Comment: Statistical Dependence in Stream Networks

Cressie, Noel; O'Donnell, David

doi:10.1198/jasa.2009.ap09530

Cited by 5 publications

(5 citation statements)

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“…For the regional dataset spanning 20 river networks, all the significant predictors were biotic interaction variables, but they only explained a small proportion of the variance in mussel abundance. In contrast, the tail‐down autocovariance component, which is applicable only to organisms that can move upstream against the river flow, as fish do (Cressie & O'Donnell, ), accounted for a larger fraction of variance (see Table ). For the sedentary mussel in the parasite–host system, the tail‐down autocovariate is an indication of the interaction between parasite and host.…”

Section: Discussionmentioning

confidence: 99%

“…Here, different mathematical functions (for equations, see Ver Hoef et al ., ) distinguish between flow‐connected and flow‐unconnected pairs of sites in the network. In general, tail‐up autocovariance models the downstream transport of materials and organisms in the river network, whereas tail‐down autocovariance is only applicable to organisms that can move upstream, against the river flow (Cressie & O'Donnell, ).…”

Section: Methodsmentioning

confidence: 99%

“…In statistical models for river networks, this process has been called a ‘tail‐up’ source of autocovariance (Cressie et al ., ; Ver Hoef et al ., ). Second, the upstream movement of organisms, as exhibited by fish (Cressie & O'Donnell, ), is an ecological process that can cause spatial autocorrelation within a river network; this process has been called ‘tail‐down’ autocovariance (Ver Hoef et al ., ; Ver Hoef & Peterson, ). Because post‐juvenile mussels, the target of our analyses, are sedentary organisms with limited ability to move upstream, it is of special interest to evaluate whether there is a tail‐down component of autocorrelation, which would indicate upstream dispersal of the mussel by its host fish during the mussel's parasitic glochidial stage.…”

Section: Introductionmentioning

confidence: 99%

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Spatial extent of biotic interactions affects species distribution and abundance in river networks: the freshwater pearl mussel and its hosts

Lois

Cowley

Outeiro

et al. 2014

Journal of Biogeography

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Aim At what spatial extent are biotic interactions discernible influences on the distribution and abundance of species in river networks? We address this question with analyses of data from river networks for Margaritifera margaritifera, a freshwater mussel that passes its larval stage attached to a host fish.Location Twenty river networks in Galicia, north-western Spain.Methods A maximum-entropy approach was implemented to model the species' distribution. Geostatistical mixed models were used to analyse the mussel's abundance in dendritic river networks. Predictor variables included the abundance and biomass of host fish (biotic interactions) and abiotic predictors for climate, geology and land-form.Results Maxent models of species distribution were improved by 4.5% in terms of the area under the receiver-operating characteristic curve (AUC) by the inclusion of biotic interactions. Host-fish predictors contributed 63% of the Maxent model prediction of mussel presence. A geostatistical mixed model explained 52% of the variance in mussel abundance when including all the mussel abundance sites in the study area; abiotic predictors had no significance and salmonid biomass and resident trout population density were the only significant biotic predictors, together explaining 2.4% of the variance. An autocovariate representing biotic interactions between mussels and fish explained 11.7% of the variance. Using only sites where migratory host fish were present (n = 149), a mixed model explained more variance (78%) and the contribution from the autocovariate for parasite-host interactions was about three times larger than for the model including all sites (n = 419). The spatial autocorrelation from mussel-fish interactions had a spatial extent (geostatistical range) greater than 15 km.Main conclusions Interactions between mussels and their larval hosts in river networks are manifested in spatial patterns of species distribution and abundance in this region, encompassing 20 river networks. The directional topology of dendritic river networks strongly supports the upstream dispersal of mussels by parasitized host fish as a component of spatial autocorrelation in mussel abundance.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Spatial extent of biotic interactions affects species distribution and abundance in river networks: the freshwater pearl mussel and its hosts

Lois

Cowley

Outeiro

et al. 2014

Journal of Biogeography

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“…Flow data that were used here were not observed but modelled and supplied by the SEPA. Observed flow data would allow a model to adapt to different flow settings over time, as observed by Cressie and O'Donnell (2010). However, here it is the flow ratios which are the crucial quantities.…”

Section: Discussionmentioning

confidence: 99%

Flexible Regression Models Over River Networks

O'Donnell

Rushworth

Bowman

et al. 2013

Journal of the Royal Statistical Society Series C: Applied Statistics

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Many statistical models are available for spatial data but the vast majority of these assume that spatial separation can be measured by Euclidean distance. Data which are collected over river networks constitute a notable and commonly occurring exception, where distance must be measured along complex paths and, in addition, account must be taken of the relative flows of water into and out of confluences. Suitable models for this type of data have been constructed based on covariance functions. The aim of the paper is to place the focus on underlying spatial trends by adopting a regression formulation and using methods which allow smooth but flexible patterns. Specifically, kernel methods and penalized splines are investigated, with the latter proving more suitable from both computational and modelling perspectives. In addition to their use in a purely spatial setting, penalized splines also offer a convenient route to the construction of spatiotemporal models, where data are available over time as well as over space. Models which include main effects and spatiotemporal interactions, as well as seasonal terms and interactions, are constructed for data on nitrate pollution in the River Tweed. The results give valuable insight into the changes in water quality in both space and time.

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“…This process has been labelled the "tail-up" source of autocovariance in river networks (Cressie et al 2006, Ver Hoef et al 2006. Second, upstream movement of organisms against the river flow, as with fish for example (Cressie and O'Donnell 2010), has been labelled "tail-down" thesis abstract Modelling and predicting biogeographical patterns in river networks autocovariance (Ver Hoef et al 2006, Ver Hoef and. Thus, any biogeographical study in rivers is likely to benefit by accounting for the contribution of these two ecosystem processes to species distribution and abundance.…”

Section: Introductionmentioning

confidence: 99%

Modelling and predicting biogeographical patterns in river networks

Lois¹

2016

Frontiers of Biogeography

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Statistical analysis and interpretation of biogeographical phenomena in rivers is now possible using a spatially explicit modelling framework, which has seen significant developments in the past decade. I used this approach to identify a spatial extent (geostatistical range) in which the abundance of the parasitic freshwater pearl mussel (Margaritifera margaritifera L.) is spatially autocorrelated in river networks. I show that biomass and abundance of host fish are a likely explanation for the autocorrelation in mussel abundance within a 15-km spatial extent. The application of universal kriging with the empirical model enabled precise prediction of mussel abundance within segments of river networks, something that has the potential to inform conservation biogeography. Although I used a variety of modelling approaches in my thesis, I focus here on the details of this relatively new spatial stream network model, thus advancing the study of biogeographical patterns in river networks.

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Comment: Statistical Dependence in Stream Networks

Cited by 5 publications

References 6 publications

Spatial extent of biotic interactions affects species distribution and abundance in river networks: the freshwater pearl mussel and its hosts

Spatial extent of biotic interactions affects species distribution and abundance in river networks: the freshwater pearl mussel and its hosts

Flexible Regression Models Over River Networks

Modelling and predicting biogeographical patterns in river networks

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