{"title":"Models of bee responses to land use and land cover changes in agricultural landscapes – a review and research agenda","authors":"Abdelhak Rouabah, Chantal Rabolin-Meinrad, Camille Gay, Olivier Therond","doi":"10.1111/brv.13109","DOIUrl":null,"url":null,"abstract":"<div>\n \n <p>Predictive modelling tools can be used to support the design of agricultural landscapes to promote pollinator biodiversity and pollination services. Despite the proliferation of such modelling tools in recent decades, there remains a gap in synthesising their main characteristics and representation capacities. Here, we reviewed 42 studies that developed non-correlative models to explore the impact of land use and land cover changes on bee populations, and synthesised information about the modelled systems, modelling approaches, and key model characteristics like spatiotemporal extent and resolution. Various modelling approaches are employed to predict the biodiversity of bees and the pollination services they provide, with a prevalence of models focusing on wild populations compared to managed ones. Of these models, landscape indicators and distance decay models are relatively simple, with few parameters. They allow mapping bee visitation probabilities using basic land cover data and considering bee foraging ranges. Conversely, mechanistic or agent-based models delineate, with varying degrees of complexity, a multitude of processes that characterise, among others, the foraging behaviour and population dynamics of bees. The reviewed models collectively encompass 38 ecological, agronomic, and economic processes, producing various outputs including bee abundance, habitat visitation rate, and crop yield. To advance the development of predictive modelling tools aimed at fostering pollinator biodiversity and pollination services in agricultural landscapes, we highlight future avenues for increasing biophysical realism in models predicting the impact of land use and land cover changes on bees. Additionally, we address the challenges associated with balancing model complexity and practical usability.</p>\n </div>","PeriodicalId":133,"journal":{"name":"Biological Reviews","volume":"99 6","pages":"2003-2021"},"PeriodicalIF":11.0000,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biological Reviews","FirstCategoryId":"99","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/brv.13109","RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Predictive modelling tools can be used to support the design of agricultural landscapes to promote pollinator biodiversity and pollination services. Despite the proliferation of such modelling tools in recent decades, there remains a gap in synthesising their main characteristics and representation capacities. Here, we reviewed 42 studies that developed non-correlative models to explore the impact of land use and land cover changes on bee populations, and synthesised information about the modelled systems, modelling approaches, and key model characteristics like spatiotemporal extent and resolution. Various modelling approaches are employed to predict the biodiversity of bees and the pollination services they provide, with a prevalence of models focusing on wild populations compared to managed ones. Of these models, landscape indicators and distance decay models are relatively simple, with few parameters. They allow mapping bee visitation probabilities using basic land cover data and considering bee foraging ranges. Conversely, mechanistic or agent-based models delineate, with varying degrees of complexity, a multitude of processes that characterise, among others, the foraging behaviour and population dynamics of bees. The reviewed models collectively encompass 38 ecological, agronomic, and economic processes, producing various outputs including bee abundance, habitat visitation rate, and crop yield. To advance the development of predictive modelling tools aimed at fostering pollinator biodiversity and pollination services in agricultural landscapes, we highlight future avenues for increasing biophysical realism in models predicting the impact of land use and land cover changes on bees. Additionally, we address the challenges associated with balancing model complexity and practical usability.
期刊介绍:
Biological Reviews is a scientific journal that covers a wide range of topics in the biological sciences. It publishes several review articles per issue, which are aimed at both non-specialist biologists and researchers in the field. The articles are scholarly and include extensive bibliographies. Authors are instructed to be aware of the diverse readership and write their articles accordingly.
The reviews in Biological Reviews serve as comprehensive introductions to specific fields, presenting the current state of the art and highlighting gaps in knowledge. Each article can be up to 20,000 words long and includes an abstract, a thorough introduction, and a statement of conclusions.
The journal focuses on publishing synthetic reviews, which are based on existing literature and address important biological questions. These reviews are interesting to a broad readership and are timely, often related to fast-moving fields or new discoveries. A key aspect of a synthetic review is that it goes beyond simply compiling information and instead analyzes the collected data to create a new theoretical or conceptual framework that can significantly impact the field.
Biological Reviews is abstracted and indexed in various databases, including Abstracts on Hygiene & Communicable Diseases, Academic Search, AgBiotech News & Information, AgBiotechNet, AGRICOLA Database, GeoRef, Global Health, SCOPUS, Weed Abstracts, and Reaction Citation Index, among others.