Uncovering habitat associations and thresholds—insights for managing breeding waterfowl in Eastern Canada DOI Creative Commons
Barbara Frei, Amelia R. Cox,

Andrea Brown

et al.

Landscape Ecology, Journal Year: 2024, Volume and Issue: 39(8)

Published: Aug. 7, 2024

Abstract Context Understanding how habitat influences species abundance is crucial in developing ecologically sound wildlife conservation management plans. Exploring associations and ecological thresholds species’ responses allows for better on a landscape-scale. Objectives This work aimed to identify drivers response of waterfowl waterbird densities eastern Canada support key landscape-level decisions wetland management. Methods We developed predictive models 17 across from 2001 2015 using data four regional surveys identified areas where prioritizing enhancement wetlands would increase the breeding density five priority species. Results Habitat spatial patterns varied species, but most responded strongly forest composition, agriculture, features. Threshold effects occurred among yet generally once 14% plot was covered wetlands, positive increased diminished Our results allow targeting investments increasing area along portions that provide best opportunities Conclusions species-habitat landscape planning prioritization limited resources. suggest efforts should be guided by attributes prioritize actions will have biggest impact multiple

Language: Английский

sdmTMB: An R Package for Fast, Flexible, and User-Friendly Generalized Linear Mixed Effects Models with Spatial and Spatiotemporal Random Fields DOI Creative Commons
Sean C. Anderson, Eric J. Ward, Philina A. English

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2022, Volume and Issue: unknown

Published: March 27, 2022

Abstract Geostatistical spatial or spatiotemporal data are common across scientific fields. However, appropriate models to analyse these data, such as generalised linear mixed effects (GLMMs) with Gaussian Markov random fields (GMRFs), computationally intensive and challenging for many users implement. Here, we introduce the R package sdmTMB , which extends flexible interface familiar of lme4, glmmTMB mgcv include latent GMRFs using an SPDE-(stochastic partial differential equation) based approach. SPDE matrices constructed fmesher estimation is conducted via maximum marginal likelihood TMB Bayesian inference tmbstan rstan . We describe model explore case studies that illustrate ’s flexibility in implementing penalised smoothers, non-stationary processes (time-varying spatially varying coefficients), hurdle models, cross-validation anisotropy (directionally dependent correlation). Finally, compare functionality, speed, interfaces related software, demonstrating can be order magnitude faster than R- INLA hope will help open this useful class a wider field geostatistical analysts.

Language: Английский

Citations

105
Spatially varying population indices DOI
Jonas Knape

Ecological Indicators, Journal Year: 2025, Volume and Issue: 174, P. 113435 - 113435

Published: April 18, 2025

Language: Английский

Citations

1
Guidelines for the use of spatially varying coefficients in species distribution models DOI Creative Commons
Jeffrey W. Doser, Marc Kéry, Sarah P. Saunders

et al.

Global Ecology and Biogeography, Journal Year: 2024, Volume and Issue: 33(4)

Published: Feb. 21, 2024

Abstract Aim Species distribution models (SDMs) are increasingly applied across macroscales using detection‐nondetection data. These typically assume that a single set of regression coefficients can adequately describe species–environment relationships and/or population trends. However, such often show nonlinear spatially varying patterns arise from complex interactions with abiotic and biotic processes operate at different scales. Spatially coefficient (SVC) readily account for variability in the effects environmental covariates. Yet, their use ecology is relatively scarce due to gaps understanding inferential benefits SVC provide compared simpler frameworks. Innovation Here we demonstrate SDMs, particular focus on how this approach be used generate test ecological hypotheses regarding drivers spatial trends relationships. We illustrate SDMs simulations two case studies: one assesses 51 forest bird species eastern United States over decades second evaluates five land cover change grasshopper sparrow ( Ammodramus savannarum ) occurrence continental States. Main conclusions found strong support alternatives both empirical studies. Factors operating fine scales, accounted by SVCs, were primary divers Additionally, SVCs revealed species–habitat grassland cropland area sparrow, providing nuanced insights into future may shape its distribution. applications display utility help reveal factors drive distributions local broad conclude discussing potential conservation.

Language: Английский

Citations

7
Seasonal-Spatial Distribution Variations and Predictions of Loliolus beka and Loliolus uyii in the East China Sea Region: Implications from Climate Change Scenarios DOI Creative Commons

Min Xu,

Wangjue Feng,

Zunlei Liu

et al.

