%0 Journal Article %J Ecological Applications %D 2013 %T Congruence in demersal fish, macro invertebrate and macroalgal community turnover on shallow temperate reefs %A Russell J. Thomson %A Nicole A. Hill %A Rebecca Leaper %A Nick Ellis %A C Roland Pitcher %A Neville Barrett %A Graham J. Edgar %X

To support coastal planning through improved understanding of patterns of biotic and abiotic surrogacy at broad scales, we used Gradient Forest Modelling (GFM) to analyse and predict spatial patterns of compositional turnover of demersal fishes, macro invertebrates and macroalgae on shallow temperate Australian reefs. Predictive models were first developed using environmental surrogates with estimates of prediction uncertainty, and then the efficacy of the three assemblages as bio-surrogates for each other was assessed.

Data from underwater visual surveys of subtidal rocky reefs were collected from the south-eastern coastline of continental Australia (including South Australia and Victoria) and northern coastline of Tasmania. These data were combined with 0.01°-resolution gridded environmental variables to develop statistical models of compositional turnover (beta diversity) using GFM. GFM extends the machine learning, ensemble tree-based method of Random Forests (RF), to allow the simultaneous modelling of multiple taxa. The models were used to generate predictions of compositional turnover for each of the three assemblages within unsurveyed areas across the 6600 km of coastline in the region of interest.

The most important predictor for all three assemblages was variability (measured as standard deviation from measures taken interannually) in sea surface temperature. Spatial predictions of compositional turnover within unsurveyed areas across the region of interest were remarkably congruent across the three taxa. However, the greatest uncertainty in these predictions varied in location between the different assemblages. Pairwise congruency comparisons of observed and predicted turnover between the three assemblages showed that invertebrate and macroalgal biodiversity were most similar, followed by fishes and macroalgae, and lastly fishes and invertebrate biodiversity, suggesting that of the three assemblages, macroalgae would make the best bio-surrogate for both invertebrate and fish compositional turnover.

%B Ecological Applications %P 130717092154007 %8 01 Jul 2013 %G eng %U http://www.esajournals.org/doi/abs/10.1890/12-1549.1 %! Ecological Applications %R 10.1890/12-1549.1 %0 Journal Article %J Journal of Biogeography %D 2012 %T Biophysical patterns in benthic assemblage composition across contrasting continental margins off New Zealand %A Compton, Tanya J. %A Bowden, David A. %A C Roland Pitcher %A Judi E Hewitt %A Nick Ellis %K Beta diversity %K biodiversity mapping %K conservation planning %K Continental shelf %K continental slope %K marine biogeography %K random forest %K spatial planning. %X

Aim
To examine whether benthic assemblages are more diverse in a region of high topographic and oceanographic complexity by comparing benthic invertebrate assemblages across continental margins with contrasting environments.
Location
Challenger Plateau and Chatham Rise, to the west and east of New Zealand, respectively. Methods Benthic faunal data were sourced from extensive seabed surveys in 2007, when both margins were sampled with an epibenthic sled and a towed video system. Three methods were used to investigate benthic assemblages in relation to environmental variables: one based on individual species distribution models (SDMs) using boosted regression trees analysis (BRT), and two community-based modelling methods using generalized dissimilarity modelling (GDM) and gradient forest analysis (GF), respectively. Each method was used to model and predict the turnover in assemblages with respect to environment – the ‘biophysical patterns’ – across the study region.
Results
Across Chatham Rise, a complex oceanographic environment arising from steep gradients in productivity and temperature at the Subtropical Front produced a high diversity of assemblages associated with the sub-Antarctic water mass, the Subtropical Front, steep-sloping regions and fast tidal currents. In contrast, Challenger Plateau lies entirely beneath a single (subtropical) water mass, and assemblage diversity was lower, with a distinctive assemblage on the plateau itself and a deep-water assemblage similar to the northern deep-water assemblage of Chatham Rise. Across both regions, assemblage turnover was fastest in cold waters, at shallow depths and in deep mixed layers.
Main conclusions
Benthic assemblages were more varied on Chatham Rise than on Challenger Plateau, supporting the hypothesis that environmentally heterogeneous margins have higher assemblage diversity. Differing assemblages on the northern and southern flanks of Chatham Rise suggest a biogeographical boundary for benthic taxa across the Subtropical Front. These results demonstrate that oceanographically and topographically complex margins have a diverse assemblage structure that should be considered in planning for the sustainable management of diversity.

