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HIERARCHICAL SPATIAL MODELS FOR PREDICTING TREE SPECIES ASSEMBLAGES ACROSS LARGE DOMAINS.

ABSTRACT: Spatially explicit data layers of tree species assemblages, referred to as forest types or forest type groups, are a key component in large-scale assessments of forest sustainability, biodiversity, timber biomass, carbon sinks and forest health monitoring. This paper explores the utility of coupling georeferenced national forest inventory (NFI) data with readily available and spatially complete environmental predictor variables through spatially-varying multinomial logistic regression models to predict forest type groups across large forested landscapes. These models exploit underlying spatial associations within the NFI plot array and the spatially-varying impact of predictor variables to improve the accuracy of forest type group predictions. The richness of these models incurs onerous computational burdens and we discuss dimension reducing spatial processes that retain the richness in modeling. We illustrate using NFI data from Michigan, USA, where we provide a comprehensive analysis of this large study area and demonstrate improved prediction with associated measures of uncertainty.

SUBMITTER: Finley AO

PROVIDER: S-EPMC2846086 | biostudies-literature | 2009 Sep

REPOSITORIES: biostudies-literature

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HIERARCHICAL SPATIAL MODELS FOR PREDICTING TREE SPECIES ASSEMBLAGES ACROSS LARGE DOMAINS.

Finley Andrew O AO Banerjee Sudipto S McRoberts Ronald E RE

The annals of applied statistics 20090901 3

Spatially explicit data layers of tree species assemblages, referred to as forest types or forest type groups, are a key component in large-scale assessments of forest sustainability, biodiversity, timber biomass, carbon sinks and forest health monitoring. This paper explores the utility of coupling georeferenced national forest inventory (NFI) data with readily available and spatially complete environmental predictor variables through spatially-varying multinomial logistic regression models to ...[more]

PMID: 20352037

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Project description:Gastropod assemblages from nearshore rocky habitats were studied over large spatial scales to (1) describe broad-scale patterns in assemblage composition, including patterns by feeding modes, (2) identify latitudinal pattern of biodiversity, i.e., richness and abundance of gastropods and/or regional hotspots, and (3) identify potential environmental and anthropogenic drivers of these assemblages. Gastropods were sampled from 45 sites distributed within 12 Large Marine Ecosystem regions (LME) following the NaGISA (Natural Geography in Shore Areas) standard protocol (www.nagisa.coml.org). A total of 393 gastropod taxa from 87 families were collected. Eight of these families (9.2%) appeared in four or more different LMEs. Among these, the Littorinidae was the most widely distributed (8 LMEs) followed by the Trochidae and the Columbellidae (6 LMEs). In all regions, assemblages were dominated by few species, the most diverse and abundant of which were herbivores. No latitudinal gradients were evident in relation to species richness or densities among sampling sites. Highest diversity was found in the Mediterranean and in the Gulf of Alaska, while highest densities were found at different latitudes and represented by few species within one genus (e.g. Afrolittorina in the Agulhas Current, Littorina in the Scotian Shelf, and Lacuna in the Gulf of Alaska). No significant correlation was found between species composition and environmental variables (r?0.355, p>0.05). Contributing variables to this low correlation included invasive species, inorganic pollution, SST anomalies, and chlorophyll-a anomalies. Despite data limitations in this study which restrict conclusions in a global context, this work represents the first effort to sample gastropod biodiversity on rocky shores using a standardized protocol across a wide scale. Our results will generate more work to build global databases allowing for large-scale diversity comparisons of rocky intertidal assemblages.

Project description:Background and aimsThe relative role of Pleistocene climate changes in driving the geographic distribution and genetic diversity of South American species is not well known, especially from open biomes such as the Cerrado, the most diverse tropical savanna, encompassing high levels of endemism. Here the effects of Quaternary climatic changes on demographic history, distribution dynamics and genetic diversity of Dimorphandra mollis, an endemic tree species widely distributed in the Cerrado, were investigated.MethodsA total of 38 populations covering most of the distribution of D. mollis were analysed using internal transcribed spacer (ITS) sequences and nuclear microsatellite variation [simple sequence repeats (SSRs)]. The framework incorporated statistical phylogeography, coalescent analyses and ecological niche modelling (ENM).Key resultsDifferent signatures of Quaternary climatic changes were found for ITS sequences and SSRs corresponding to different time slices. Coalescent analyses revealed large and constant effective population sizes, with high historical connectivity among the populations for ITS sequences and low effective population sizes and gene flow with recent population retraction for SSRs. ENMs indicated a slight geographical range retraction during the Last Glacial Maximum. A large historical refugium across central Brazil was predicted. Spatially explicit analyses showed a spatial cline pattern in genetic diversity related to the paleodistribution of D. mollis and to the centre of its historical refugium.ConclusionsThe complex genetic patterns found in D. mollis are the result of a slight geographical range retraction during the Last Glacial Maximum followed by population expansion to the east and south from a large refugium in the central part of the Cerrado. This historical refugium is coincident with an area predicted to be climatically stable under future climate scenarios. The identified refugium should be given high priority in conservation polices to safeguard the evolutionary potential of the species under predicted future climatic changes.

Dataset Information

HIERARCHICAL SPATIAL MODELS FOR PREDICTING TREE SPECIES ASSEMBLAGES ACROSS LARGE DOMAINS.

Publications

HIERARCHICAL SPATIAL MODELS FOR PREDICTING TREE SPECIES ASSEMBLAGES ACROSS LARGE DOMAINS.

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