Results 71 to 80 of about 609 (154)

Integrated species distribution models fitted in INLA are sensitive to mesh parameterisation [PDF]

, 2023
The ever-growing popularity of citizen science, as well as recent technological and digital developments, have allowed the collection of data on species' distributions at an extraordinary rate.
Boughey, Katherine, Dambly, Lea, Isaac, N, Jones, Katherine, O'Hara, R   +4 more
core  

Patterns and drivers of biotic disturbance hotspots in western United States coniferous forests

Ecography, Volume 2025, Issue 9, September 2025.
Globally, forest disturbances caused by herbivorous insects and plant pathogens (i.e. biotic disturbances) have increased since the 1990s, a trend linked in part to climate warming. With increases in biotic disturbance activity, an emerging ecological phenomenon has been documented: biotic disturbance ‘hotspots', or areas where two or more biotic ...
Michele S. Buonanduci, Sarah J. Hart, Patrick C. Tobin, Brian J. Harvey   +3 more
wiley   +1 more source

Migration-adjusted prostate cancer incidence in China: a population-based epidemiological analysis

Frontiers in Public Health
BackgroundChina’s internal migration poses major challenges to cancer surveillance systems that rely on household-registered populations (HRP). Excluding migrants can lead to biased incidence estimates and misinformed public health planning.MethodsThis ...
Yu Zhang, Yu Zhang, Yu Zhang, Guijin Li, Guijin Li, Guijin Li, Shuxiu Hao, Shuxiu Hao, Shuxiu Hao, Huixin Sun, Huixin Sun, Huixin Sun, Huixin Sun, Linlin Du, Linlin Du, Linlin Du, Linlin Du, Xinyu Liu, Liangfang Xue, Xinshu Wang, Tong Wang, Tong Wang, Tong Wang, Qi Li   +23 more
doaj   +1 more source

Addressing spatial confounding in geostatistical regression models: An R‐INLA approach

Methods in Ecology and Evolution, Volume 16, Issue 9, Page 2082-2097, September 2025.
Abstract Spatial confounding, which has been studied extensively in recent years, can explain inconsistencies between results obtained by regression models with and without spatial modelling. While the most common solutions for spatial confounding mitigation often target areal data or geostatistical data modelling by splines, we aim to extend some ...
Jérémy Lamouroux, Alizée Geffroy, Sébastien Leblond, Caroline Meyer, Isabelle Albert   +4 more
wiley   +1 more source

Warming winters promote biodiversity through reduced mortality of a habitat‐forming species in soft‐sediment intertidal systems

Oikos, Volume 2025, Issue 8, August 2025.
Habitat‐forming species enhance biodiversity by providing shelter, substrate and feeding grounds for many species. Climate‐change effects on these species may have wide ranging impacts on local species richness and abundance. Warmer winter temperatures in particular are an important driver of demographic parameters in temperate to polar environments ...
Paula de la Barra, Katharina Alter, Allert I. Bijleveld   +2 more
wiley   +1 more source

Technical note: Accurate, reliable, and high-resolution air quality predictions by improving the Copernicus Atmosphere Monitoring Service using a novel statistical post-processing method [PDF]

Atmospheric Chemistry and Physics
Starting from the regional air quality forecasts produced by the Copernicus Atmosphere Monitoring Service (CAMS), we propose a novel post-processing approach to improve and downscale results on a finer scale.
A. Riccio, A. Riccio, E. Chianese
doaj   +1 more source

Development of spatio-temporal data fusion frameworks for point and gridded soil moisture data [PDF]

Monitoring soil moisture can play an important role in helping to inform researchers, regulators, and landowners about the available water content of the soil for agriculture and vegetation.
Zheng, Weiyue
core   +1 more source

Room to roam and hotspots of conservation conflicts: Lions, livestock and people in the matrix [PDF]

, 2022
Ph. D. ThesisGlobally, large terrestrial carnivores have suffered precipitous declines in population and range. Today, they must persist in increasingly isolated natural habitat patches within a human-dominated landscape matrix.
Sargent, Rebecca
core  

Fast and scalable inference for spatial extreme value models

Canadian Journal of Statistics, Volume 53, Issue 2, June 2025.
Abstract The generalized extreme value (GEV) distribution is a popular model for analyzing and forecasting extreme weather data. To increase prediction accuracy, spatial information is often pooled via a latent Gaussian process (GP) on the GEV parameters. Inference for GEV‐GP models is typically carried out using Markov Chain Monte Carlo (MCMC) methods,
Meixi Chen, Reza Ramezan, Martin Lysy
wiley   +1 more source

Modelação Geoestatística [PDF]

, 2022
Nos últimos anos, a geoestatística tem sidomuito utilizada em vários estudos de diferentes áreas de pesquisa, a atividade pesqueira é uma dessas áreas de interesse, principalmente quando se pretende estudar fenómenos relacionados com a distribuição ...
Miguel, Belchior Coelho
core