Abstract
One of the primary ways in which climate change will impact coastal freshwater wetlands is through changes in the frequency, intensity, timing and distribution of extreme weather events. Disentangling the direct and indirect mechanisms of population- and community-level responses to extreme events is vital to predicting how species composition of coastal wetlands will change under future conditions. We extended static structural equation modelling approaches to incorporate system dynamics in a multi-year multispecies occupancy model to quantify the effects of extreme weather events on a coastal freshwater wetland system. We used data from an 8-year study (2009-2016) on St. Marks National Wildlife Refuge in Florida, USA, to quantify species-specific and community-level changes in amphibian and fish occupancy associated with two flooding events in 2012 and 2013. We examine how physical changes to the landscape, including potential changes in salinity and increased wetland connectivity, may have contributed to or exacerbated the effects of these extreme weather events on the biota of isolated coastal wetlands. We provide evidence that the primary effects of flooding on the amphibian community were through indirect mechanisms via changes in the composition of the sympatric fish community that may have had lethal (i.e. through direct predation) or non-lethal (i.e. through direct or indirect competitive interactions) effects. In addition, we have shown that amphibian species differed in their sensitivity to direct flooding effects and indirect changes in the fish community and wetland-specific conductance, which led to variable responses across the community. These effects led to the overall decline in amphibian species richness from 2009 to 2016, suggesting that wetland-breeding amphibian communities on St. Marks National Wildlife Refuge may not be resilient to predicted changes in coastal disturbance regimes because of climate change. Understanding both direct and indirect effects, as well as species interactions, is important for predicting the effects of a changing climate on individual species, communities and ecosystems.
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