biocenotic role in forest ecosystems, high potential for carbon sequestration, and prevention of soil erosion. Furthermore, the changes would likely affect the economic potential regarding fruit production and culturally relevant uses of different parts of the plants, mainly fruits.

Evaluating sampling bias correction methods for invasive species distribution modeling in Maxent

As globalization continues, the spread of invasive species is accelerating, posing a severe threat to native biodiversity. To manage such species, reduce their negative impact on native biota and utilize management costs efficiently, a profound understanding of their geographical distribution pattern is mandatory. In this study, the species distribution model Maxent was used to predict the potential spatial distribution of U. europaeus. To account for sampling bias, three bias correction methods were applied, including a novel approach to increase the number of presence points by sampling occurrences based on satellite images. Furthermore, a decision structured process was used to evaluate and select optimal Maxent parameterization and account for limitations of single evaluation criteria. The currently suitable area of U. europaeus is primarily distributed in the coastal and central regions of Chilean natural region Zona Sur in south-central Chile. Annual mean temperature (bio1), annual precipitation (bio12), and precipitation seasonality (bio15) were the most important environmental variables that affected the distribution of U. europaeus. The sampling of additional presence points could effectively correct for sampling bias in species occurrence data. The use of a decision structured process for model evaluation proved to be useful in determining optimal model parameterization for decreased model complexity. This study highlights the importance of optimized Maxent calibrations to yield results as accurately as possible. The predicted suitable habitats can inform nature conservation planners and landscape managers to guide and prioritize conservation measures.

Evaluating the robustness of nature-based solutions: future resilience of sedum-based soft capping as a conservation approach for heritage sites in Britain and Ireland

ABSTRACT Nature-based solutions are seen to benefit both society and biodiversity. However, research into their future resilience is required. Soft capping is a nature-based conservation strategy that mimics the natural colonisation of plants on top of ruined walls and reduces rates of material deterioration. To remain effective, soft capping species must be tolerant of future climatic conditions. We use the Maxent species distribution model to assess the resilience of soft capping species to low and high emission scenarios across Britain and Ireland. We assess the mid- and end-century presence probability of four native and archaeophyte species used in soft capping (Sedum album, S. acre, S. anglicum and Saxifraga granulata). Future presence probabilities were calculated using the climate models HadGEM3-GC31-LL, IPSL-CM6A-LR and MIROC6. Results suggest that current sedum-based soft caps will remain viable until mid-century with additional maintenance (e.g. watering) during droughts, although soft caps predominantly formed of Sedum album may be prone to failure in south-eastern England. In the future, species more resilient to arid conditions may need to be preferentially selected for soft capping to ensure resilience under a warming climate. Species distribution modelling provides a useful way of predicting the future resilience of nature-based solutions.

Assessing high conservation value areas for rare, endemic and threatened (RET) species: A study in high altitude Changthang landscape of India

The Forest Stewardship Council developed the concept of High Conservation Values (HCVs) as a criteria in the forest certification process in order to promote sustainable forest management. It has six major components or values and component one and two of HCVs deal with the habitat for viable populations of “rare, endemic and threatened (RET) species” using the IUCN Red List category and other national / regional / local lists. But a consistent robust methodology for identification of these areas, does not exist. The present study tried to develop for the first time, a straight forward inclusive methodology for identification of HCVAs for the RET species on a spatio-temporal scale. A total of 50 RET and other significant species (32 flora, 10 fauna and 8 avifauna) were identified after a thorough literature review, field surveys and consultations with experts. Occurrence data of the selected species was collected from different secondary sources, field surveys, institutes and scientists who have worked on them. A 10 km grid-based approach and stratified random sampling was used for the primary GPS field surveys conducted during 2018–2019. MaxEnt species distribution model (SDM) software was used based on the occurrence data and environmental variables for identification of potential suitable habitats for the selected species. Linear support vector machine (LSVM) model was used for assessing the performance of the SDMs. The performance of each SDM has been validated through Cohen's Kappa (KAPPA), true skill statistic (TSS) and receiver operating characteristics (ROC) models. The proposed methodology addresses the urgent need for a holistic and robust set of techniques to apply the HCV toolkit. This is key to identify and map HCVAs for RET species at the landscape level and can be easily adapted to and adopted at the national, regional, state or local level in India. The methods offer an efficient, reliable approach for the application of the HCV concept, elsewhere in the world.