Animals, Journal Year: 2024, Volume and Issue: 14(14), P. 2070 - 2070

Published: July 15, 2024

Global climate change profoundly impacts the East China Sea ecosystem and poses a major challenge to fishery management in this region. In addition, closely related species with low catches are often not distinguished production relevant data commonly merged statistics fishing logbooks, making it challenging accurately predict their habitat distribution range. Here, fisheries-independent of squid

Language: Английский

Citations

5
Addressing uncertainty when projecting marine species' distributions under climate change DOI Creative Commons
Sarah C. Davies, Patrick L. Thompson, Catalina Gómez

et al.

Ecography, Journal Year: 2023, Volume and Issue: 2023(11)

Published: Sept. 13, 2023

Species distribution models (SDMs) have been widely used to project terrestrial species' responses climate change and are increasingly being for similar objectives in the marine realm. These projections critically needed develop strategies resource management conservation of ecosystems. SDMs a powerful necessary tool; however, they subject many sources uncertainty, both quantifiable unquantifiable. To ensure that SDM informative decisions, uncertainty must be considered properly addressed. Here we provide ten overarching guidelines will aid researchers identify, minimize, account through entire model development process, from formation study question presentation results. focus on correlative were developed at an international workshop attended by over 50 practitioners. Although our broadly applicable across biological realms, particular challenges uncertainties associated with projecting impacts species

Language: Английский

Citations

11
Impacts of different types of data integration on the predictions of spatio-temporal models: A fishery application and simulation experiment DOI
Arnaud Grüss, Richard L. O’Driscoll, James T. Thorson

et al.

Fisheries Research, Journal Year: 2025, Volume and Issue: 284, P. 107321 - 107321

Published: March 11, 2025

Language: Английский

Citations

0
Identifying the spatio-temporal distribution patterns of mixed fisheries to inform multispecies management in the Yellow and Bohai Seas DOI
Jun Li Ren, Jia Wo, Qun Liu

et al.

Fisheries Research, Journal Year: 2025, Volume and Issue: 285, P. 107351 - 107351

Published: April 3, 2025

Language: Английский

Citations

0
Applications of species distribution modeling and future needs to support marine resource management DOI Creative Commons
Melissa A. Karp, Megan A. Cimino, J. Kevin Craig

et al.

ICES Journal of Marine Science, Journal Year: 2025, Volume and Issue: 82(3)

Published: Feb. 25, 2025

Abstract Fisheries science agencies are responsible for informing fisheries management and ocean planning worldwide, often requiring scientific analysis actions across multiple spatial scales. For example, catch limits typically defined annually over regional scales, fishery bycatch rules at fine scales on daily to annual time aquaculture energy lease areas decades subregional permitting intermediate Similarly, these activities require synthesizing monitoring data mechanistic knowledge operating different resolutions domains. These needs drive a growing role models that predict animal presence or densities including daily, seasonal, interannual variation, called species distribution/density (SDMs). SDMs can inform many needs; however, their development usage haphazard. In this paper we discuss various ways have been used in stock, habitat, protected species, ecosystem as well marine planning, survey optimization, an interface with climate models. We conclude discussion of future directions, focusing information current development, highlight avenues furthering the community practice around SDM use.

Language: Английский

Citations

0
Modeling Complex Species-Environment Relationships Through Spatially-Varying Coefficient Occupancy Models DOI
Jeffrey W. Doser, Andrew O. Finley, Sarah P. Saunders

et al.

Journal of Agricultural Biological and Environmental Statistics, Journal Year: 2024, Volume and Issue: unknown

Published: Jan. 18, 2024

Language: Английский

Citations

3
Integrating survey and observer data improves the predictions of New Zealand spatio-temporal models DOI Creative Commons
Arnaud Grüss, Anthony R. Charsley, James T. Thorson

et al.

ICES Journal of Marine Science, Journal Year: 2023, Volume and Issue: 80(7), P. 1991 - 2007

Published: Aug. 22, 2023

Abstract In many situations, species distribution models need to make use of multiple data sources address their objectives. We developed a spatio-temporal modelling framework that integrates research survey and collected by observers onboard fishing vessels while accounting for physical barriers (islands, convoluted coastlines). demonstrated our two bycatch in New Zealand deepwater fisheries: spiny dogfish (Squalus acanthias) javelinfish (Lepidorhynchus denticulatus). Results indicated employing observer-only or integrated is necessary map fish biomass at the scale exclusive economic zone, interpolate local indices (e.g., east coast South Island) years with no but available observer data. also showed that, if enough are available, fisheries analysts should: (1) develop both an model relying on survey-only data; (2) given geographic area, ultimately choose index produced based reliability interannual variability index. conducted simulation experiment, which predictions virtually insensitive consideration barriers.

Language: Английский

Citations

7