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Climate change can significantly impact the ecological suitability and diversity of species. Pyrethrum tatsienense, a critically endangered species in China, requires a thorough understanding of its habitat distribution and the environmental factors that affect it in the context of climate change. The Maxent algorithm was used to examine the key factors influencing the distribution of P. tatsienense in China, using data from 127 species occurrences and environmental variables from the Last Interglacial (LIG), Last Glacial Maximum (LGM), Mid‐Holocene (MH), current, and future scenarios. The Maxent model was optimized utilizing the R package ENMeval, providing the most accurate predictions for suitable habitats across various scenarios. Results show that suitable regions for P. tatsienense encompass approximately 15.02% (14.42 × 105 km2) of China, predominantly on the Qinghai‐Tibetan Plateau. The mean UV‐B of the highest month (UVB3: 39.7%), elevation (elev: 28.7%), and the warmest season of precipitation (Bio18: 17.4%) are the major limiting factors for suitable habitat. The optimal species distribution ranges are identified as > 7500 J m−2 day−1 for UVB3, 2700–5600 m for elev, and 150–480 mm for Bio18. Predictions for the historical climate indicate the presence of refugia at the junction of Sichuan, Tibet, and Qinghai. The MH predictions show an increase in climatic suitability for P. tatsienense compared to the LIG and LGM, with an expansion of suitable areas westward. Future climate change scenarios indicate that the potential suitable habitat for P. tatsienense is expected to increase with increasing radiative forcing, with higher latitude regions becoming new marginally suitable habitats. However, predicted environmental changes in western Tibet may drive the loss of highly suitable habitats in the future. These findings enhance our understanding of how environmental factors impact the habitat suitability of P. tatsienense and provide valuable insights for developing effective management and conservation strategies for this important species. [ABSTRACT FROM AUTHOR]

The foraging strategies of sympatric ungulates with similar ecological niches are important for understanding ecological niche differentiation, resource utilization, competition, and coexistence and for understanding the ecological impacts on plant communities in the ecosystem. The behavior of the wapiti (Cervus elaphus) and Siberian roe deer (Capreolus pygargus) foraging on Japanese yew (Taxus cuspidata) has affected its succession and renewal in the northeastern forests of China, which has become an urgent problem for the relevant departments. This study analyzed the foraging strategies of the wapiti and Siberian roe deer on Japanese yew from July 2021 to January 2024 using field investigations and infrared camera monitoring in the Muling National Nature Reserve, Heilongjiang Province, China. It was found that the wapiti and Siberian roe deer have different foraging strategies in terms of time, space, and behavior. Temporally, they both preferred to forage for the saplings of the Japanese yew during the winter season, the degree of overlap in foraging rhythms was medium (Dhat1 = 0.67), and the diurnal foraging activity index (DRAI) of the wapiti was larger than that of the Siberian roe deer. Spatially, the suitable foraging habitat of the Siberian roe deer was twice that of the wapiti, and their overlap was low in the location and direction of saplings and the distance of the seed tree. Behaviorally, the foraging intensity of the wapiti was high, and that of Siberian roe deer was low. Foraging reduced the average primary branch height, number of new branches, and length of lateral branches of saplings, and the influence of the wapiti was significantly greater than that of the Siberian roe deer. This study provides a scientific basis for solving the conservation and management problems of the deer animals foraging on Japanese yew and contributes to further understanding of the competition‐coexistence mechanism of sympatric species. [ABSTRACT FROM AUTHOR]

Energy landscape analysis is a data‐driven method to analyse multidimensional time series, including functional magnetic resonance imaging (fMRI) data. It has been shown to be a useful characterization of fMRI data in health and disease. It fits an Ising model to the data and captures the dynamics of the data as movement of a noisy ball constrained on the energy landscape derived from the estimated Ising model. In the present study, we examine test–retest reliability of the energy landscape analysis. To this end, we construct a permutation test that assesses whether or not indices characterizing the energy landscape are more consistent across different sets of scanning sessions from the same participant (i.e. within‐participant reliability) than across different sets of sessions from different participants (i.e. between‐participant reliability). We show that the energy landscape analysis has significantly higher within‐participant than between‐participant test–retest reliability with respect to four commonly used indices. We also show that a variational Bayesian method, which enables us to estimate energy landscapes tailored to each participant, displays comparable test–retest reliability to that using the conventional likelihood maximization method. The proposed methodology paves the way to perform individual‐level energy landscape analysis for given data sets with a statistically controlled reliability. [ABSTRACT FROM AUTHOR]

