Models and maps: predicting the distribution of corals and other benthic macro‐invertebrates in shelf habitats

Krigsman, Lisa M.; Yoklavich, Mary M.; Dick, Edward J.; Cochrane, Guy R.

doi:10.1890/es11-00295.1

Cited by 28 publications

(13 citation statements)

References 31 publications

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“…However, in many cases the covariates may not be available for the entire spatial domain (e.g. the presence of biogenic habitats in marine systems; Krigsman et al 2012) or an appropriate parametric relationship between the covariate and species may be unknown. In such cases, the flexibility provided by SFA is preferable to estimating an ad hoc functional form.…”

Section: Discussionmentioning

confidence: 99%

Spatial factor analysis: a new tool for estimating joint species distributions and correlations in species range

et al. 2015

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Summary1 Predicting and explaining the distribution and density of species is one of the oldest concerns in ecology. Species distributions can be estimated using geostatistical methods, which estimate a latent spatial variable explaining observed variation in densities, but geostatistical methods may be imprecise for species with low densities or few observations. Additionally, simple geostatistical methods fail to account for correlations in distribution among species and generally estimate such cross-correlations as a post hoc exercise. 2 We therefore present spatial factor analysis (SFA), a spatial model for estimating a low-rank approximation to multivariate data, and use it to jointly estimate the distribution of multiple species simultaneously. We also derive an analytic estimate of cross-correlations among species from SFA parameters. 3 As a first example, we show that distributions for 10 bird species in the breeding bird survey in 2012 can be parsimoniously represented using only five spatial factors. As a second case study, we show that forward prediction of catches for 20 rockfishes (Sebastes spp.) off the U.S. West Coast is more accurate using SFA than analysing each species individually. Finally, we show that single-species models give a different picture of cross-correlations than joint estimation using SFA. 4 Spatial factor analysis complements a growing list of tools for jointly modelling the distribution of multiple species and provides a parsimonious summary of cross-correlation without requiring explicit declaration of habitat variables. We conclude by proposing future research that would model species cross-correlations using dissimilarity of species' traits, and the development of spatial dynamic factor analysis for a low-rank approximation to spatial time-series data.

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Section: Discussionmentioning

confidence: 99%

Spatial factor analysis: a new tool for estimating joint species distributions and correlations in species range

et al. 2015

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“…) or for the identification of vulnerable and critical habitat (Krigsman et al. ), they have rarely been integrated into models of fish distribution (Robinson et al. , Shelton et al.…”

Section: Discussionmentioning

confidence: 99%

Space‐time investigation of the effects of fishing on fish populations

Ono

Shelton

Ward

et al. 2016

Ecological Applications

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Species distribution models (SDMs) are important statistical tools for obtaining ecological insight into species-habitat relationships and providing advice for natural resource management. Many SDMs have been developed over the past decades, with a focus on space- and more recently, time-dependence. However, most of these studies have been on terrestrial species and applications to marine species have been limited. In this study, we used three large spatio-temporal data sources (habitat maps, survey-based fish density estimates, and fishery catch data) and a novel space-time model to study how the distribution of fishing may affect the seasonal dynamics of a commercially important fish species (Pacific Dover sole, Microstomus pacificus) off the west coast of the USA. Dover sole showed a large scale change in seasonal and annual distribution of biomass, and its distribution shifted from mid-depth zones to inshore or deeper waters during late summer/early fall. In many cases, the scale of fishery removal was small compared to these broader changes in biomass, suggesting that seasonal dynamics were primarily driven by movement and not by fishing. The increasing availability of appropriate data and space-time modeling software should facilitate extending this work to many other species, particularly those in marine ecosystems, and help tease apart the role of growth, natural mortality, recruitment, movement, and fishing on spatial patterns of species distribution in marine systems.

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“…The use of habitat variables in species distribution modeling and biomass estimation has a long history in terrestrial ecosystems, but basic habitat information has been lacking in marine ecosystems until the past decades or so. While these data have been used to inform the location of marine reserves Vanderklift 1999, Sundblad et al 2011) or for the identification of vulnerable and critical habitat (Krigsman et al 2012), they have rarely been integrated into models of fish distribution (Robinson et al 2011, Shelton et al 2014. In this study, we took advantage of newly available spatio-temporal data on fish density and habitat and incorporated it into a space-time model to identify potential causal mechanisms (fishing or other biological mechanisms such as ontogenetic movement, recruitment, natural mortality, and growth) explaining the seasonal dynamics of Dover sole across the U.S. west coast.…”

Section: Discussionmentioning

confidence: 99%

Space-time investigation of the effects of fishing on fish populations

Ono¹,

Shelton²,

Ward³

et al. 2015

Ecological Applications

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27Species distribution models (SDMs) are important statistical tools for obtaining ecological 28 insight into species-habitat relationships, and providing advice for natural resource management. 29Many SDMs have been developed over the past decades, with a focus on space-and more 30 recently, time-dependence. However, most of these studies have been on terrestrial species and 31 applications to marine species have been limited. In this study, we used three large spatio-32 temporal data sources (habitat maps, survey-based fish density estimates, and fishery catch data) 33 and a novel space-time model to study how the distribution of fishing may affect the seasonal 34 dynamics of a commercially important fish species (Pacific Dover sole, Microstomus pacificus) 35 off the US West coast. Dover sole showed a large scale change in seasonal and annual 36 distribution of biomass and its distribution shifted from mid-depth zones to inshore or deeper 37 waters during late summer/early fall. In many cases, the scale of fishery removal was small 38 compared to these broader changes in biomass, suggesting that seasonal dynamics were 39 primarily driven by movement and not by fishing. The increasing availability of appropriate data 40 and space-time modeling software should facilitate extending this work to many other species -41 particularly those in marine ecosystems -and help tease apart the role of growth, natural 42 mortality, recruitment, movement, and fishing on spatial patterns of species distribution in 43 marine systems. 44 45 A central aim in conservation is to preserve important habitats for organisms to ensure 48 species and population persistence in the face of anthropogenic threats (ESA 1973, Kareiva et al. 49 2008). Species distribution models have proven vital as tools for expanding our understanding of 50 species habitat associations and for conservation planning and resource management (Guisan and 51 Thuiller 2005, Elith and Leathwick 2009). Many methods have been developed over the past 52 several decades to model the distribution of species in relation to habitat. Statistical techniques 53 include generalized linear models (GLMs), generalized additive models (GAMs), quantile 54 regression, artificial neural networks, regression trees, and genetic algorithms (see reviews by 55 Guisan and Thuiller 2005, Elith and Leathwick 2009). To date, however, many of these models 56 have failed to consider space and time dependence (Dormann 2007, Hoeting 2009). Explicitly 57 accounting for space-time dependence is crucial for understanding current and future threats 58 from anthropogenic forces, and for reducing the risks of erroneous results as many exogenous 59 (e.g. climate, habitat) and endogenous (e.g. dispersal, predation) drivers of species distributions 60 are likely to vary through time and space (Legendre 1993, Hoeting 2009, Cressie and Wikle 61 2011). 62 Despite the importance of spatial and temporal processes in biology, the use of space-time 63 models has remained limited due to their presumed complexity an...

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Models and maps: predicting the distribution of corals and other benthic macro‐invertebrates in shelf habitats

Cited by 28 publications

References 31 publications

Spatial factor analysis: a new tool for estimating joint species distributions and correlations in species range

Spatial factor analysis: a new tool for estimating joint species distributions and correlations in species range

Space‐time investigation of the effects of fishing on fish populations

Space-time investigation of the effects of fishing on fish populations

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