Modeling the response of an endangered flagship predator to climate change in Iran

Unraveling the unknown: Adaptive spatial planning to enhance climate resilience for the endangered Swamp Grass-babbler (Laticilla cinerascens) with habitat connectivity and complexity approach

The endangered and poorly known Swamp Grass-babbler, Laticilla cinerascens (Passeriformes: Pellorneidae), confronts critical threats and vulnerability due to its specific habitat requirements and restricted populations in the northeastern region of the Indian Subcontinent. This study investigates the distribution of the species, habitat quality, geometry and shape complexity of connectivity among the protected areas (PAs), and responses to climate change in Northeast India under different climate change pathways by utilizing ensemble distribution models, and ecological metrics. From the total distribution extent (1,42,000 km2), approximately 9366 km2 (6.59 %) is identified as the suitable habitat for this threatened species. Historically centered around Dibru Saikhowa National Park (DSNP), the species faced a drastic decline due to anthropogenic activities and alteration in land use and lover cover. The study also reveals a significant decline in suitable habitat for L. cinerascens in future climate scenarios, with alarming reductions under SSP126 (>10 % in the timeframe 2041-2060 and > 30 % from 2061 to 2080), SSP245 (>90 % in both time periods), and SSP585 (>90 % in both timeframes) from the present scenario. At present, DSNP has the most suitable habitat within the distribution range but is projected to decline (>90 %) under more severe climate change scenarios, as observed in other PAs. Landscape fragmentation analysis indicates a shift in habitat geometry, highlighting the intricate impact of climate change. It predicts a substantial 343 % increase (in the SSP126) in small habitat patches in the future. Connectivity analysis among PAs shows a significant shift, with a decline exceeding 20 %. The analysis of shape complexity and connectivity geometry reveals a significant increase of over 220 % in the fragmentation of connectivity among PAs between 2061 and 2080 under the SSP585 climate change scenario compared to the present conditions. The study underscores the urgent need for conservation actions, emphasizing the complex interplay of climate change, habitat suitability, and fragmentation. Prioritizing PAs with suitable habitats and assessing their connectivity is crucial. Adaptive management strategies are essential to address ongoing environmental changes and safeguard biodiversity. Future research in critical areas is needed to establish long-term monitoring programs to lead/extend effective conservation strategies.

An ensemble modeling approach to predict spatial risk patches of the Persian leopard-livestock conflicts in Lorestan Province, Iran

This study was conducted in the Lorestan Province in the west of Iran with two objectives of identifying major environmental variables in spatial risk modeling and identifying spatial risk patches of livestock predation by the Persian leopard. An ensemble approach of three models of maximum entropy (MaxEnt), generalized boosting model (GBM), and random forest (RF) were applied for spatial risk modeling. Our results revealed that livestock density, distance to villages, forest density, and human population density were the most important variables in spatial risk modeling of livestock predation by the leopard. The center of the study area had the highest probability of livestock predation by the leopard. Ten spatial risk patches of livestock predation by the leopard were identified in the study area. In order to mitigate the revenge killing of the leopards, the findings of this study highlight the imperative of implementing strategies by the Department of Environment (DoE) to effectively accompany the herds entering the wildlife habitats with shepherds and a manageable number of guarding dogs. Accordingly, the identified risk patches in this study deserve considerable attention, especially three primary patches found in the center and southeast of Lorestan Province.

Modeling climate change impacts on the distribution of an endangered brown bear population in its critical habitat in Iran

Climate change is one of the major challenges to the current conservation of biodiversity. Here, by using the brown bear, Ursus arctos, in the southernmost limit of its global distribution as a model species, we assessed the impact of climate change on the species distribution in western Iran. The mountainous forests of Iran are inhabited by small and isolated populations of brown bears that are prone to extinction in the near future. We modeled the potential impact of climate change on brown bear distribution and habitat connectivity by the years 2050 and 2070 under four representative concentration pathways (RCPs) of two general circulation models (GCMs): BCC-CSM1-1 and MRI-CGCM3. Our projections revealed that the current species' range, which encompasses 6749.8 km² (40.8%) of the landscape, will decline by 10% (2050: RCP2.6, MRI-CGCM3) to 45% (2070: RCP8.5, BCC-CSM1-1). About 1850 km² (27.4%) of the current range is covered by a network of conservation (CAs) and no-hunting (NHAs) areas which are predicted to decline by 0.64% (2050: RCP2.6, MRI-CGCM3) to 15.56% (2070: RCP8.5, BCC-CSM1-1) due to climate change. The loss of suitable habitats falling within the network of CAs and NHAs is a conservation challenge for brown bears because it may lead to bears moving outside the CAs and NHAs and result in subsequent increases in the levels of bear-human conflict. Thus, re-evaluation of the network of CAs and NHAs, establishing more protected areas in suitable landscapes, and conserving vital linkages between habitat patches under future climate change scenarios are crucial strategies to conserve and manage endangered populations of the brown bear.