 

%B Journal of Biogeography %8 01 Sep 2012 %U http://onlinelibrary.wiley.com/doi/10.1111/j.1365-2699.2012.02761.x/full %! J. Biogeogr. %R 10.1111/j.1365-2699.2012.02761.x %0 Journal Article %J Journal of Applied Ecology %D 2012 %T Exploring the role of environmental variables in shaping patterns of seabed biodiversity composition in regional-scale ecosystems %A C Roland Pitcher %A Lawton, P. %A Nick Ellis %A Smith, Stephen J. %A Lewis, S. Incze %A Wei, C-L. %A Greenlaw, M.E. %A Wolff, N.H. %A Sameoto, J.A. %A Snelgrove, P.V.R %K beta diversity; community ecology %K Conservation %K environmental surrogates %K habitat suitability modelling %K spatial planning and management %K species distribution modelling %X

Environmental variables are often used as indirect surrogates for mapping biodiversity because species survey data are scant at regional scales, especially in the marine realm. However, environmental variables are measured on arbitrary scales unlikely to have simple, direct relationships with biological patterns. Instead, biodiversity may respond nonlinearly and to interactions between environmental variables.

To investigate the role of the environment in driving patterns of biodiversity composition in large marine regions, we collated multiple biological survey and environmental data sets from tropical NE Australia, the deep Gulf of Mexico and the temperate Gulf of Maine. We then quantified the shape and magnitude of multispecies responses along >30 environmental gradients and the extent to which these variables predicted regional distributions. To do this, we applied a new statistical approach, Gradient Forest, an extension of Random Forest, capable of modelling nonlinear and threshold responses.

The regional-scale environmental variables predicted an average of 13–35% (up to 50–85% for individual species) of the variation in species abundance distributions. Important predictors differed among regions and biota and included depth, salinity, temperature, sediment composition and current stress. The shapes of responses along gradients also differed and were nonlinear, often with thresholds indicative of step changes in composition. These differing regional responses were partly due to differing environmental indicators of bioregional boundaries and, given the results to date, may indicate limited scope for extrapolating bio-physical relationships beyond the region of source data sets.

Synthesis and applications. Gradient Forest offers a new capability for exploring relationships between biodiversity and environmental gradients, generating new information on multispecies responses at a detail not available previously. Importantly, given the scarcity of data, Gradient Forest enables the combined use of information from disparate data sets. The gradient response curves provide biologically informed transformations of environmental layers to predict and map expected patterns of biodiversity composition that represent sampled composition better than uninformed variables. The approach can be applied to support marine spatial planning and management and has similar applicability in terrestrial realms.

%B Journal of Applied Ecology %I British Ecological Society %V 49 %P 670-679 %U http://onlinelibrary.wiley.com/doi/10.1111/j.1365-2664.2012.02148.x/abstract %R 10.1111/j.1365-2664.2012.02148.x %0 Journal Article %J Ecology %D 2012 %T Gradient forests: calculating importance gradients on physical predictors %A Nick Ellis %A Smith, Stephen J. %A C Roland Pitcher %K Biodiversity %K community data %K compositional turnover %K Great Barrier Reef %K random forests %K variable importance %X