Sanjiangyuan National Park (SNP), the first national park in China, is one of the most important biodiversity conservation areas in the Sanjiangyuan National Nature Reserve (SNNR) and even the world. The threatened ungulates play an irreplaceable role in maintaining the ecosystem diversity and stability in SNNR. Here, based on 1434 occurrence records of six ungulates, the maximum entropy model, with two different strategies, was utilized to determine the priority reserves. The results indicated that the priority reserves in SNNR was mainly located in and around SNP, which were mainly distributed in the middle east, middle west, and southwest of SNNR. Six ungulates shared preference for altitude ranging 4000–5000 m, the average annual temperature below −3.0°C, and average annual precipitation ranging 200–400 mm on meadow, steppe, and unused land. The proportion of high and medium suitable areas for ungulates in SNP was higher than that in SNNR. As the SNP is not contiguously spaced in space, and some core wildlife habitats are not included, it is suggested to optimize the functional areas and adjust the boundary range on the basis of the pilot scope of SNP, so as to enhance the integrity and connectivity of each functional area. [ABSTRACT FROM AUTHOR]

Previous studies and efforts to prevent and manage avian influenza (AI) outbreaks have mainly focused on the wintering season. However, outbreaks of AI have been reported in the summer, including the breeding season of waterfowl. Additionally, the spatial distribution of waterfowl can easily change during the annual cycle due to their life‐cycle traits and the presence of both migrants and residents in the population. Thus, we assessed the spatiotemporal variation in AI exposure risk in poultry due to spatial distribution changes in three duck species included in both major residents and wintering migrants in South Korea, the mandarin, mallard and spot‐billed duck, during wintering (October–March), breeding (April–June) and whole annual seasons. To estimate seasonal ecological niche variations among the three duck species, we applied pairwise ecological niche analysis using the Pianka index. Subsequently, seasonal distribution models were projected by overlaying the monthly ranges estimated by the maximum entropy model. Finally, we overlaid each seasonal distribution range onto a poultry distribution map of South Korea. We found that the mandarin had less niche overlap with the mallard and spot‐billed duck during the wintering season than during the breeding season, whereas the mallard had less niche overlap with the mandarin and spot‐billed duck during the breeding season than during the wintering season. Breeding and annual distribution ranges of the mandarin and spot‐billed duck, but not the mallard, were similar or even wider than their wintering ranges. Similarly, the mandarin and spot‐billed duck showed more extensive overlap proportions between poultry and their distributional ranges during both the breeding and annual seasons than during the wintering season. These results suggest that potential AI exposure in poultry can occur more widely in the summer than in winter, depending on sympatry with the host duck species. Future studies considering the population density and variable pathogenicity of AI are required. [ABSTRACT FROM AUTHOR]

The pairwise maximum entropy model (MEM) for resting state functional MRI (rsfMRI) has been used to generate energy landscape of brain states and to explore nonlinear brain state dynamics. Researches using MEM, however, has mostly been restricted to fixed‐effect group‐level analyses, using concatenated time series across individuals, due to the need for large samples in the parameter estimation of MEM. To mitigate the small sample problem in analyzing energy landscapes for individuals, we propose a Bayesian estimation of individual MEM using variational Bayes approximation (BMEM). We evaluated the performances of BMEM with respect to sample sizes and prior information using simulation. BMEM showed advantages over conventional maximum likelihood estimation in reliably estimating model parameters for individuals with small sample data, particularly utilizing the empirical priors derived from group data. We then analyzed individual rsfMRI of the Human Connectome Project to show the usefulness of MEM in differentiating individuals and in exploring neural correlates for human behavior. MEM and its energy landscape properties showed high subject specificity comparable to that of functional connectivity. Canonical correlation analysis identified canonical variables for MEM highly associated with cognitive scores. Inter‐individual variations of cognitive scores were also reflected in energy landscape properties such as energies, occupation times, and basin sizes at local minima. We conclude that BMEM provides an efficient method to characterize dynamic properties of individuals using energy landscape analysis of individual brain states. [ABSTRACT FROM AUTHOR]

