Models of bee responses to land use and land cover changes in agricultural landscapes – a review and research agenda

IF 11 1区 生物学 Q1 BIOLOGY Biological Reviews Pub Date : 2024-06-28 DOI:10.1111/brv.13109
Abdelhak Rouabah, Chantal Rabolin-Meinrad, Camille Gay, Olivier Therond
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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.

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蜜蜂对农业景观中土地利用和土地覆盖变化的反应模型--综述与研究议程。
预测建模工具可用于支持农业景观设计,以促进授粉生物多样性和授粉服务。尽管近几十年来此类建模工具大量涌现,但在总结其主要特征和表示能力方面仍存在差距。在此,我们回顾了 42 项为探索土地利用和土地覆被变化对蜜蜂种群的影响而开发非相关模型的研究,并综合了有关建模系统、建模方法以及时空范围和分辨率等主要模型特征的信息。我们采用了各种建模方法来预测蜜蜂的生物多样性及其提供的授粉服务,与人工管理的蜜蜂相比,野生蜜蜂种群是建模的重点。在这些模型中,景观指标和距离衰减模型相对简单,参数较少。它们可以利用基本的土地覆盖数据绘制蜜蜂造访概率图,并考虑蜜蜂的觅食范围。相反,机械模型或基于代理的模型则以不同的复杂程度描述了蜜蜂觅食行为和种群动态等多种过程的特征。所审查的模型共包括 38 个生态、农艺和经济过程,产生的各种输出结果包括蜜蜂数量、栖息地访问率和作物产量。为了推动旨在促进授粉者生物多样性和农业景观授粉服务的预测建模工具的发展,我们强调了在预测土地利用和土地覆盖变化对蜜蜂的影响的模型中提高生物物理真实性的未来途径。此外,我们还讨论了与平衡模型复杂性和实际可用性有关的挑战。
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来源期刊
Biological Reviews
Biological Reviews 生物-生物学
CiteScore
21.30
自引率
2.00%
发文量
99
审稿时长
6-12 weeks
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.
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