Results 141 to 150 of about 27,328 (252)
MaxEnt‐based predictions reveal dramatic range dynamics of Choerospondias axillaris during Quaternary climate cycles, including northward expansion during the last interglacial, southward contraction into fragmented refugia during the last glacial maximum, and subsequent northward recolonization since the post‐glacial period.
Peng Luo +7 more
wiley +1 more source
Climate Change Alters Elevational Distribution Patterns of Cormus domestica Habitat
This study utilizes species distribution models to project the habitat shifts of Cormus domestica in Italy, predicting a substantial range reduction of 32%–68% by 2100 driven primarily by rising annual mean temperatures. The results indicate a significant shift of suitable habitats toward higher elevations, creating a scenario where the necessary ...
Qianjiang Li +4 more
wiley +1 more source
Traditional statistical models for debris-flow susceptibility often overlook critical triggering mechanisms and geotechnical parameters. To address this, we propose an innovative framework that couples the Maximum Entropy (MaxEnt) statistical model with ...
Xinlong Xu +8 more
doaj +1 more source
The study predicted the potential global distribution of invasive pest Echinothrips americanus using MaxEnt models, revealing its primary habitats concentrated in Asia, Europe, North America, South America, and Africa (total area ~ 3.41 × 107 km2). Under future climate scenarios, marginally suitable areas are projected to contract significantly, while ...
Qingling Hu +3 more
wiley +1 more source
Current Protections and Future Threats to Say's Spiketail Habitat in the Southeastern USA
Dragonflies and damselflies are important indicators of ecological integrity and environmental quality but remain underrepresented in conservation efforts, in part due to a lack of quantitative information regarding their habitat requirements. Species distribution models (SDMs) can facilitate conservation planning by allowing researchers to estimate ...
J. Matthew Flenniken +2 more
wiley +1 more source
Correlative species distribution models (SDMs) are quantitative tools in biogeography and macroecology. Building upon the ecological niche concept, they correlate environmental covariates to species presence to model habitat suitability and predict species distributions.
Moritz Klaassen +3 more
wiley +1 more source
Reviewing and benchmarking ecological modelling practices in the context of land use
Despite habitat loss and degradation are the primary drivers of biodiversity loss, different conclusions have been drawn about the importance of land‐use or land‐cover (LULC) change for biodiversity. Differences may be due to the difficulty of framing a coherent model design to assess LULC effects.
Elie Gaget +6 more
wiley +1 more source
Twenty years of dynamic occupancy models: a review of applications and look to the future
Since their introduction over 20 years ago, dynamic occupancy models (DOMs) have become a powerful and flexible framework for estimating species occupancy across space and time while accounting for imperfect detection. As their popularity has increased and extensions have further expanded their capabilities, DOMs have been applied to increasingly ...
Saoirse Kelleher +3 more
wiley +1 more source
Predicting areas of wildlife damage can reduce long‐term costs. We developed and tested models of the geographic distribution of damage by woodpeckers to utility infrastructure and delineated areas of greater importance. Abstract Predicting areas of wildlife damage to human development has the potential to reduce long‐term costs associated with ...
Hannah C. Wright +4 more
wiley +1 more source
Non-Monotonic Reasoning on Probability Models: Indifference,Independence &MaxEnt [PDF]
Through completing an underspecified probability model, Maximum Entropy (MaxEnt) supports non-monotonic inferences. Some major aspects of how this is done by MaxEnt can be understood from the background of two principles of rational decision: the concept of Indifference and the concept of Independence. In a formal specification MaxEnt can be viewed as (
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