Predicting Suitable Spatial Distribution Areas for Urban Trees Under Climate Change Scenarios Using Species Distribution Models: A Case Study of Michelia chapensis

Land, Journal Year: 2025, Volume and Issue: 14(3), P. 638 - 638

Published: March 18, 2025

Climate change has presented considerable challenges in the management of urban forests and trees. Varieties studies have predicted potential changes species distribution by employing single-algorithm models (SDMs) to investigate impacts climate on plant species. However, there is still limited quantitative research suitable ranges commonly used tree Therefore, our study aims optimize traditional SDMs integrating multiple machine learning algorithms propose a framework for identifying trees under change. We took Michelia chapensis, particular significance southern China, as pilot evolution its range context two future scenarios (SSP126 SSP585) across four periods (2030s, 2050s, 2070s, 2090s). The findings indicated that ensemble SDM showed strong predictive capacity, with an area curve (AUC) value 0.95. chapensis estimated at 15.9 × 105 km2 currently it will expand most areas according projection. contract southeastern Yunnan, central Guangdong, Sichuan Basin, northern Hubei, Jiangxi, etc. location current located Hengyang, Hunan (27.36° N, 112.34° E), projected shift westward future. migration magnitude positively correlated intensity These provide scientific basis landscape planning chapensis. Furthermore, proposed can be seen valuable tool predicting response change, providing insights proactive adaptation management.

Language: Английский

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Application of geographic information system and remote sensing technology in ecosystem services and biodiversity conservation

Elsevier eBooks, Journal Year: 2025, Volume and Issue: unknown, P. 97 - 122

Published: Jan. 1, 2025

Language: Английский

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Integrating population genetics and species distribution models to predict red seabream distribution under climate change

Global Ecology and Conservation, Journal Year: 2025, Volume and Issue: unknown, P. e03589 - e03589

Published: April 1, 2025

Language: Английский

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Characterising Essential Fish Habitat using spatio‐temporal analysis of fishery data: A case study of the European seabass spawning areas

Fisheries Oceanography, Journal Year: 2021, Volume and Issue: 30(4), P. 413 - 428

Published: Jan. 8, 2021

Abstract Fish habitats sustain essential functions for fish to complete their life cycle, such as feeding, growing and spawning. Conservation is crucial maintain populations exploitation. Since 2013, the spawning stock biomass of northern European seabass ( Dicentrarchus labrax ) has been in a worrying state. A series low recruitments with persistently high level fishing blamed, raising concerns about processes involved reproduction settlement nurseries. Here, we characterise areas along French Atlantic coast using vessel monitoring system (VMS) data. non‐linear geostatistical approach was applied, from 2008 2014, detect locations where aggregate Occurrence maps distribution were combined into probability quantify seasonal inter‐annual variability highlight recurrent, occasional unfavourable areas. We identified three main areas: Rochebonne Plateau Bay Biscay, Western English Channel North Cotentin peninsula Eastern Channel. The correlative link between this geographical environmental factors investigated Bayesian spatio‐temporal model. structure accounted vast majority model predictive skills, whereas covariates had negligible effect. Our revealed persistence spatial intra‐ variability. Offshore appear be seabass, should considered management strategies.

Language: Английский

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RangeShiftR: an R package for individual‐based simulation of spatial eco‐evolutionary dynamics and species' responses to environmental changes

Ecography, Journal Year: 2021, Volume and Issue: 44(10), P. 1443 - 1452

Published: Aug. 29, 2021

Reliably modelling the demographic and distributional responses of a species to environmental changes can be crucial for successful conservation management planning. Process‐based models have potential achieve this goal, but so far they remain underused predictions species' distributions. Individual‐based offer additional capability model inter‐individual variation evolutionary dynamics thus capture adaptive change. We present RangeShiftR, an R implementation flexible individual‐based platform which simulates eco‐evolutionary in spatially explicit way. The package provides fast simulations by making software RangeShifter available widely used statistical programming R. features auxiliary functions support specification analysis results. provide outline package's functionality, describe underlying structure with its main components short example. RangeShiftR offers substantial complexity, especially dispersal processes. It comes elaborate tutorials comprehensive documentation facilitate learning help at all levels. As core code is implemented C++, computations are fast. complete source published under public licence, adaptations contributions feasible. facilitates application mechanistic questions operating powerful simulation from allows effortless interoperation existing packages create streamlined workflows that include data preparation, integrated results analysis. Moreover, strengthens coupling other models.

Language: Английский

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