Spatial‐statistical downscaling with uncertainty quantification in biodiversity modelling

Methods in Ecology and Evolution, Journal Year: 2025, Volume and Issue: unknown

Published: March 6, 2025

Abstract Accurate downscaling with uncertainty quantification and its inclusion in fitting biodiversity models to data are essential for accurate, valid inferences predictions. Here, we provide a general framework spatial modelling of that involves environmental covariates. We derive ecological based on spatial‐statistical model accounts change‐of‐support. Through simulation study, demonstrate our statistical provides accurate quantification. With the Monte Carlo samples downscaled covariate, develop two‐stage protocol propagates generalised linear (GLM), commonly used modelling. call implementation CORGI (Change Of Resolution GLM Inference). A study shows this covariates improves propagation use when compared existing methods. The is broad utility given routine available at scales different from those species population or diversity metrics models. Moreover, readily implemented aid standard software packages. Extensions include accounting measurement errors missing values covariate data, non‐Gaussian fusing multi‐source adding random effects imposing physical constraints, discussed.

Language: Английский

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Differences in predictions of marine species distribution models based on expert maps and opportunistic occurrences

Conservation Biology, Journal Year: 2025, Volume and Issue: unknown

Published: March 24, 2025

Species distribution models (SDMs) are important tools for assessing biodiversity change. These require high-quality occurrence data, which not always available. Therefore, it is increasingly to determine how data choice affects predictions of species' ranges. Opportunistic records and expert maps both widely used sources species SDMs. However, unclear SDMs based on these differ in performance, particularly the marine realm. We built 233 fish from 2 families with types compared their performances potential predictions. occurrences were sourced field surveys South China Sea online repositories International Union Conservation Nature Red List database. generalized linear explore drivers differences prediction between model types. When projecting distinct regions no calibrated using opportunistic performed better than those maps, indicating transferability new environments. Differences predictor values accounted dissimilarity predictions, likely because included large areas unsuitable environmental conditions. Dissimilarity levels among differed, suggesting a taxonomic bias sources. Our findings highlight sensitivity distributional data. Although have an role modeling, we suggest researchers assess accuracy reduce commission errors knowledge target species.

Language: Английский

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Applications of species distribution modeling and future needs to support marine resource management

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

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Site-level and spatially-explicit modelling provides some insights on key factors driving seasonal dynamics of an intertidal seagrass

Ecological Modelling, Journal Year: 2024, Volume and Issue: 495, P. 110802 - 110802

Published: July 19, 2024

In a context of worldwide decline and given the critical ecological role marine seagrasses to coastal ecosystem structure functioning, regional conservation initiatives have emerged over past thirty years protect these important habitat-forming species.Yet, effective interventions need account for site-specific processes stressors.Thus, our ability accurately predict seagrass dynamics is pivotal support management interventions.To date, determinist process-based modelling has provided insights on drivers dynamics.Here, we developed an original model framework that combines hydrodynamics ocean with local data-driven models rely Boosted Regression Trees seasonal patch-level plant-level features as function environmental conditions.Based only 12-month monitoring across nine sites, traits successfully reproduce overall based mostly inferred relationships monthly light temperature, lesser extent, exposure physical stressors (i.e., currents waves).While fail finely capture spatial discrepancies all sites (especially where demonstrates higher growth potential), spatially-explicit simulations highlight how seagrass-hydrodynamics feedback whole bay can dampen potential due shear stress.However, this offers simulate long-term changes in extent status meadows Arcachon Bay, explicit resolving hydro-sediment effects appears priority better range between conditions.

Language: Английский

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