The shadow model: how and why small choices in spatially explicit species distribution models affect predictions

PeerJ, Journal Year: 2022, Volume and Issue: 10, P. e12783 - e12783

Published: Feb. 14, 2022

The use of species distribution models (SDMs) has rapidly increased over the last decade, driven largely by increasing observational evidence distributional shifts terrestrial and aquatic populations. These permit, for example, quantification range shifts, estimation co-occurrence, association habitat to abundance. complexity contemporary SDMs presents new challenges—as choices among modeling options increase, it is essential understand how these affect model outcomes. Using a combination original analysis literature review, we synthesize effects three common in semi-parametric predictive process modeling: structure, spatial extent data, scale predictions. To illustrate choices, develop case study centered around sablefish ( Anoplopoma fimbria ) on west coast USA. represent decisions necessary virtually all ecological applications methods, are important because consequences impact derived quantities interest e.g ., estimates population size their management implications). Truncating data near observed edge, or using that misspecified terms covariates spatiotemporal fields, led bias biomass trends mean compared from full dataset appropriate structure. In some cases, suboptimal may be unavoidable, but understanding tradeoffs impacts predictions critical. We seemingly small often made out necessity simplicity, can scientific advice informing decisions—potentially leading erroneous conclusions about changes abundance precision such estimates. For show incorrect could cause overestimation abundance, which result resulting overfishing. Based findings gaps, outline frontiers SDM development.

Language: Английский

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Comparison of machine learning models within different spatial resolutions for predicting the bigeye tuna fishing grounds in tropical waters of the Atlantic Ocean

Fisheries Oceanography, Journal Year: 2023, Volume and Issue: 32(6), P. 509 - 526

Published: April 2, 2023

Abstract To understand the effects of machine learning models and spatial resolutions on prediction accuracy bigeye tuna ( Thunnus obesus ) fishing grounds, logbook data 13 Chinese longliners operating in high seas Atlantic Ocean from 2016 to 2019 were collected. The environmental factors selected based correlation analysis calculation catch per unit effort (CPUE) marine vertical factors. Five models: random forest, gradient‐boosting decision tree, K ‐nearest neighbor, logistic regression stacking ensemble (STK) within four .5° × .5°, 1° 1°, 2° 5° grids constructed compared. Results showed that (1) performance STK model was best, with highest scores evaluation indexes, (Acc), precision (P), recall (R), F1‐score (F1), correct rate for predicting “high CPUE ground”; (2) resolution predicted better results compared grids; (3) could be used as reliable predictors models. suggested using improve generalization grounds Ocean.

Language: Английский

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Addressing uncertainty when projecting marine species' distributions under climate change

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

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Identifying priority areas for spatial management of mixed fisheries using ensemble of multi‐species distribution models

Fish and Fisheries, Journal Year: 2023, Volume and Issue: 25(2), P. 187 - 204

Published: Nov. 15, 2023

Abstract Spatial fisheries management is widely used to reduce overfishing, rebuild stocks, and protect biodiversity. However, the effectiveness optimization of spatial measures depend on accurately identifying ecologically meaningful areas, which can be difficult in mixed fisheries. To apply a method generally range target species, we developed an ensemble species distribution models (e‐SDM) that combines general additive models, generalized linear random forest, gradient‐boosting machine methods training testing protocol. The e‐SDM was integrate density indices from two scientific bottom trawl surveys with geopositional data, relevant oceanographic variables three‐dimensional physical‐biogeochemical operational model, fishing effort vessel monitoring system. determined best distributions for juveniles adults are determine hot spots aggregation based single or multiple species. We applied juvenile adult stages 10 marine demersal representing 60% total landings central areas Mediterranean Sea. Using results, grounds potentially more selective were identified each group otter beam results confirm ecological appropriateness existing fishery restriction support identification locations new measures.

Language: Английский

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Ecological forecasts of insect range dynamics: a broad range of taxa includes winners and losers under future climate

Current Opinion in Insect Science, Journal Year: 2024, Volume and Issue: 62, P. 101159 - 101159

Published: Jan. 9, 2024

Language: Английский

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