In ecological analyses of species and community distributions there is interest in
the nature of their responses to environmental gradients and in identifying the most important
environmental variables, which may be used for predicting patterns of biodiversity. Methods
such as random forests already exist to assess predictor importance for individual species and
to indicate where along gradients abundance changes. However, there is a need to extend these
methods to whole assemblages, to establish where along the range of these gradients the
important compositional changes occur, and to identify any important thresholds or change
points. We develop such a method, called ‘‘gradient forest,’’ which is an extension of the
random forest approach. By synthesizing the cross-validated R2 and accuracy importance
measures from univariate random forest analyses across multiple species, sampling devices,
and surveys, gradient forest obtains a monotonic function of each predictor that represents the
compositional turnover along the gradient of the predictor. When applied to a synthetic data
set, the method correctly identified the important predictors and delineated where the
compositional change points occurred along these gradients. Application of gradient forest to
a real data set from part of the Great Barrier Reef identified mud fraction of the sediment as
the most important predictor, with highest compositional turnover occurring at mud fraction
values around 25%, and provided similar information for other predictors. Such refined
information allows for more accurate capturing of biodiversity patterns for the purposes of
bioregionalization, delineation of protected areas, or designing of biodiversity surveys.

%B Ecology %V 93 %P 156 - 168 %8 01 Jan 2012 %U http://www.esajournals.org/doi/abs/10.1890/11-0252.1 %N 1 %! Ecology %R 10.1890/11-0252.1 %0 Conference Paper %B ICES CM 2011/G:06 %D 2011 %T Analysis of relationships between seabed species/assemblages and their physical environment using Random Forests statistical methods %A C Roland Pitcher %E Nick Ellis %Y Lawton, P. %Y Incze, L.S. %Y Smith, Stephen J. %Y Shirley, T.C. %Y Wei, C-L. %Y Rowe, G.T. %Y Wolff, N.H. %Y Greenlaw, M.E. %Y Sameoto, J.A. %K beta diversity prediction and mapping %K Great Barrier Reef %K Gulf of Maine %K Gulf of Mexico %X

Conference Title: Exploring patterns of compositional change along environmental gradients, and mapping expected patterns of biodiversity composition at regional scale. Abstract Environmental variables are increasingly used as indirect surrogates for mapping patterns of biodiversity because species survey data are scant, especially in the marine realm. This Census of Marine Life cross‐program synthesis quantified the shapes and magnitude of multiple species responses to >30 environmental gradients, and the extent to which these variables were useful as predictors for mapping patterns or biodiversity composition.

%B ICES CM 2011/G:06 %8 01 Jan 2011 %0 Journal Article %J Ecosphere %D 2011 %T Predictions of beta diversity for reef macroalgae across southeastern Australia %A Rebecca Leaper %A Nicole A. Hill %A Graham J. Edgar %A Nick Ellis %A E Lawrence %A C Roland Pitcher %A Neville Barrett %A Russell J. Thomson %K Beta diversity %K conservation planning %K generalized dissimilarity modeling %K gradient forest modeling %K macroalgae %K subtidal rocky reefs %X
We analyzed and predicted spatial patterns of turnover in macroalgal community composition (beta diversity) that accounted for broad-scale environmental gradients using two contrasting community modelling methods, Generalised Dissimilarity Modelling (GDM) and Gradient Forest Modelling (GFM). Percentage cover data from underwater macroalgal surveys of subtidal rocky reefs along the southeastern coastline of continental Australia and northern coastline of Tasmania were combined with 0.01°-resolution gridded environmental variables, to develop statistical models of beta diversity. GDM, a statistical approach based on a matrix regression, and GFM, a machine learning approach based on ensemble tree based methods, were used to fit models and generate predictions of beta diversity within unsurveyed areas across the region of interest. Patterns of macroalgal beta diversity predicted by both methods were remarkably congruent and showed a similar and striking change in community composition from eastern South Australia to western Victoria and northern Tasmania. Macroalgal communities differed markedly in predicted composition between the open coast and inshore locations. A distinct algal community was predicted for the enclosed Port Philip Bay in Victoria. Sea surface temperature standard deviation and average contributed most to changes in beta diversity for both the GDM and GFM models; changes in wave exposure and oxygen also influenced beta diversity in the GDM model, while salinity and exposure contributed substantially to the GFM model. The GDM and GFM analyses allowed us to model and predict spatial patterns of beta diversity in macroalgal communities comprising >180 species over 6600 km of coastline. These outputs advance regional-scale conservation management by allowing planners to interpolate from point source ecological data to assess the distribution of biodiversity across their full domain of interest. The congruence between methods suggests that strong environmental gradients related to temperature and exposure are the common drivers of community change in this region.

