A data-driven method for selecting candidate reference sites for stream bioassessment programs using generalised dissimilarity models

Rose, Peter M.; Kennard, Mark J.; Sheldon, Fran; Moffatt, David B.; Butler, Gavin L.

doi:10.1071/mf14254

Cited by 13 publications

(18 citation statements)

References 65 publications

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“…; Rose et al . ), climate‐change impact assessment (Ferrier, Harwood & Williams ; Prober et al . ), and for visualising spatial patterns in community composition (Ferrier et al .…”

Section: Introductionmentioning

confidence: 99%

Multi‐site generalised dissimilarity modelling: using zeta diversity to differentiate drivers of turnover in rare and widespread species

2017

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Summary Generalised dissimilarity modelling (GDM) applies pairwise beta diversity as a measure of species turnover with the purpose of explaining changes in species composition under changing environments or along environmental gradients. Beta diversity only captures turnover across pairs of sites and, therefore, disproportionately represents turnover in rare species across communities. By contrast, zeta diversity, the average number of shared species across multiple sites, captures the full spectrum of rare, intermediate and widespread species as they contribute differently to compositional turnover. We show how integrating zeta diversity into GDMs (which we term multi‐site generalised dissimilarity modelling, MS‐GDM), provides a more information rich approach to modelling how communities respond to environmental variation and change. We demonstrate the value of including zeta diversity in biodiversity assessment and modelling using BirdLife Australia Atlas data. Zeta diversity values for different numbers of sites (the order of zeta) are regressed against environmental differences and distance using two kinds of regressions: shape constrained additive models and a combination of I‐splines and generalised linear models. Applying MS‐GDM to different orders of zeta revealed shifts in the importance of environmental variables in explaining species turnover, varying with the order of zeta and thus with the level of co‐occurrence of the species and, by extension, their commonness and rarity. In particular, precipitation gradients emerged as drivers in the turnover of rare species, whereas temperature gradients were more important drivers of turnover in widespread species. Appreciation of the factors that drive compositional turnover across multiple sites is necessary for accommodating the full spectrum of compositional turnover across rare to common species. This extends beyond understanding drivers for pairwise beta diversity only. MS‐GDM provides a valuable addition to the toolkit of GDM, with further potential for survey gap analysis and prediction of species composition in unsampled sites.

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“…; Rose et al . ), climate‐change impact assessment (Ferrier, Harwood & Williams ; Prober et al . ), and for visualising spatial patterns in community composition (Ferrier et al .…”

Section: Introductionmentioning

confidence: 99%

Multi‐site generalised dissimilarity modelling: using zeta diversity to differentiate drivers of turnover in rare and widespread species

2017

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“…These extensions were first demonstrated by Ferrier, Drielsma, Manion, and Watson () although not often applied—see examples by Brown, Cameron, Yoder, and Vences () for terrestrial vertebrates in Madagascar and Rose et al. () for freshwater fish in eastern Australia. Insights drawn from this data‐driven approach to inform conservation decisions are discussed below.…”

Section: Discussionmentioning

confidence: 93%

“…In eastern Australia, modelling by Growns () explained 21%, 36% and 41% of DE for invertebrates, frogs and fish respectively; while Rose et al. () explained 33% for fish. Unexplained variation will be due to missing or poorly represented environmental variables in the models correlated with ecological/evolutionary processes such as historical biogeography, competitive niche‐differentiation and disturbance heterogeneity (Loeuille & Leibold, ; Urban, ).…”

Section: Discussionmentioning

confidence: 96%

“…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%

“…Quantitative maps of aquatic biodiversity have been derived using freshwater taxa (e.g. Abell et al, 2008), abiotic surrogates (Higgins et al, 2005;Pusey et al, 2009;Soranno et al, 2010) or combined environmental and biological datasets (Growns, 2009;Leathwick et al, 2011;Rose, Kennard, Sheldon, Moffatt, & Butler, 2016). A tool for the latter is generalised dissimilarity modelling (GDM) (Ferrier, 2002;Ferrier, Manion, Elith, & Richardson, 2007).…”

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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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Spatially Structured Communities

Fletcher

Fortin

2018

Spatial Ecology and Conservation Modeling

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A data-driven method for selecting candidate reference sites for stream bioassessment programs using generalised dissimilarity models

Cited by 13 publications

References 65 publications

Multi‐site generalised dissimilarity modelling: using zeta diversity to differentiate drivers of turnover in rare and widespread species

Multi‐site generalised dissimilarity modelling: using zeta diversity to differentiate drivers of turnover in rare and widespread species

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

Spatially Structured Communities

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