So Many Variables: Joint Modeling in Community Ecology

Warton, David I.; Blanchet, F. Guillaume; O’Hara, Robert B.; Ovaskainen, Otso; Taskinen, Sara; Walker, Steven C.; Hui, Francis K. C.

doi:10.1016/j.tree.2015.09.007

Cited by 661 publications

(863 citation statements)

References 70 publications

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“…We extend the joint hierarchical model of species abundance in Box 1 of Warton et al [1] by adding a layer to accommodate imperfect detection using measurements derived from repeated surveys over a period when the population is closed.…”

Section: What Is a Detection-based Joint Model For Abundance?mentioning

confidence: 99%

“…The remainder is identical to models in Box 1 of [1], except we relabel their y ij as N ij to clarify that abundance is imperfectly observed and to distinguish true (N ij ) from measured (y ij ) abundance. For the latent variable model from [1] we have: To make the model identifiable, we need repeated abundance measurements (i.e., k>1) for at least some sites, and put some constraints on p ijk (typically site-level covariates or random effects). Analogous models can be specified for occurrence instead of abundance [5][6][7].…”

Section: What Is a Detection-based Joint Model For Abundance?mentioning

confidence: 99%

“…Both types are straightforward to implement in a Bayesian framework. Box 1 illustrates how to adapt the approach in Warton et al [1] to create a detectionbased joint model for abundance.…”

Section: What Is a Detection-based Joint Model For Abundance?mentioning

confidence: 99%

“…Gurutzeta Guillera-Arroita, 2 Elise F. Zipkin,3 Robert M. Dorazio,4 J. Andrew Royle, 5 and Marc Kéry 6 Warton et al [1] advance community ecology by describing a statistical framework that can jointly model abundances (or distributions) across many taxa to quantify how community properties respond to environmental variables. This framework specifies the effects of both measured and unmeasured (latent) variables on the abundance (or occurrence) of each species.…”

mentioning

confidence: 99%

“…These differences in detectability create biases in estimates of abundance, occupancy, and dynamics derived from raw counts of multispecies surveys, which are the basis for the joint modeling framework and the examples given in [1]. Undetected individuals result in underestimation of population size when species are common and in false absences when species are rare.…”

mentioning

confidence: 99%

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Incorporating Imperfect Detection into Joint Models of Communities: A response to Warton et al.

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Section: What Is a Detection-based Joint Model For Abundance?mentioning

confidence: 99%

Section: What Is a Detection-based Joint Model For Abundance?mentioning

confidence: 99%

“…Both types are straightforward to implement in a Bayesian framework. Box 1 illustrates how to adapt the approach in Warton et al [1] to create a detectionbased joint model for abundance.…”

Section: What Is a Detection-based Joint Model For Abundance?mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

See 3 more Smart Citations

Incorporating Imperfect Detection into Joint Models of Communities: A response to Warton et al.

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Habitat area and local habitat conditions outweigh fragmentation effects on insect communities in vineyards

Bosco

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et al. 2023

Ecol Sol and Evidence

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1. Fragmentation of habitat, for example by intensive agricultural practices, can be detrimental to local biodiversity. However, it often remains unclear whether such biodiversity declines are caused by loss of habitat area or increased fragmentation, and how habitat quality factors into it. In our study system, vegetated vineyards are typically small, and isolated from one another, potentially limiting the distribution and dispersal of organisms.2. In a full-factorial experiment of a priori selected vegetated vineyard patches of differing size and fragmentation, we aimed to disentangle the effects of habitat area (area of vegetated vineyards), habitat fragmentation (number of vegetated vineyards per 100 ha) and field-scale ground vegetation density on ground beetle, leafhopper and wild bee communities using a combined framework of multiscale and multispecies modelling (Hierarchical Model of Species Communities).

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Stress‐gradient framework for green roofs: Applications for urban agriculture and other ecosystem services

Rolhauser

MacIvor

Roberto

et al. 2023

Ecol Sol and Evidence

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Green roofs are promoted to contribute to more resilient cities by enhancing urban ecosystem services and food systems. Extensive, low‐maintenance green roofs experience frequent environmental stresses, which reduce plant survival and growth. Stress‐tolerant plants are therefore used to sustain well‐established services, such as building temperature regulation. However, transitioning extensive green roofs to provide other key urban services, such as food production, involves less tolerant plant species. Although facilitation exerted by stress‐tolerant species (nurses) has been proposed to improve the performance of stress‐intolerant species (protégés) in extensive green roofs, the conditions under which facilitation could occur are not well understood. Therefore, a comprehensive framework is needed that integrates current knowledge on how the performance of protégé species is affected by nurse plants across stress conditions. We present a framework for green roof research that results in a linear model that integrates (i) modern trait–environment theory and (ii) facilitation ecology in a refined stress‐gradient hypothesis (SGH) originally developed following study of other stressful environments. The model makes testable predictions on how phenotypic traits mediate the performance response of protégé species to nurse plants along stress gradients in extensive green roofs. This is not only useful for the analysis of eco‐physiological performance measures directly linked with multifunctionality and ecosystem services, but also demographic or ‘vital’ rates that drive species persistence and plant community maintenance. We discuss a range of applications related to key agricultural and ecological questions arising from contemporary extensive green roof research, such as enhancing conditions for crop production, weed management, plant invasions and biodiversity conservation. We also provide guidelines for the generation of appropriate data and for fitting this model using readily available statistical procedures. Our framework will allow researchers to assess under which environmental conditions nurse–protégé interactions are feasible. We expect the findings from such research to help develop strategies and guidelines for managing environmental conditions that optimize protégé performances that ultimately affect the delivery of ecosystem services in constructed urban green spaces.

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So Many Variables: Joint Modeling in Community Ecology

Cited by 661 publications

References 70 publications

Incorporating Imperfect Detection into Joint Models of Communities: A response to Warton et al.

Incorporating Imperfect Detection into Joint Models of Communities: A response to Warton et al.

Habitat area and local habitat conditions outweigh fragmentation effects on insect communities in vineyards

Stress‐gradient framework for green roofs: Applications for urban agriculture and other ecosystem services

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