Ecological impact assessment of climate change and habitat loss on wetland vertebrate assemblages of the Great Barrier Reef catchment and the influence of survey bias

Abstract

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.

Island size predicts mammal diversity in insular environments, except for land‐bridge islands

“…For instance, there are currently 191 hydroelectric dams operating only across the Amazon basin, with other 246 already planned to be built in the following years (Lees et al, 2016). Different anthropogenic stressors may further threaten forest species diversity in archipelago landscapes, such as cropping, urban expansion, grazing, climate change, logging, hunting, and species introduction (Canning & Waltham, 2021; Ferreira Neto et al, 2021; Rickart et al, 2016). Understanding the magnitude and strength that island area predicts species diversity in different insular environments is central to implementing effective practical management actions and proposing guidelines for reserve design in different island environments (Lomolino & Weiser, 2001).…”

Will fencing floodplain and riverine wetlands from feral pig damage conserve fish community values?

“…The cultural and ecological value of coastal wetlands means that management intervention is increasingly necessary to ensure they remain productive and viable habitat (Creighton et al, 2015 ; Canning & Waltham, 2021 ). Overall, these data support a model that damage to wetlands from pig activities not only contributes to reduced aquatic habitat, through loss of aquatic vegetation communities, but also probably has secondary impacts including water temperature and asphyxiation risks for many hours each day, that are higher than when compared to fenced wetlands (Figure 5 ).…”

Rediscovering wild food to diversify production across Australia's agricultural landscapes

“…ALA is a large database that collates sightings of animals from a wide range of organisations and contributors (Belbin and Williams, 2016). Given the likely differences in survey method and intensity among observers (Canning and Waltham, 2021), which could reduce the reliability of abundance data, this analysis only examined the presence of a species, rather than the abundance. Furthermore, surveys could not be used to indicate species absence.…”