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Climate change poses a serious threat to biodiversity. Predicting the effects of climate change on the distribution of a species' habitat can help humans address the potential threats which may change the scope and distribution of species. Pterocarya stenoptera is a common fast‐growing tree species often used in the ecological restoration of riverbanks and alpine forests in central and eastern China. Until now, the characteristics of the distribution of this species' habitat are poorly known as are the environmental factors that influence its preferred habitat. In the present study, the Maximum Entropy Modeling (Maxent) algorithm and the Genetic Algorithm for Ruleset Production (GARP) were used to establish the models for the potential distribution of this species by selecting 236 sites with known occurrences and 14 environmental variables. The results indicate that both models have good predictive power. Minimum temperature of coldest month (Bio6), mean temperature of warmest quarter (Bio10), annual precipitation (Bio12), and precipitation of driest month (Bio14) were important environmental variables influencing the prediction of the Maxent model. According to the models, the temperate and subtropical regions of eastern China had high environmental suitability for this species, where the species had been recorded. Under each climate change scenario, climatic suitability of the existing range of this species increased, and its climatic niche expanded geographically to the north and higher elevation. GARP predicted a more conservative expansion. The projected spatial and temporal patterns of P. stenoptera can provide reference for the development of forest management and protection strategies. Minimum temperature of coldest month (Bio6), mean temperature of warmest quarter (Bio10), annual precipitation (Bio12), and precipitation of driest month (Bio14) were important environmental variables influencing the prediction of the Maxent model. Under each climate change scenario, climatic suitability of the existing range of this species increased, and its climatic niche expanded geographically toward farther north and higher elevation. The GARP predicted a more conservative expansion.

BACKGROUND: The Triatoma brasiliensis complex is a monophyletic group, comprising three species, one of which includes two subspecific taxa, distributed across 12 Brazilian states, in the caatinga and cerrado biomes. Members of the complex are diverse in terms of epidemiological importance, morphology, biology, ecology, and genetics. Triatoma b. brasiliensis is the most disease-relevant member of the complex in terms of epidemiology, extensive distribution, broad feeding preferences, broad ecological distribution, and high rates of infection with Trypanosoma cruzi; consequently, it is considered the principal vector of Chagas disease in northeastern Brazil. METHODS: We used ecological niche models to estimate potential distributions of all members of the complex, and evaluated the potential for suitable adjacent areas to be colonized; we also present first evaluations of potential for climate change-mediated distributional shifts. Models were developed using the GARP and Maxent algorithms. RESULTS: Models for three members of the complex (T. b. brasiliensis, N = 332; T. b. macromelasoma, N = 35; and T. juazeirensis, N = 78) had significant distributional predictivity; however, models for T. sherlocki and T. melanica, both with very small sample sizes (N = 7), did not yield predictions that performed better than random. Model projections onto future-climate scenarios indicated little broad-scale potential for change in the potential distribution of the complex through 2050. CONCLUSIONS: This study suggests that T. b. brasiliensis is the member of the complex with the greatest distributional potential to colonize new areas: overall; however, the distribution of the complex appears relatively stable. These analyses offer key information to guide proactive monitoring and remediation activities to reduce risk of Chagas disease transmission.

A predictive Fasciola hepatica model, based on the growing degree day-water budget (GDD-WB) concept and the known biological requirements of the parasite, was developed within a geographical information system (GIS) in Colombia. Climate-based forecast index (CFI) values were calculated and represented in a national-scale, climate grid (18 x 18 km) using ArcGIS 9.3. A mask overlay was used to exclude unsuitable areas where mean annual temperature exceeded 25 °C, the upper threshold for development and propagation of the F. hepatica life cycle. The model was then validated and further developed by studies limited to one department in northwest Colombia. F. hepatica prevalence data was obtained from a 2008-2010 survey in 10 municipalities of 6,016 dairy cattle at 673 herd study sites, for which global positioning system coordinates were recorded. The CFI map results were compared to F. hepatica environmental risk models for the survey data points that had over 5% prevalence (231 of the 673 sites) at the 1 km2 scale using two independent approaches: (i) a GIS map query based on satellite data parameters including elevation, enhanced vegetation index and land surface temperature day-night difference; and (ii) an ecological niche model (MaxEnt), for which geographic point coordinates of F. hepatica survey farms were used with BioClim data as environmental variables to develop a probability map. The predicted risk pattern of both approaches was similar to that seen in the forecast index grid. The temporal risk, evaluated by the monthly CFIs and a daily GDD-WB forecast software for 2007 and 2008, revealed a major July-August to January transmission period with considerable inter-annual differences.

