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• W2887757963 abstract "Anthropogenic land use is a major driver of biodiversity loss, with different land use activities having a range of impacts on native communities. These myriad impacts make it difficult to identify the key drivers of species declines, especially across heterogenous anthropogenic environments. Our study aims to identify whether the species and traits being lost in disturbed environments differ across a land-use intensity gradient, in order to prioritise management effort in Greater Brisbane, Australia. We applied List Length Analysis (LLA) to standardise citizen-collected avian records, and model the change in prevalence for 182 bird species within urban, rural and forested environments. We then tested whether understorey-nesting, ground-nesting, insectivorous or small-bodied functional groups were significantly declining in prevalence within the entire avian assemblage. We found a greater probability of decline for small-bodied and understorey-nesting species in urban environments, lending support to established findings that, in urban environments of Greater Brisbane, competition with larger territorial birds and understorey loss are impacting communities. Our study also highlighted that the species declining and increasing in prevalence differed across the land use intensity gradient. Management approaches should therefore be targeted to mitigate the distinct impacts associated with particular land uses. In Greater Brisbane, managers should focus on maintaining urban understories and monitoring overabundant avian competitors. Where funds are limited, LLA represents a useful tool to harness non-standardised data, to guide early management and monitoring effort. Such tools equip managers to conserve biodiversity in anthropogenic environments." @default.
• W2887757963 created "2018-08-22" @default.
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• W2887757963 date "2018-11-01" @default.
• W2887757963 modified "2023-10-10" @default.
• W2887757963 title "Understanding avian assemblage change within anthropogenic environments using citizen science data" @default.
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• W2887757963 doi "https://doi.org/10.1016/j.landurbplan.2018.07.006" @default.
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