Compositional patterns in terrestrial fauna and wetland flora and fauna across the Pilbara biogeographic region of Western Australia and the representativeness of its conservation reserve system

Gibson, L.A.; Williams, Kristen J.; Pinder, Adrian; Harwood, Thomas D.; McKenzie, N.L.; Ferrier, Simon; Lyons, M.N.; Burbidge, A.H.; Manion, Glenn

doi:10.18195/issn.0313-122x.78(2).2015.515-545

Cited by 12 publications

(16 citation statements)

References 36 publications

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“…Model 1, using site‐measured and spatial variables, explained 73% of DE in compositional similarity, compared to 61% for Model 2, which only used spatial variables. The latter is similar to those found for terrestrial and aquatic biodiversity in the Pilbara region of Australia (46% and 59% respectively—Gibson et al., ). Leathwick et al.…”

Section: Discussionsupporting

confidence: 88%

“…This led to the need to develop spatial variables representing the hydrological landscape and connectivity. Few other studies have developed such contextual spatial predictors for modelling aquatic systems (Kennard et al, 2010;Rose et al, 2016) or addressed the linkage between terrestrial-aquatic environments and the aquatic biota enabling the whole landscape to be classified (Gibson et al, 2015). Inclusion of these contextual predictors resulted in a model that explained a greater proportion of aquatic biodiversity patterning than previous applications of GDM (Gibson et al, 2015;Growns, 2009;Leathwick et al, 2011;Rose et al, 2016).…”

Section: Discussionmentioning

confidence: 99%

“…We, therefore, used the extrapolation index developed by Gibson et al. () to identify and include only reliable predictions in the classification of GDM Model 2; in this case, values equal or below 0.1 (Figure S8). The index is calculated as the absolute sum of the transformed grid values for each spatial predictor outside the input data range of the fitted model.…”

Section: Methodsmentioning

confidence: 99%

“…Applying the method outlined in Gibson et al. () to Model 2, we derived a continuous metric describing the spatial distribution of formal reserve representativeness specific to the analysis region (Equation ).

{normalR}_{i} = \frac{A^{reserved}}{A^{total}} = \frac{{false\sum}_{j = 1}^{j = n} s_{i j} h_{j}}{{false\sum}_{j = 1}^{j = n} s_{i j}}

where R i , the proportional protection of ecological environments (scaled by aquatic invertebrate data) similar to cell i in conservation reserves, is given by A reserved , the area in reserves, which is calculated as the sum of similarities ( s ij ) multiplied by a binary index h j (0 outside reserves, 1 inside reserves), divided by A total , calculated as the sum of similarities ( s ij ) for all n cells ( n = 978,354). This measure can be thought of as estimating the average proportion of original habitat supporting the natural distribution of a species that is represented somewhere within a reserve.…”

Section: Methodsmentioning

confidence: 99%

“…For the reserve analysis only formal reserves were assessed. Applying the method outlined in Gibson et al (2015) to…”

Section: Step 3a Refined Gdm Models For Conservation Assessmentmentioning

confidence: 99%

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Whole‐landscape modelling of compositional turnover in aquatic invertebrates informs conservation gap analysis: An example from south‐western Australia

Pennifold¹,

Williams

Pinder³

et al. 2017

Freshwater Biology

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Freshwater aquatic ecosystems are in decline due to intensifying land use, salinisation, water abstraction and climate change. Understanding compositional patterns in aquatic biota is a useful step towards better management of aquatic ecosystems. We used generalised dissimilarity modelling (GDM) to predict compositional turnover in riverine invertebrate fauna (primarily insects) as a function of environment. Conceptual understanding of major drivers of aquatic invertebrate species distribution helped decide which predictor variables to source and include. Five groups of environmental variables—waterscape, local habitat, climate, landscape and disturbance—were derived from either spatial layers or in situ (site) measurements. Predictive models and variance partitioning tests of variable groups demonstrated the importance of representing all conceptual drivers of ecological pattern and process. As expected, waterscape variables were independently the most important group, followed by local habitat and landscape variables; with complex interactions between groups. Climate variables independently contributed the least. To determine the information content for mapping patterns, we investigated the independent and combined contribution of site‐measured and spatial predictors. Even though predictive models developed using only site‐measured variables or only spatial variables explained around the same amount of deviance (DE), combined they increased explained model DE by 11.2%. Compositional dissimilarities between the 51 surveyed site pairs predicted by the model using only spatial variables were highly correlated (r2 = .85) with dissimilarities predicted using site and spatial variables. These results support the use of the spatial model for conservation decisions. The spatial model was used to evaluate representativeness of both the conservation reserve network and biological monitoring locations. The location of aquatic monitoring sites was uneven, with comprehensive coverage south and coastward, and less representative of inland environments. Proportional protection of ecological environments (scaled by riverine invertebrate taxa) was found to vary between 20% and 30%, being higher in southern parts where more land has been allocated to reserves and less in northern and inland parts. This demonstrated local progress towards achieving the Convention on Biological Diversity's Aichi Target 11 for inland waters. These results provide a focus for improving the robustness of information used in decisions affecting the conservation of aquatic biodiversity, including places to target to fill gaps in the reserve network and additional aquatic monitoring locations (supporting Convention on Biological Diversity's Aichi Target 19). The GDM‐based approach to characterising ecological environments, provided a first quantitative foundation for comprehensively evaluating the conservation status of freshwater ecosystems in south‐western Australia. Potential future applications include assessing the ecological implication...

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Section: Discussionsupporting

confidence: 88%