 

%B Ecosphere %I Ecosphere %V 2 %P art73 %8 01 Jul 2011 %U http://www.esajournals.org/doi/abs/10.1890/ES11-00089.1 %N 7 %! Ecosphere %R 10.1890/ES11-00089.1 %0 Online Database %D 2010 %T Predicted seabed assemblage patterns of marine fauna in the East Marine Region (EMR) - Product Description %A Nick Ellis %A C Roland Pitcher %A Sharples, R. %X

This product provides planners and managers with the most recent and complete information about the predicted seabed assemblage patterns of marine fauna, at a range of scales, in the EMR, based on extensive analyses of species responses to the physical environment. It can be used as follows:  1. To produce maps of predicted patterns of seabed assemblage of marine fauna (i.e. benthic invertebrates and demersal fish combined) in the EMR; 2. To provide the results of scientific analysis of extensive biological data to planners and managers with the responsibility to conserve and manage seabed biodiversity in the EMR (e.g. MPA planning and management); 3. As a biologically informed data input to models of the marine environment in the EMR, where appropriate (e.g. Marxan); and 4. To identify areas of highest priority for future seabed biodiversity surveys, the findings of which can be compared with these predictions of seabed assemblage patterns of marine fauna in the EMR.

Notes on entries above:
URL field – contains link to datasets
Item field – launches a pdf document of additional information (product description)

%I CSIRO %U http://www.marine.csiro.au/marq/edd_search.Browse_Citation?txtSession=8631 %0 Online Database %D 2010 %T Predicted seabed assemblage patterns of marine fauna in the South-East Marine Region (SEMR) - Product Description %A Nick Ellis %A C Roland Pitcher %X This product provides planners and managers with the most recent and complete information about the predicted seabed assemblage patterns of marine fauna, at a range of scales, in the SEMR, based on extensive analyses of species responses to the physical environment. It can be used as follows: 1. To produce maps of predicted patterns of seabed assemblage of marine fauna (i.e. benthic invertebrates and demersal fish combined) in the SEMR; 2. To provide the results of scientific analysis of extensive biological data to planners and managers with the responsibility to conserve and manage seabed biodiversity in the SEMR (e.g. MPA planning and management); 3.As a biologically informed data input to models of the marine environment in the SEMR, where appropriate (e.g. Marxan); and 4. To identify areas of highest priority for future seabed biodiversity surveys, the findings of which can be compared with these predictions of seabed assemblage patterns of marine fauna in the SEMR. Notes on entries above: URL field – contains link to datasets Item field – launches a pdf document of additional information (product description) Version: 10 March 2010 %I CSIRO %U http://www.marine.csiro.au/marq/edd_search.Browse_Citation?txtSession=8755 %0 Generic %D 2010 %T The role of physical environmental variables in shaping seabed biodiversity patterns %A C Roland Pitcher %A Nick Ellis %A Lawton, P. %A Smith, Stephen J. %A Wei, C-L. %A Incze, L.S. %A Greenlaw, M.E. %A Sameoto, J.A. %A Wolff, N.H. %A Shirley T.C. %A Rowe G.T. %0 Report %D 2009 %T Mapping Seabed Habitats and Biodiversity of the Continental Shelf of the Great Barrier Reef Region %A C Roland Pitcher %A Browne, M. %A Venables W. %A Nick Ellis %A Doherty, P.J. %A Hooper J.N.A. %A Gribble N. %0 Report %D 2009 %T Meso-scale biophysical characterisation of large marine regions for management planning %A C Roland Pitcher %A Nick Ellis %0 Online Database %D 2009 %T Predicted seabed assemblage patterns of marine fauna in the North-West Marine Region (NWMR) - Product Description %A Nick Ellis %A C Roland Pitcher %A E Lawrence %X

