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The difficulty to measure or predict species community composition at fine spatio-temporal resolution and over large spatial scales severely hampers our ability to understand species assemblages and take appropriate conservation measures.…

Habitat loss is one of the biggest threats facing plant species nowadays. We formulate a simple mathematical model of seed dispersal on reduced habitats to discuss survival of the species in relation to the habitat size and seeds production…

Probability · Mathematics 2023-05-24 Cristian F. Coletti , Nevena Marić , Pablo M. Rodriguez

Species distribution models (SDMs) are widely used to predict species' geographic distributions, serving as critical tools for ecological research and conservation planning. Typically, SDMs relate species occurrences to environmental…

Machine Learning · Computer Science 2025-08-12 Hager Radi Abdelwahed , Mélisande Teng , Robin Zbinden , Laura Pollock , Hugo Larochelle , Devis Tuia , David Rolnick

When analyzing data from multiple sources, it is often convenient to strike a careful balance between two goals: capturing the heterogeneity of the samples and sharing information across them. We introduce a novel framework to model a…

Methodology · Statistics 2026-03-02 Laura D'Angelo , Bernardo Nipoti , Andrea Ongaro

We focus on species distribution modeling using global-scale presence-only data, leveraging geographical and environmental features to map species ranges, as in previous studies. However, we innovate by integrating taxonomic classification…

Computer Vision and Pattern Recognition · Computer Science 2025-09-09 Srikumar Sastry , Xin Xing , Aayush Dhakal , Subash Khanal , Adeel Ahmad , Nathan Jacobs

Species distribution models (SDMs) are increasingly applied across macroscales. Such models typically assume that a single set of regression coefficients can adequately describe species-environment relationships and/or population trends.…

Over the last few decades, ecologists have come to appreciate that key ecological patterns, which describe ecological communities at relatively large spatial scales, are not only scale dependent, but also intimately intertwined. The…

Populations and Evolution · Quantitative Biology 2016-09-13 Fabio Peruzzo , Sandro Azaele

Occupancy models are frequently used by ecologists to quantify spatial variation in species distributions while accounting for observational biases in the collection of detection-nondetection data. However, the common assumption that a…

One of the first beings affected by changes in the climate are trees, one of our most vital resources. In this study tree species interaction and the response to climate in different ecological environments is observed by applying a joint…

Populations and Evolution · Quantitative Biology 2019-10-14 Hyun Choi , Ali Sadeghian , Sergio Marconi , Ethan White , Daisy Zhe Wang

Conservation science depends on an accurate understanding of what's happening in a given ecosystem. How many species live there? What is the makeup of the population? How is that changing over time? Species Distribution Modeling (SDM) seeks…

Machine Learning · Computer Science 2021-07-23 Sara Beery , Elijah Cole , Joseph Parker , Pietro Perona , Kevin Winner

Presence-absence data is defined by vectors or matrices of zeroes and ones, where the ones usually indicate a "presence" in a certain place. Presence-absence data occur for example when investigating geographical species distributions,…

Methodology · Statistics 2021-11-24 Gabriele d'Angella , Christian Hennig

The distributions of species lifetimes and species in space are related, since species with good local survival chances have more time to colonize new habitats and species inhabiting large areas have higher chances to survive local…

Populations and Evolution · Quantitative Biology 2019-02-18 Tobias Rogge , David Jones , Barbara Drossel , Korinna T. Allhoff

The investigation of leaf-level traits in response to varying environmental conditions has immense importance for understanding plant ecology. Remote sensing technology enables measurement of the reflectance of plants to make inferences…

Applications · Statistics 2022-10-04 Philip A. White , Michael F. Christensen , Henry Frye , Alan E. Gelfand , John A. Silander

For many taxonomic groups, online biodiversity portals used by naturalists and citizen scientists constitute the primary source of distributional information. Over the last decade, site-occupancy models have been advanced as a promising…

Aims To propose and analyze a general, dynamic, process-oriented theory of the area of distribution. Methods The area of distribution is modelled by combining (by multiplication) three matrices: one matrix represents movements, another…

Populations and Evolution · Quantitative Biology 2022-12-14 Jorge Soberón , Luis Osorio-Olvera

We develop a class of nonlocal delay Reaction-Diffusion (RD) models in a circular domain. Previous modeling efforts include RD population models with respect to one-dimensional unbounded domain, unbounded strip and rectangular spatial…

Analysis of PDEs · Mathematics 2014-12-16 M. Bani-Yaghoub , G. Yao , A. Reed

A reparametrized Dirichlet-multinomial distribution is introduced, and the covariance matrix, as well as, the algorithm for calculating the PDF for n species are provided. The distribution is suited for modelling the joint distribution of…

Populations and Evolution · Quantitative Biology 2020-03-04 Christian Damgaard

Natural ecosystems are characterized by striking diversity of form and functions and yet exhibit deep symmetries emerging across scales of space, time and organizational complexity. Species-area relationships and species-abundance…

Models at various levels of resolution are commonly used, both for forest management and in ecological research. They all have comparative advantages and disadvantages, making desirable a better understanding of the relationships between…

Populations and Evolution · Quantitative Biology 2022-02-03 Oscar García

Species distribution models (SDMs) aim to predict the distribution of species by relating occurrence data with environmental variables. Recent applications of deep learning to SDMs have enabled new avenues, specifically the inclusion of…

Machine Learning · Computer Science 2024-11-07 Nina van Tiel , Robin Zbinden , Emanuele Dalsasso , Benjamin Kellenberger , Loïc Pellissier , Devis Tuia