Combining multiscale niche modeling, landscape connectivity, and gap analysis to prioritize habitats for conservation of striped hyaena (Hyaena hyaena)

Identifying spatial gaps in conservation networks requires information on species-environment relationships, and prioritization of habitats and corridors. We combined multi-extent niche modeling, landscape connectivity, and gap analysis to investigate scale-dependent environmental relationships, and identify core habitats and corridors for a little-known carnivore in Iran, the striped hyaena (Hyaena hyaena). This species is threatened in Iran by road vehicle collisions and direct killing. Therefore, understanding the factors that affect its habitat suitability, spatial pattern of distribution, and connectivity among them are prerequisite steps to delineate strategies aiming at human-striped hyaena co-existence. The results showed that the highest predictive power and extent of habitats was obtained at the extent sizes of 4 and 2 km, respectively. Also, connectivity analysis revealed that the extent and number of core habitats and corridors changed with increasing dispersal distance, and approximately 21% of the landscape was found to support corridors. The results of gap analysis showed that 15–17% of the core habitats overlapped with conservation areas. Given the body size of the species, its mobility, and lack of significant habitat specialization we conclude that this species would be more strongly influenced by changes in habitat amount rather than landscape configuration. Our approach showed that the scale of variables and dispersal ability must be accounted for in conservation efforts to prioritize habitats and corridors, and designing conservation areas. Our results could facilitate the conservation of striped hyaena through the identification and prioritization of habitats, establishment of conservation areas, and mitigating conflicts in corridors.

Habitat suitability and connectivity implications for the conservation of the Persian leopard along the Iran-Iraq border

Habitat fragmentation has major negative impacts on wildlife populations, and the connectivity could reduce these negative impacts. This study was conducted to assess habitat suitability and structural connectivity of the Persian leopard along the Iran-Iraq border (i.e., the Zagros Mountains) and compare the situation of identified core habitats and connectivity with existing conservation areas (CAs). An ensemble modeling approach resulting from five models was used to predict habitat suitability. To identify core habitats and corridors along the Iran-Iraq border, factorial least-cost path analyses were applied. The results revealed that topographic roughness, distance to CAs, annual precipitation, vegetation/cropland density, and distance to rivers were the most influential variables for predicting the occurrence of the Persian leopard in the study area. By an estimated dispersal distance of 82 km (suggested by previous studies), three core habitats were identified (two cores in Iran and one core in Iraq). The largest cores were located in the south and the center of the study area, which had the highest connectivity priorities. The connectivity from these cores was maintained to the core within the Iraqi side. Only about one-fifth of detected core habitats and relative corridors were protected by CAs in the study area. Detected core habitats and connectivity areas in this study could be an appropriate road map to accomplish the CAs network along the Iran-Iraq border regarding Persian leopard conservation. Establishing transboundary CAs, particularly in the core habitat located in the center of the study area, is strongly recommended to conserve existing large carnivores, including the Persian leopard.

Adaptive spatial planning of protected area network for conserving the Himalayan brown bear

Large mammals that occur in low densities, particularly in the high-altitude areas, are globally threatened due to fragile climatic and ecological envelopes. Among bear species, the Himalayan brown bear (Ursus arctos isabellinus) has a distribution that is restricted to Himalayan highlands with relatively small and fragmented populations. To date, very little scientific information on the Himalayan brown bear, which is vital for the conservation of the species and the management of its habitats, especially in protected areas of the landscape, is available. The present study aims to understand the effectiveness of existing Himalayan Protected Areas in terms of representativeness for the conservation of Himalayan brown bear (HBB), an umbrella species in high-altitude habitats of the Himalayan region. We used the ensemble approach of the species distribution model and then assessed biological connectivity to predict the current and future distribution and movement of HBB in climate change scenarios for the year 2050. Approximately 33 protected areas (PAs) currently possess suitable habitats. Our model suggests a massive decline of approximately 73.38% and 72.87% under 4.5 and 8.5 representative concentration pathway (RCP) respectively in the year 2050 compared with the current distribution. The predicted change in suitability will result in loss of habitats from thirteen PAs; eight will become completely uninhabitable by the year 2050, followed by loss of connectivity in the majority of PAs. Habitat configuration analysis suggested a 40% decline in the number of suitable patches, a reduction in large habitat patches (up to 50%) and aggregation of suitable areas (9%) by 2050, indicating fragmentation. The predicted change in geographic isotherm will result in loss of habitats from thirteen PAs, eight of them will become completely inhabitable. Hence, these PAs may lose their effectiveness and representativeness in achieving the very objective of their existence or conservation goals. Therefore, we recommend adaptive spatial planning for protecting suitable habitats distributed outside the PA for climate change adaptation.