California's Sierra Nevada has experienced a large increase in wildfire activities over recent decades. This intensifying fire regime has coincided with a warming climate and increasing human activity, but the relative importance of the biophysical and anthropogenic drivers of wildfire remains unclear across this diverse landscape, especially at a finer spatial scale. We used multisource geospatial data sets of fire occurrence, and human, climatic, and biophysical variables to examine the spatial pattern and controls on Sierra Nevada wildfires averaged from 1984 to 2017. The maximum entropy model driven by both biophysical and anthropogenic variables predicted the spatial distribution of fire probability well, with an area under the curve (AUC) score of 0.81. Model diagnostics revealed that aspects of the climate, including vapor pressure deficit (VPD), temperature, and burning index (difficulty of control), dominated the spatial patterns of fire probability across the whole Sierra Nevada region. The VPD was the leading control, with a relative contribution of 32.1%. Population density and fuel amount were also significant drivers, each accounting for 15.8%–12.4% of relative contribution. VPD and burning index were the most important factors for fire probability in higher elevation forest, while population density was comparatively more important in the lower elevation forest regions of the Sierra Nevada. Our findings improved our understanding of the relative importance of various factors in shaping the spatial patterns of historical fire probability in the Sierra Nevada and across various subecoregions, providing insights for targeting spatially varying forest management strategies to limit potential future increases in wildfires. Key Points: Maximum entropy models driven by biophysical and anthropogenic variables predicted the spatial distribution of wildfires accuratelyVapor pressure deficit dominated the spatial patterns of fire probability, and population density and fuel amount were significant driversThe relative importance of biophysical versus human controls varied with subecoregions [ABSTRACT FROM AUTHOR]

BACKGROUND Culicoides obsoletus is an important pest for animal husbandry. In the world, it is a major vector of bluetongue disease, which has been added to the list of Class A Animal Diseases by the World Organization for Animal Health (OIE) and must be reported in the event of an outbreak. Although numerous studies on Culicoides sampling in farms exist, the distribution of C. obsoletus in different landscapes remains unclear. RESULTS: This study aimed to assess the effect of landscape type on the distribution of C. obsoletus. Samples were collected using light traps during spring and fall. The effect of landscape type on the distribution of C. obsoletus samples was measured by the maximum entropy model (MaxEnt). Results show that C. obsoletus mainly appears in forests and pastures. CONCLUSION: Our results may provide references for controlling the C. obsoletus population and provide suggestions of other areas that may also face the challenge of bluetongue. [ABSTRACT FROM AUTHOR]

Many marine animals aggregate seasonally at predictable locations to exploit their prey in oligotrophic environments, making them vulnerable to human disturbance.The foraging strategies and habitats of manta rays and Stenella spp. in the Solor waters were largely unknown, despite the importance of this information for their effective management, e.g. through the establishment of marine protected areas in this region.Over the last few decades, the use of foraging habitat models in describing the foraging strategy and delineating the predicted habitats has grown in popularity. However, most of these studies have relied on remotely sensed data such as sea surface chlorophyll‐a to estimate prey distribution. Very few studies have used the distribution of in situ prey, which is that closer to the trophic level of modelled species, although such a strategy would improve model performance.The study compares foraging habitat model performance for manta rays and Stenella spp. in the waters of Solor using remotely sensed sea surface chlorophyll‐a concentrations and in situ zooplankton biomass as proxies for prey quantities and distribution. A Maximum Entropy model that integrated species sightings with environmental predictors was used to quantify the importance of predictors in explaining habitat preference and distribution of manta rays and Stenella spp. and to compare model performance and predict foraging habitats.Results indicate that the use of prey proxy closer to the trophic level of examined species improve model performance, ecological explanations, and spatial predictions.The zooplankton biomass distribution performed much better in explaining the manta rays' habitats compared to that of Stenella spp., indicating that a trophic level gap might influence the zooplankton's ability to predict foraging habitats of Stenella spp.This study highlights the importance of integrating foraging habitat models into marine protected area design to ensure the effectiveness of species management. [ABSTRACT FROM AUTHOR]