Evaluation of Habitat Suitability for Asian Elephants in Sipsongpanna under Climate Change by Coupling Multi-Source Remote Sensing Products with MaxEnt Model

The Asian elephant (Elephas maximus Linnaeus) is a globally endangered species, an internationally protected species, and a first-class protected animal in China. However, future climate change and human activities exacerbate the instability of its habitat range, leading to a possible reduction in the range. By using multi-source remote sensing data and products, as well as climate change models, including ASTER GDEM v3, Landsat8 OLI image and ClimateAP, we examined the effects of ecological factors related to climate and natural and anthropogenic influences on the distribution of Asian elephants in Sipsongpanna. Multiyear elephant field tracking data were used with a MaxEnt species distribution model and the climate model. First, the distribution of Asian elephants in potentially suitable areas in Sipsongpanna was simulated under current climatic conditions without considering human activities. The predicted distribution was verified by existing Asian elephant migration trajectories. Subsequently, the distribution of potentially suitable areas for Asian elephants in Sipsongpanna was simulated under two climate change scenarios (RCP4.5, RCP8.5) in three periods (2025, 2055, and 2085). The changes in potentially suitable areas for Asian elephants in Sipsongpanna were analyzed under multiple climate change scenarios for the current (2017) and different future periods by considering the effects of human activities. The results show the following: (1) under anthropogenic interference (AI), the optimal MaxEnt model has a high prediction accuracy with the area under the curve (AUC) of 0.913. The feature combination (FC) includes linear, quadratic, and threshold features, and the regularization multiplier (RM) is 2.1. (2) Jackknife analyses of the non-anthropogenic interference (NAI) and anthropogenic interference (AI) scenarios indicate that topography (altitude (Alt)), temperature (mean warmest month temperature (MWMT)), and precipitation (mean annual precipitation (MAP)) are the top three factors influencing the distribution of Asian elephants. (3) The total area suitable for Asian elephants under current climate conditions and AI accounts for 46.35% of the total area. Areas of high suitability (occurrence probability &gt;0.5) are located in Jinghong City in central Sipsongpanna and Mengla County in southeastern Sipsongpanna. Among them, the minimum habitat range and ecological corridors are mainly located in Mengman Town, Mohan Town, Mengla Town, Mengban Township, Dadugang Township, and Mengwang Township. (4) The change in potentially suitable areas for Asian elephants between current and future conditions is small under AI and large under undisturbed conditions.

Distribution Modeling and Gap Analysis of Shorebird Conservation in Northern Brazil

Migratory shorebirds are currently at significant risk, with populations of multiple species dropping globally, often due to habitat disruption and loss. The coast of northern Brazil, along the states of Pará and Maranhão, encompasses one of the broadest expanses of shorebird overwintering habitat for many shorebird species in the Atlantic Americas Flyway. Delineating how current management protects shorebird habitat is an important first step to identifying gaps in protections that then need to be prioritized. Different frameworks of conservation decision-making, such as focusing on flagship, umbrella, or biodiversity, can be used to prioritize what habitat needs to be protected. Understanding the effects of these different conservation perspectives is key to making informed management decisions. Multiple dates of Landsat 8 imagery at different tidal stages were used to identify intertidal habitats for further analysis. MaxEnt species distribution models were derived for each species using remote sensing and landscape metrics, as well as point-count survey data collected during the winters of 2016 and 2017. Landsat 8 and Sentinel 2 image data were included as direct inputs to the model rather than interpreted and mapped first into different habitat types. The resulting Maxent models successfully delineated between presence and absence locations for five of the eight target shorebird species. The model outputs were combined to map the intertidal habitat covered under several different management scenarios. These scenarios included using red knots as a flagship species, semipalmated sandpipers as an umbrella species, and species richness as a metric of shorebird diversity. Slightly more than 25% of delineated intertidal habitat was found to be included within a designated Marine Extractive Reserve, with three major gaps in current protections identified. The flagship, umbrella, and biodiversity perspectives result in different prioritizations of these distinct locations. While umbrella and diversity approaches are successful at protecting diverse communities, community assemblages of rarer or specialist species, such as the red knots, may fall outside their protective shadows. A hybrid approach that uses both diversity and the extent of rare flagship species produces results that best meet the management goals of identifying gaps in existing conservation to protect the most at-risk species while conserving the diverse assemblages they coexist with.

New Zealand Fern Distributions from the Last Glacial Maximum to 2070: A Dynamic Tale of Migration and Community Turnover

The coming decades are predicated to bring widespread shifts in local, regional, and global climatic patterns. Currently there is limited understanding of how ferns will respond to these changes and few studies have attempted to model shifts in fern distribution in response to climate change. In this paper, we present a series of these models using the country of New Zealand as our study system. Ferns are notably abundant in New Zealand and play important ecological roles in early succession, canopy biology, and understory dynamics. Here we describe how fern distributions have changed since the Last Glacial Maximum to the present and predict how they will change with anthropogenic climate change – assuming no measures are taken to reduce carbon emissions. To do this, we used MaxEnt species distribution modelling with publicly available data from gbif.org and worldclim.org to predict the past, present, and future distributions of 107 New Zealand fern species. The present study demonstrates that ferns in New Zealand have and will continue to expand their ranges and migrate southward and upslope. Despite the predicted general increased range size as a result of climate change, our models predict that the majority (52%) of many species' current suitable habitats may be climatically unsuitable in 50 years, including the ecologically important group: tree ferns. Additionally, fern communities are predicted to undergo drastic shifts in composition, which may be detrimental to overall ecosystem functioning in New Zealand.