Spatial Prediction of Future Flood Risk: An Approach to the Effects of Climate Change

Preparation of a flood probability map serves as the first step in a flood management program. This research develops a probability flood map for floods resulting from climate change in the future. Two models of Flexible Discrimination Analysis (FDA) and Artificial Neural Network (ANN) were used. Two optimistic (RCP2.6) and pessimistic (RCP8.5) climate change scenarios were considered for mapping future rainfall. Moreover, to produce probability flood occurrence maps, 263 locations of past flood events were used as dependent variables. The number of 13 factors conditioning floods was taken as independent variables in modeling. Of the total 263 flood locations, 80% (210 locations) and 20% (53 locations) were considered model training and validation. The Receiver Operating Characteristic (ROC) curve and other statistical criteria were used to validate the models. Based on assessments of the validated models, FDA, with a ROC-AUC = 0.918, standard error (SE = 0.038), and an accuracy of 0.86% compared to the ANN model with a ROC-AUC = 0.897, has the highest accuracy in preparing the flood probability map in the study area. The modeling results also showed that the factors of distance from the River, altitude, slope, and rainfall have the greatest impact on floods in the study area. Both models’ future flood susceptibility maps showed that the highest area is related to the very low class. The lowest area is related to the high class.

Multi-scale habitat modelling and predicting change in the distribution of tiger and leopard using random forest algorithm

Tigers and leopards have experienced considerable declines in their population due to habitat loss and fragmentation across their historical ranges. Multi-scale habitat suitability models (HSM) can inform forest managers to aim their conservation efforts at increasing the suitable habitat for tigers by providing information regarding the scale-dependent habitat-species relationships. However the current gap of knowledge about ecological relationships driving species distribution reduces the applicability of traditional and classical statistical approaches such as generalized linear models (GLMs), or occupancy surveys to produce accurate predictive maps. This study investigates the multi-scale habitat relationships of tigers and leopards and the impacts of future climate change on their distribution using a machine-learning algorithm random forest (RF). The recent advancements in the machine-learning algorithms provide a powerful tool for building accurate predictive models of species distribution and their habitat relationships even when little ecological knowledge is available about the species. We collected species occurrence data using camera traps and indirect evidence of animal presences (scats) in the field over 2 years of rigorous sampling and used a machine-learning algorithm random forest (RF) to predict the habitat suitability maps of tiger and leopard under current and future climatic scenarios. We developed niche overlap models based on the recently developed statistical approaches to assess the patterns of niche similarity between tigers and leopards. Tiger and leopard utilized habitat resources at the broadest spatial scales (28,000 m). Our model predicted a 23% loss in the suitable habitat of tigers under the RCP 8.5 Scenario (2050). Our study of multi-scale habitat suitability modeling provides valuable information on the species habitat relationships in disturbed and human-dominated landscapes concerning two large felid species of conservation importance. These areas may act as refugee habitats for large carnivores in the future and thus should be the focus of conservation importance. This study may also provide a methodological framework for similar multi-scale and multi-species monitoring programs using robust and more accurate machine learning algorithms such as random forest.

Machine learning approaches for spatial modeling of agricultural droughts in the south-east region of Queensland Australia

A quantitative understanding of the hydro-environmental factors that influence the occurrence of agricultural drought events would enable more strategic climate change adaptation and drought management plans. Practical drought hazard mapping remains challenging due to possible exclusion of the most pertinent drought drivers, and to the use of inadequate predictive models that cannot describe drought adequately. This research aims to develop new approaches to map agricultural drought hazard with state-of-the-art machine learning models, including classification and regression trees (CART), boosted regression trees (BRT), random forests (RF), multivariate adaptive regression splines (MARS), flexible discriminant analysis (FDA) and support vector machines (SVM). Hydro-environmental datasets were used to calculate the relative departure of soil moisture (RDSM) for eight severe droughts for drought-prone southeast Queensland, Australia, over the period 1994-2013. RDSM was then used to generate an agricultural drought inventory map. Eight hydro-environmental factors were used as potential predictors of drought. The goodness-of-fit and predictive performance of all models were evaluated using different threshold-dependent and threshold-independent methods, including the true skill statistic (TSS), Efficiency (E), F-score, and the area under the receiver operating characteristic curve (AUC-ROC). The RF model (AUC-ROC = 97.7%, TSS = 0.873, E = 0.929, F-score = 0.898) yielded the highest accuracy, while the FDA model (with AUC-ROC = 73.9%, TSS = 0.424, E = 0.719, F-score = 0.512) showed the worst performance. The plant available water holding capacity (PAWC), mean annual precipitation, and clay content were the most important variables to be used for predicting the agricultural drought. About 21.2% of the area is in high or very high drought risk classes, and therefore, warrant drought and environmental protection policies. Importantly, the models do not require data on the precipitation anomaly for any given drought year; the spatial patterns in AGH were consistent for all drought events, despite very different spatial patterns in precipitation anomaly among events. Such machine-learning approaches are able to construct an overall risk map, thus assisting in the adoption of a robust drought contingency planning measure not only for this area, but also, in other regions where drought presents a pressing challenge, including its influence on key practical dimensions of social, environmental and economic sustainability.