This product provides planners and managers with the most recent and complete information about the predicted seabed assemblage patterns of marine fauna, at a range of scales, in the NWMR, based on extensive analyses of species responses to the physical environment. It can be used as follows:  1. To produce maps of predicted patterns of seabed assemblage of marine fauna (i.e. benthic invertebrates and demersal fish combined) in the NWMR; 2. To provide the results of scientific analysis of extensive biological data to planners and managers with the responsibility to conserve and manage seabed biodiversity in the NWMR (e.g. MPA planning and management); 3. As a biologically informed data input to models of the marine environment in the NWMR, where appropriate (e.g. Marxan); and 4. To identify areas of highest priority for future seabed biodiversity surveys, the findings of which can be compared with these predictions of seabed assemblage patterns of marine fauna in the NWMR.

Notes on entries above:
URL field – contains link to datasets
Item field – launches a pdf document of additional information (product description)

Version: 26 November 2009

%I CSIRO %U http://www.marine.csiro.au/marq/edd_search.Browse_Citation?txtSession=8593 %0 Online Database %D 2009 %T Predicted seabed assemblage patterns of marine fauna in the North Marine Region (NMR) - Product Description %A Nick Ellis %A C Roland Pitcher %X

This product provides planners and managers with the most recent and complete information about the predicted seabed assemblage patterns of marine fauna, at a range of scales, in the NMR, based on extensive analyses of species responses to the physical environment. It can be used as follows:  1. To produce maps of predicted patterns of seabed assemblage of marine fauna (i.e. benthic invertebrates and demersal fish combined) in the NMR; 2. To provide the results of scientific analysis of extensive biological data to planners and managers with the responsibility to conserve and manage seabed biodiversity in the NMR (e.g. MPA planning and management); 3. As a biologically informed data input to models of the marine environment in the NMR, where appropriate (e.g. Marxan); and 4. To identify areas of highest priority for future seabed biodiversity surveys, the findings of which can be compared with these predictions of seabed assemblage patterns of marine fauna in the NMR.

Notes on entries above:
URL field – contains link to datasets
Item field – launches a pdf document of additional information (product description)

Version:  1 September 2009

%I CSIRO %U http://www.marine.csiro.au/marq/edd_search.Browse_Citation?txtSession=8519 %0 Online Database %D 2009 %T Predicted seabed assemblage patterns of marine fauna in the South-West Marine Region (SWMR) - Product Description %A Nick Ellis %A C Roland Pitcher %X

This product provides planners and managers with the most recent and complete information about the predicted seabed assemblage patterns of marine fauna, at a range of scales, in the SWMR, based on extensive analyses of species responses to the physical environment. It can be used as follows: 1. To produce maps of predicted patterns of seabed assemblage of marine fauna (i.e. benthic invertebrates and demersal fish combined) in the SWMR; 2. To provide the results of scientific analysis of extensive biological data to planners and managers with the responsibility to conserve and manage seabed biodiversity in the SWMR (e.g. MPA planning and management); 3. As a biologically informed data input to models of the marine environment in the SWMR, where appropriate (e.g. Marxan); and 4. To identify areas of highest priority for future seabed biodiversity surveys, the findings of which can be compared with these predictions of seabed assemblage patterns of marine fauna in the SWMR.

Notes on entries above:
URL field – contains link to datasets
Item field – launches a pdf document of additional information (product description)

Version: 1 September 2009

%I CSIRO %U http://www.marine.csiro.au/marq/edd_search.Browse_Citation?txtSession=8526 %0 Report %D 2008 %T Mapping Seabed Habitats and Biodiversity on the Continental Shelf of the Great Barrier Reef (GBR) region by biophysical prediction %A C Roland Pitcher %A Browne, M. %A Venables W. %A Nick Ellis %A Doherty, P.J. %A Hooper J.N.A. %A Gribble N.