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Aim: Studies of climate change impacts on animal distributions typically consider only the direct impacts of a changing climate, under the assumption that future areas of suitable climate will otherwise remain ecologically suitable. Here we assess both the direct and indirect impacts of climate change on rain forest ant communities, where substantial shifts are projected to occur for both climate and habitat types. Location: Australian Wet Tropics (AWT). Methods: Generalized Dissimilarity Modelling was used to model turnover in the composition of ~300 rain forest ant species sampled at 150 sites across six mountains spanning five degrees of latitude. Ants were sampled within the leaf litter, on the litter surface and on tree trunks. By modelling the rate of turnover among sites, we determined the current effective area of similar ecological environments (SEE) for ant communities and consequently the expected change in species persistence when habitat shifts under two climate projections for the years 2035, 2055 and 2085. We compared results when SEE considered only the direct effects of a changing climate, with those when changes in habitat were also included. Results: The rain forest ant communities modelled will lose a substantial proportion of SEE under both climate change scenarios. Under combined direct (changes in climate) and indirect (changes in rain forest habitat) impacts of climate change, >90% of the communities in the region were predicted to lose up to half their similar habitats compared with 50%–60% when only considering direct climate change impacts. The highest risk of reduction in area of SEE is projected in drier inland areas of the AWT, where profound compositional turnover is linked to dramatic changes in rain forest vegetation, including extensive conversion into savanna. Main conclusions: Our findings emphasize the importance of incorporating habitat suitability into future projections of species turnover under a changing climate, particularl

Aim: Observed, realized niche space often underestimates species’ physiological tolerances due to interactions with other species, dispersal constraints, and because some combinations of influential environmental factors do not currently exist in the real world. Conversely, correlative ecological niche models rely on the assumption that the range of environmental conditions encompassed by a species’ geographic distribution accurately reflects their environmental tolerances, including community-level approaches like generalized dissimilarity modelling (GDM). We extend GDM to better understand what effect broader environmental tolerances could have on compositional turnover under climate change. Innovation: We show how GDM can be adjusted as a function of best-available estimates of the average ratio between realized and potential niche widths to modify projected temporal turnover. We demonstrate this approach by using the estimated niche ratios of Australian plant species (n = 7,184) relative to thermal extremes, and the rate at which this ratio varied with temperature. The modified GDMs showed existing thermal tolerance could reduce the turnover predicted by standard models under climate change by up to 11%. We further show how the reduction in expected turnover by 2090 will influence where a greater proportion of the current community will persist in a region. Main conclusions: We suggest that standard spatial GDMs and their modified versions represent the extremes of ecological niche perspectives (i.e., realized and potential) and the range of tolerance communities may have when responding to environmental change. GDM projections therefore identify the range of uncertainty associated with a critical model assumption, and as climate change continues, ongoing community monitoring could be used to validate the balance between the two possibilities.

A key debate in contemporary ecology concerns whether ecosystem functions such as productivity are distinctly influenced by biological diversity in natural environments. Recent work has emphasised the importance of links between ecosystem functions and measures of ecological specialisation as proxies of biodiversity. However, few studies have analysed these relationships at broad spatio-temporal scales. This research tests the empirical relationship between primary productivity and ecological specialisation at continental and bioregional scales, using two proxies of specialisation: taxonomic β-diversity and site-level environmental niche width. It also examines how the environmental niches of species vary across continental environmental gradients. Gross primary productivity (GPP) may be influenced not only by the biological diversity at each location (α-diversity) but also by the biological turnover between locations (β-diversity). Generalized additive models were used to test whether the magnitude or variability of GPP were distinctly influenced by either taxonomic α- or β-diversity across continental Australia, over and above environmental influences. Neither α- nor β-diversity improved the explanatory power of GPP models beyond that of environment-only models. The realised environmental niches of species are important indicators of ecological specialisation and biogeographic history. Bivariate regression models were used to test whether species niches vary across continental environmental gradients for 1771 vascular plants from the Australian Wet Tropics. The temperature niches of these species did not vary substantially. However, niches were narrower in drier and less fertile environments. The macroecological complementarity hypothesis predicts that locations with greater ecological specialisation —those with collectively narrower niches — should be more productive than locations with less ecological specialisation. For pairs of environmentally similar Wet Trop

Land‐use change is one of the biggest threats to biodiversity globally. The effects of land use on biodiversity manifest primarily at local scales which are not captured by the coarse spatial grain of current global land‐use mapping. Assessments of land‐use impacts on biodiversity across large spatial extents require data at a similar spatial grain to the ecological processes they are assessing. Here, we develop a method for statistically downscaling mapped land‐use data that combines generalized additive modeling and constrained optimization. This method was applied to the 0.5° Land‐use Harmonization data for the year 2005 to produce global 30″ (approx. 1 km2) estimates of five land‐use classes: primary habitat, secondary habitat, cropland, pasture, and urban. The original dataset was partitioned into 61 bio‐realms (unique combinations of biome and biogeographical realm) and downscaled using relationships with fine‐grained climate, land cover, landform, and anthropogenic influence layers. The downscaled land‐use data were validated using the PREDICTS database and the geoWiki global cropland dataset. Application of the new method to all 61 bio‐realms produced global fine‐grained layers from the 2005 time step of the Land‐use Harmonization dataset. Coarse‐scaled proportions of land use estimated from these data compared well with those estimated in the original datasets (mean R2: 0.68 ± 0.19). Validation with the PREDICTS database showed the new downscaled land‐use layers improved discrimination of all five classes at PREDICTS sites (P < 0.0001 in all cases). Additional validation of the downscaled cropping layer with the geoWiki layer showed an R2 improvement of 0.12 compared with the Land‐use Harmonization data. The downscaling method presented here produced the first global land‐use dataset at a spatial grain relevant to ecological processes that drive changes in biodiversity over space and time. Integrating these data with biodiversity measures will enable the re