BACKGROUND: Phlebotomine sandfly (Diptera: Psychodidae) is best known for its role as the vector of the leishmaniasis. Leishmaniasis is a global disease, currently known to be affecting 88 countries and 12 million people worldwide. RESULTS: This study explored the impacts of climate factors and landscape on the local abundance of Phlebotomine sandfly in mainland China. A presence‐only modelling method was used to evaluate this species' habitat preferences from environmental factors. Jackknife analysis revealed that several biologically meteorological variables, including the maximum temperature of the warmest quarter, the precipitation in the driest month, the daily average temperature and daily precipitation, would significantly affect the existence of this species. Moreover, the existence of Phlebotomine sandfly was significantly associated with grassland and forest. CONCLUSION: The results will improve scientific understanding of the risks of the spread of leishmaniasis over current infected areas and can be used to design more detailed surveillance programmes and more evidence‐based control planning for Phlebotomine sandfly and leishmaniasis. © 2019 Society of Chemical Industry [ABSTRACT FROM AUTHOR]

Abstract: Phlebotomine sandflies (Diptera: Phlebotomidae) are vectors of the zoonotic disease leishmaniasis. To better understand the distribution of phlebotomine sandflies in order to facilitate control of leishmaniasis transmission, the present study explored the impacts of climate and landscape on local abundances of Phlebotomus chinensis in northwestern mainland China. Identification records were used to create a geodatabase for the locations at which P. chinensis had been collected in the region, and a regional‐scale map was developed to show the distribution of P. chinensis. Location data and data on environmental factors during the years in which the samples were collected were incorporated, and a presence‐only modelling method was used to evaluate the species' habitat preferences and to predict its potential distribution in northwestern mainland China. Jackknife analysis revealed that several meteorological variables, including maximum temperature in the warmest quarter, precipitation in the driest month, daily average temperature and daily precipitation, significantly affected the presence of this species. Moreover, the presence of P. chinensis was significantly associated with grassland and shrubland. Probability distributions using maximum entropy were used to map the distribution ranges of P. chinensis based on suitable habitats in northwestern mainland China. The models generated can be used to develop detailed strategies for the prevention and control of leishmaniasis. [ABSTRACT FROM AUTHOR]

Abstract: Tsetse flies (genus Glossina) are the only vector for the parasitic trypanosomes responsible for sleeping sickness and nagana across sub‐Saharan Africa. In Uganda, the tsetse fly Glossina fuscipes fuscipes is responsible for transmission of the parasite in 90% of sleeping sickness cases, and co‐occurrence of both forms of human‐infective trypanosomes makes vector control a priority. We use population genetic data from 38 samples from northern Uganda in a novel methodological pipeline that integrates genetic data, remotely sensed environmental data, and hundreds of field‐survey observations. This methodological pipeline identifies isolated habitat by first identifying environmental parameters correlated with genetic differentiation, second, predicting spatial connectivity using field‐survey observations and the most predictive environmental parameter(s), and third, overlaying the connectivity surface onto a habitat suitability map. Results from this pipeline indicated that net photosynthesis was the strongest predictor of genetic differentiation in G. f. fuscipes in northern Uganda. The resulting connectivity surface identified a large area of well‐connected habitat in northwestern Uganda, and twenty‐four isolated patches on the northeastern margin of the G. f. fuscipes distribution. We tested this novel methodological pipeline by completing an ad hoc sample and genetic screen of G. f. fuscipes samples from a model‐predicted isolated patch, and evaluated whether the ad hoc sample was in fact as genetically isolated as predicted. Results indicated that genetic isolation of the ad hoc sample was as genetically isolated as predicted, with differentiation well above estimates made in samples from within well‐connected habitat separated by similar geographic distances. This work has important practical implications for the control of tsetse and other disease vectors, because it provides a way to identify isolated populations where it will be safer and easier to implement vector control and that should be prioritized as study sites during the development and improvement of vector control methods. [ABSTRACT FROM AUTHOR]

