Trophic interactions will expand geographically but be less intense as oceans warm

Global Change Biology, Journal Year: 2020, Volume and Issue: 26(12), P. 6805 - 6812

Published: Oct. 5, 2020

Abstract Interactions among species are likely to change geographically due climate‐driven range shifts and in intensity physiological responses increasing temperatures. Marine ectotherms experience temperatures closer their upper thermal limits the paucity of temporary refugia compared those available terrestrial organisms. Thermal marine also vary trophic levels, making interactions more prone changes as oceans warm. We assessed how temperature affects reef fish Western Atlantic modeled projections occurrence, biomass, feeding across latitudes climate change. Under ocean warming, tropical reefs will diminished interactions, particularly herbivory invertivory, potentially reinforcing algal dominance this region. Tropicalization events occur northern hemisphere, where by herbivores is predicted expand from Caribbean extratropical reefs. Conversely, omnivores decrease area with minor increases southern Brazil. Feeding invertivores declines all future predictions, jeopardizing a critical link. Most 2050 can significantly affect ecosystem functioning, causing rise novel ecosystems.

Language: Английский

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The New Dominator of the World: Modeling the Global Distribution of the Japanese Beetle under Land Use and Climate Change Scenarios

Land, Journal Year: 2022, Volume and Issue: 11(4), P. 567 - 567

Published: April 12, 2022

The spread of invasive species is a threat to global biodiversity. Japanese beetle native Japan, but alien populations this insect occur in North America, and recently, also southern Europe. This was recently included on the list priority European concern, as it highly agricultural pest. Thus, study, we aimed at (i) assessing its current distribution range, identifying areas potential invasion, (ii) predicting using future climatic land-use change scenarios for 2050. We collected occurrences available citizen science platform iNaturalist, combined data with predictors Bayesian framework, specifically integrated nested Laplace approximation, stochastic partial differential equation. found that mainly, positively, driven by percentage croplands, annual range temperature, habitat diversity, human settlements, population density; negatively related distance airports, elevation, mean temperature diurnal wetlands, waters. As result, based conditions, likely 47,970,200 km2, while will from between 53,418,200 59,126,825 according 2050 scenarios. concluded high-risk species, able find suitable conditions colonization several regions around globe, especially light ongoing change. strongly recommend strict biosecurity checks quarantines, well regular pest management surveys, order reduce spread.

Language: Английский

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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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Combining animal interactions and habitat selection into models of space use: a case study with white‐tailed deer

Wildlife Biology, Journal Year: 2024, Volume and Issue: 2024(3)

Published: Feb. 8, 2024

Animals determine their daily movement trajectories in response to a network of ecological processes, including interactions with other organisms, memories previous events, and the changing environment. These combine cause emergent space use patterns observed over longer periods time, such as whole season. Understanding which processes these emerge, how, requires process‐based modelling approach. Individual‐based decisions can be described system partial‐differential equations (PDEs) produce dynamic description built from underlying process. Here we PDE‐based models step‐selection analysis investigate combined effects three established that partially shape use: 1) heterogeneous environment; 2) environmental markings moving conspecifics; 3) memory direct conspecifics. We apply this framework large GPS‐based dataset white‐tailed deer Odocoileus virginianus southeastern US. fit at population level provide predictive models, then tailor individual deer. specifically incorporate relationships between each possible pair define animal's responses unique local environments using separate integrated analyses. show how movements yield animal distributions, full generalised so it may applied any species simultaneously responding multiple potentially interacting stimuli (e.g. sociality, morphology, etc.). found bucks had highly varied preferences for vegetation, but were shaping conspecific interactions, dependent on two advocate increased consideration individual‐based rules determinants realized use, particularly affect distributions entire species.

Language: Английский

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Identifying optimal variables for machine-learning-based fish distribution modeling

Canadian Journal of Fisheries and Aquatic Sciences, Journal Year: 2024, Volume and Issue: 81(6), P. 687 - 698

Published: March 5, 2024

Machine learning occupies a central position in the modeling of fish distribution patterns. The augmentation explanatory variables habitat through many kinds observational methodologies necessitates discernment an optimal combination these for modeling. We proposed feature selection technique, recursive elimination with cross-validation (RFECV), to determine combinations yellowfin tuna Pacific Ocean. Four tree-based models, random forest, eXtreme Gradient Boosting, Light Boosting Machine, and categorical boosting driven by RFECV, were developed using comprehensive fisheries biotic/abiotic data. Habitat including sea temperature, dissolved oxygen concentration, chlorophyll-a salinity, surface height identified as significant features all models. models trained corresponding selected variables, employed predict spatiotemporal from 1995 2019. results obtained could inform useful knowledge sustainable exploitation Ocean furnish benchmark machine-learning-based other pelagic species.

Language: Английский

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Dealing with area‐to‐point spatial misalignment in species distribution models

Ecography, Journal Year: 2024, Volume and Issue: 2024(5)

Published: March 22, 2024

Species distribution models (SDMs) are extensively used to estimate species–environment relationships (SERs) and predict species across space time. For this purpose, it is key choose relevant spatial grains for predictor response variables at the onset of modelling process. However, environmental often derived from large‐scale climate a grain that can be coarser than one variable. Such area‐to‐point misalignment bias estimates SER jeopardise robustness predictions. We virtual approach, running simulations different levels seek statistical solutions problem. specifically compared accuracy predictive performances, assessed degrees heterogeneity in conditions, three SDMs: GLM, GLM Berkson error model (BEM) accounts fine‐grain within coarse‐grain cells. Only BEM accurately relatively data (up 50 times grain), while two GLMs provide flattened SER. all perform poorly when predicting data, particularly environments more heterogeneous training conditions. Conversely, decreasing relative dataset reduces biases. Because predictions made covariate‐grain displays lower performance GLMs. Thus, standard selection methods would fail select best SERs (here, BEM), which could lead false interpretations about drivers distributions. Overall, we conclude BEM, because robustly grain, holds great promise overcome misalignment.

Language: Английский

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Geostatistical Models for Identifying Juvenile Fish Hotspots in Marine Conservation

Springer proceedings in mathematics & statistics, Journal Year: 2025, Volume and Issue: unknown, P. 349 - 362

Published: Jan. 1, 2025

Language: Английский

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Unveiling Land Use Dynamics: Insights from a Hierarchical Bayesian Spatio-Temporal Modelling of Compositional Data

Journal of Agricultural Biological and Environmental Statistics, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 12, 2025

Language: Английский

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MIRA: Myocardial Insulin Resistance App for clinical practice

Computer Methods and Programs in Biomedicine, Journal Year: 2025, Volume and Issue: 263, P. 108674 - 108674

Published: Feb. 14, 2025

Language: Английский

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Estimating effects of ocean environmental conditions on summer flounder (Paralichthys dentatus) distribution

Environmental and Ecological Statistics, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 18, 2025

Language: Английский

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