Estimating Size and Composition of Biological Communities by Modeling the Occurrence of Species

By R. Dorazio, J. Andrew Royle. 2005

548 citations recorded by the source corpus.

We develop a model that uses repeated observations of a biological community to estimate the number and composition of species in the community. Estimators of community-level attributes are constructed from model-based estimators of occurrence of individual species that incorporate imperfect detection of individuals. Data from the North American Breeding Bird Survey are analyzed to illustrate the variety of ecologically important quantities that are easily constructed and estimated using our model-based estimators of species occurrence. In particular, we compute site-specific estimates of species richness that honor classical notions of species-area relationships. We suggest extensions of our model to estimate maps of occurrence of individual species and to compute inferences related to the temporal and spatial dynamics of biological communities.

Subfields: Ecology, Nature and Landscape Conservation, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/5226d45ef21ebdac77a64d6d075a61d352bbb8d9

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In Ecology

In Nature and Landscape Conservation

In Statistics and Probability

Citations in the corpus

Assessing global patterns in mammalian carnivore occupancy and richness by integrating local camera trap surveys (2017, methodology): “We incorporated site-level (i.e., camera station-specific) characteristics affecting species-specific occurrence probabilities using a generalized linear mixed modelling approach (Dorazio & Royle, 2005).”
Using camera trapping and hierarchical occupancy modelling to evaluate the spatial ecology of an African mammal community (2016, methodology): “As a result, we modelled among species correlation (q) between a0i and b0i by specifying the two parameters to be jointly distributed (Dorazio & Royle 2005; K ery & Royle 2008).”
Imperfect detection is the rule rather than the exception in plant distribution studies (2013, methodology): “STATIST ICAL METHODS We applied a multispecies site-occupancy model (Dorazio & Royle 2005; Russell et al. 2009; Zipkin, Dewan & Royle 2009) to our data of two samples of 100 species from 451 sites.”
Estimating species richness: calibrating a large avian monitoring programme (2005, methodology): “A more elegant and efficient analysis would be to account directly for structural zeroes by building a combined likelihood for all species, quadrats and years ( Dorazio & Royle 2005 ) and declaring some visits as providing only missing 108 M. Kery & H. Schmid”
Hierarchical Bayes estimation of species richness and occupancy in spatially replicated surveys (2008, methodology): “Dorazio et al. (2006) and Royle et al (2007) developed a parameterization of the unconditional likelihood using a data-augmentation approach, the benefit of which is that it yields to a simple Bayesian implementation.”
Occupancy dynamics in a tropical bird community: unexpectedly high forest use by birds classified as non‐forest species (2010, methodology): “We used a recently developed hierarchical modelling approach that estimates community-level attributes through species-speciﬁc estimates of occupancy, detection and colonization (Dorazio & Royle 2005; Dorazio et al. 2006; Royle & Dorazio 2008; Ke´ry et al. 2009).”
Analysis of Multinomial Models With Unknown Index Using Data Augmentation (2007, methodology): “Seventy-five species of birds were detected along this route, and there was considerable variation among species in the observed frequencies of detection at each site (Dorazio and Royle 2005a).”
Advances and applications of occupancy models (2014, background): “Wehave chosen to focuson thosemodels that involve a single to a few target species, but we acknowledge that there is a rich literature involving the occurrence of multiple species (e.g. Dorazio & Royle 2005; K ery & Royle 2009; Zipkin, Dewan & Royle 2009).”
Making Great Leaps Forward: Accounting for Detectability in Herpetological Field Studies (2007, background): “To address this issue, Dorazio and Royle (2005), Dorazio et al. (2006), and Royle et al. (2007) have developed animal community models by specifying species-specific models of occurrence.”
Joint dynamic species distribution models: a tool for community ordination and spatio‐temporal monitoring (2016, methodology): “Joint SDMs are an alternative procedure for simultaneously modelling the distribution of multiple species within a community, and were originally developed to estimate total species richness when accounting for imperfect species detection (Dorazio & Royle, 2005).”
WHAT ARE THE ISSUES WITH PRESENCE–ABSENCE DATA FOR WILDLIFE MANAGERS? (2005, methodology): “Indeed, it was this type of practical situation that Tyre et al. (2003) envisioned when suggesting the use of a zero-inflated binomial model (see also Wintle et al. 2004, Field et al. 2005).”
Predicting species distributions from checklist data using site‐occupancy models (2010, background): “Site-occupancy models also form the basis for the recently developed models for inference about communities, both static (Dorazio & Royle, 2005; Gelfand et al., 2005; Dorazio et al., 2006; Latimer et al., 2006; Kéry & Royle, 2008a,b) and dynamic (Kéry et al., 2009; Dorazio et al., in press).”
Accounting for detectability improves estimates of species richness in tropical bat surveys (2011, background): “Recently developed hierarchical multispecies site-occupancy models (Dorazio & Royle 2005; Kéry & Royle 2008; Kéry et al. 2009) provide a somewhat more versatile framework as they retain species identity across sites and thus allow for the simultaneous estimation of assemblage-wide and…”
Inference About Species Richness and Community Structure Using Species-Specific Occupancy Models in the National Swiss Breeding Bird Survey MHB (2009, methodology): “Modelling this correlation can easily be justified on grounds of the expected relationships between quadrat-specific abundance and occupancy on the one hand and abundance-induced heterogeneity in quadrat-specific detection probability on the other (Royle and Nichols 2003; Dorazio and Royle 2005).”