We explored the seasonal potential fishing grounds of neon flying squid ( Ommastrephes bartramii) in the western and central North Pacific using maximum entropy (MaxEnt) models fitted with squid fishery data as response and environmental factors from remotely sensed [sea surface temperature ( SST), sea surface height ( SSH), eddy kinetic energy ( EKE), wind stress curl ( WSC) and numerical model-derived sea surface salinity ( SSS)] covariates. The potential squid fishing grounds from January-February (winter) and June-July (summer) 2001-2004 were simulated separately and covered the near-coast (winter) and offshore (summer) forage areas off the Kuroshio-Oyashio transition and subarctic frontal zones. The oceanographic conditions differed between regions and were regulated by the inherent seasonal variability and prevailing basin dynamics. The seasonal and spatial extents of potential squid fishing grounds were largely explained by SST (7-17°C in the winter and 11-18°C in the summer) and SSS (33.8-34.8 in the winter and 33.7-34.3 in the summer). These ocean properties are water mass tracers and define the boundaries of the North Pacific hydrographic provinces. Mesoscale variability in the upper ocean inferred from SSH and EKE were also influential to squid potential fishing grounds and are presumably linked to the augmented primary productivity from nutrient enhancement and entrainment of passive plankton. WSC, however, has the least model contribution to squid potential fishing habitat relative to the other environmental factors examined. Findings of this work underpin the importance of SST and SSS as robust predictors of the seasonal squid potential fishing grounds in the western and central North Pacific and highlight MaxEnt's potential for operational fishery application. [ABSTRACT FROM AUTHOR]

The article presents a study which determines measures to mitigate anthropogenic disturbance among wintering Black Grouse and mountain ecosystems. It outlines the method of the study which applies spatial modeling to localize conflict zones and estimate the amount of habitat reduction caused by winter sports and recreation activities. The study found out the effectiveness of spatial modeling to accurately identify zones of potential conflict to help create measures to minimize such risks.

Aim Species distribution models are invaluable tools in biogeographical, ecological and applied biological research, but specific concerns have been raised in relation to different modelling techniques in terms of their validity. Here we compare two fundamentally different approaches to species distribution modelling, one based on simple occurrence data where the lack of an ecological framework has been criticized, and the other firmly based in socio-ecological theory but requiring highly detailed behavioural information that is often limited in availability. Location (Sub-Saharan) Africa. Methods We used two distinct techniques to predict the realized distribution of a model species, the vervet monkey ( Cercopithecus aethiops Linnaeus, 1758). A maximum entropy model was produced taking 13 environmental variables and presence-only data from 174 sites throughout Africa as input, with an additional 58 sites retained to test the model. A time-budget model considering the same environmental variables was constructed from detailed behavioural data on 20 groups representing 14 populations, with presence-only data from the remaining 218 sites reserved to test model predictions on vervet monkey occurrence. Both models were further validated against a reference species distribution map as drawn up by the African Mammals Databank. Results Both models performed well, with the time budget and maximum entropy algorithms correctly predicting vervet monkey presence at 78.4% and 91.4% of their respective test sites. Similarly, the time-budget model correctly predicted presence and absence at 87.4% of map pixels against the reference distribution map, and the maximum entropy model achieved a success rate of 81.8%. Finally, there was a high level of agreement (81.6%) between the presence–absence maps produced by the two models, and the environmental variables identified as most strongly driving vervet monkey distribution were the same in both models. Main conclusions The time-budget and maximum entropy models produced accurate and remarkably similar species distribution maps, despite fundamental differences in their conceptual and methodological approaches. Such strong convergence not only provides support for the credibility of current results, but also relieves concerns about the validity of the two modelling approaches. [ABSTRACT FROM AUTHOR]