In most instances, the spatial distribution of infectious disease cases or pathogens is not random. This is particularly true for diseases with etiologic agents that are dependent on vector‐borne transmission, or have zoonotic or environmental reservoirs. This chapter highlights the use of geographic information systems and spatial analysis to support infectious disease surveillance of Cryptococcus gattii, an emerging fungal pathogen recently detected in the Pacific Northwest of North America, and Yersinia pestis, an age‐old vector‐borne zoonosis that is still present in many parts of the world. Spatial risk modeling of areas posing an elevated risk of exposure to C. gattii and Y. pestis based on ecologic, climatic, and topographical variables is described, and the challenges of working with geographically indexed disease surveillance data are discussed in the case studies.

This book provides a first synthetic view of an emerging area of ecology and biogeography, linking individual- and population-level processes to geographic distributions and biodiversity patterns. Problems in evolutionary ecology, macroecology, and biogeography are illuminated by this integrative view. The book focuses on correlative approaches known as ecological niche modeling, species distribution modeling, or habitat suitability modeling, which use associations between known occurrences of species and environmental variables to identify environmental conditions under which populations can be maintained. The spatial distribution of environments suitable for the species can then be estimated: a potential distribution for the species. This approach has broad applicability to ecology, evolution, biogeography, and conservation biology, as well as to understanding the geographic potential of invasive species and infectious diseases, and the biological implications of climate change. The authors lay out conceptual foundations and general principles for understanding and interpreting species distributions with respect to geography and environment. Focus is on development of niche models. While serving as a guide for students and researchers, the book also provides a theoretical framework to support future progress in the field.

Background Dengue is an acute infectious tropical disease that poses a significant public health burden in the Philippines; however, studies employing spatial distribution modeling and ecological approaches to analyze dengue occurrence data remain limited. This study aims to determine the high-risk areas suitable for dengue occurrence and its determinants in the Philippines. Methods Dengue case data from 2017 to 2024 were analyzed using kernel density estimation (KDE) and inverse distance weighting (IDW) spatial interpolation to characterize spatial intensity and estimate incidence in unsampled areas. An ecological niche model was developed using maximum entropy modeling, implemented through the MaxEnt software, with climatic, environmental, and socioeconomic predictors. Model performance was evaluated using the area under the curve (AUC), and predictor importance was assessed using jackknife testing. Results Results show highest intensity in 2019 and consistent high case density in the National Capital Region (NCR). Meanwhile, high predicted incidence rates were consistently exhibited in northern Luzon. The maximum entropy model had a strong performance in predicting the suitable areas for dengue with a mean area under curve (AUC) of 0.847. Nighttime lights (32.3%), land cover (31.1%), and population density (9.4%) significantly contributed to the model. The NCR was found to be a high-risk suitable area for dengue occurrence along with some parts of other provinces. Conclusions This study represents the first application of ecological niche modeling to dengue in the Philippines. The integration of KDE, IDW, and maximum entropy model provides a robust framework for identifying high-risk areas and key determinants, emphasizing the role of urbanization in dengue distribution. These findings are valuable to authorities for an informed risk-based surveillance, genotype-specific monitoring, and decision-making for geospatially targeted disease risk management. Graphical Abstract

The current range of ecological habitats occupied by a species reflects a combination of the ecological tolerance of the species, dispersal limitation, and competition. Whether the current distribution of a species accurately reflects its niche has important consequences for the role of ecological niche modeling in predicting changes in species ranges as the result of biological invasions and climate change. We employed a detailed data set of species occurrence and spatial variation in biotic and abiotic attributes to model the niche of a native California annual plant, Collinsia sparsiflora. We tested the robustness of our model for both the realized and fundamental niche by planting seeds collected from four populations, representing two ecotypes, into plots that fully represented the five-dimensional niche space described by our model. The model successfully predicted which habitats allowed for C. sparsiflora persistence, but only for one of the two source ecotypes. Our results show that substantial niche divergence has occurred in our sample of four study populations, illustrating the importance of adequately sampling and describing within-species variation in niche modeling.