Canning Adam D, Waltham Nathan J
Centre for Tropical Water and Aquatic Ecosystem Research (TropWATER) James Cook University Townsville Qld Australia.
Ecol Evol. 2021 Mar 24;11(10):5244-5254. doi: 10.1002/ece3.7412. eCollection 2021 May.
Wetlands are among the most vulnerable ecosystems, stressed by habitat loss and degradation from expanding and intensifying agricultural and urban areas. Climate change will exacerbate the impacts of habitat loss by altering temperature and rainfall patterns. Wetlands within Australia's Great Barrier Reef (GBR) catchment are not different, stressed by extensive cropping, urban expansion, and alteration for grazing. Understanding how stressors affect wildlife is essential for the effective management of biodiversity values and minimizing unintended consequences when trading off the multiple values wetlands support. Impact assessment is difficult, often relying on an aggregation of ad hoc observations that are spatially biased toward easily accessible areas, rather than systematic and randomized surveys. Using a large aggregate database of ad hoc observations, this study aimed to examine the influence of urban proximity on machine-learning models predicting taxonomic richness and assemblage turnover, relative to other habitat, landscape, and climate variables, for vertebrates dwelling in the wetlands of the GBR catchment. The distance from the nearest city was, by substantial margins, the most influential factor in predicting the richness and assemblage turnover of all vertebrate groups, except fish. Richness and assemblage turnover was predicted to be greatest nearest the main urban centers. The extent of various wetland habitats was highly influential in predicting the richness of all groups, while climate (predominately the rainfall in the wettest quarter) was highly influential in predicting assemblage turnover for all groups. Bias of survey records toward urban centers strongly influenced our ability to model wetland-affiliated vertebrates and may obscure our understanding of how vertebrates respond to habitat loss and climate change. This reinforces the need for randomized and systematic surveys to supplement existing ad hoc surveys. We urge modelers in other jurisdictions to better portray the potential influence of survey biases when modeling species distributions.
湿地是最脆弱的生态系统之一，受到农业和城市区域扩张及集约化导致的栖息地丧失和退化的压力。气候变化将通过改变温度和降雨模式加剧栖息地丧失的影响。澳大利亚大堡礁（GBR）集水区内的湿地也不例外，受到广泛种植、城市扩张和放牧改造的压力。了解压力源如何影响野生动物对于有效管理生物多样性价值以及在权衡湿地所支持的多种价值时尽量减少意外后果至关重要。影响评估很困难，通常依赖于临时观测的汇总，这些观测在空间上偏向于容易到达的区域，而不是系统的随机调查。本研究利用一个大型临时观测汇总数据库，旨在研究相对于其他栖息地、景观和气候变量，城市 proximity 对预测分类丰富度和群落周转的机器学习模型的影响，这些模型针对居住在GBR集水区湿地的脊椎动物。除鱼类外，到最近城市的距离在很大程度上是预测所有脊椎动物类群丰富度和群落周转的最有影响力的因素。预计在主要城市中心附近丰富度和群落周转最大。各种湿地栖息地的范围在预测所有类群的丰富度方面具有高度影响力，而气候（主要是最湿润季度的降雨量）在预测所有类群的群落周转方面具有高度影响力。调查记录对城市中心的偏向强烈影响了我们对与湿地相关的脊椎动物进行建模的能力，并可能掩盖我们对脊椎动物如何应对栖息地丧失和气候变化的理解。这强化了进行随机和系统调查以补充现有临时调查的必要性。我们敦促其他司法管辖区的建模者在对物种分布进行建模时更好地描绘调查偏差的潜在影响。
