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

Beissinger, Steven R.; Iknayan, Kelly J.; Guillera‐Arroita, Gurutzeta; Zipkin, Elise F.; Dorazio, Robert M.; Royle, J. Andrew; Kéry, Marc

doi:10.1016/j.tree.2016.07.009

Cited by 46 publications

(27 citation statements)

References 11 publications

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“…The first perspective relates to the need to explicitly account for the observation process when modelling the data, thus allowing one to separate the processes of interest from biases introduced by the observer (Guillera‐Arroita ; Beissinger et al . ; Warton et al . ).…”

Section: Future Directions and Challengesmentioning

confidence: 99%

How to make more out of community data? A conceptual framework and its implementation as models and software

et al. 2017

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Community ecology aims to understand what factors determine the assembly and dynamics of species assemblages at different spatiotemporal scales. To facilitate the integration between conceptual and statistical approaches in community ecology, we propose Hierarchical Modelling of Species Communities (HMSC) as a general, flexible framework for modern analysis of community data. While non-manipulative data allow for only correlative and not causal inference, this framework facilitates the formulation of data-driven hypotheses regarding the processes that structure communities. We model environmental filtering by variation and covariation in the responses of individual species to the characteristics of their environment, with potential contingencies on species traits and phylogenetic relationships. We capture biotic assembly rules by species-to-species association matrices, which may be estimated at multiple spatial or temporal scales. We operationalise the HMSC framework as a hierarchical Bayesian joint species distribution model, and implement it as R-and Matlab-packages which enable computationally efficient analyses of large data sets. Armed with this tool, community ecologists can make sense of many types of data, including spatially explicit data and time-series data. We illustrate the use of this framework through a series of diverse ecological examples.

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Section: Future Directions and Challengesmentioning

confidence: 99%

How to make more out of community data? A conceptual framework and its implementation as models and software

et al. 2017

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“…, Beissinger et al. , Kéry and Royle ). We use simulations to evaluate and compare the performance of our models under different sample sizes and illustrate their application with a large real‐world data set of 62 passerine bird species in Switzerland.…”

Section: Introductionmentioning

confidence: 98%

“…, Beissinger et al. , Kéry and Royle , chapter 11). Hence, it has been argued repeatedly that it would be desirable to incorporate this important feature of measurement error in real ecological data into such JSDMs as well (Beissinger et al.…”

Section: Introductionmentioning

confidence: 99%

Joint species distribution models with species correlations and imperfect detection

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Kéry

Hui

et al. 2019

Ecology

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Spatiotemporal patterns in biological communities are typically driven by environmental factors and species interactions. Spatial data from communities are naturally described by stacking models for all species in the community. Two important considerations in such multispecies or joint species distribution models (JSDMs) are measurement errors and correlations between species. Up to now, virtually all JSDMs have included either one or the other, but not both features simultaneously, even though both measurement errors and species correlations may be essential for achieving unbiased inferences about the distribution of communities and species co-occurrence patterns. We developed two presence-absence JSDMs for modeling pairwise species correlations while accommodating imperfect detection: one using a latent variable and the other using a multivariate probit approach. We conducted three simulation studies to assess the performance of our new models and to compare them to earlier latent variable JSDMs that did not consider imperfect detection. We illustrate our models with a large Atlas data set of 62 passerine bird species in Switzerland. Under a wide range of conditions, our new latent variable JSDM with imperfect detection and species correlations yielded estimates with little or no bias for occupancy, occupancy regression coefficients, and the species correlation matrix. In contrast, with the multivariate probit model we saw convergence issues with large data sets (many species and sites) resulting in very long run times and larger errors. A latent variable model that ignores imperfect detection produced correlation estimates that were consistently negatively biased, that is, underestimated. We found that the number of latent variables required to represent the species correlation matrix adequately may be much greater than previously suggested, namely around n/2, where n is community size. The analysis of the Swiss passerine data set exemplifies how not accounting for imperfect detection will lead to negative bias in occupancy estimates and to attenuation in the estimated covariate coefficients in a JSDM. Furthermore, spatial heterogeneity in detection may cause spurious patterns in the estimated species correlation matrix if not accounted for. Our new JSDMs represent an important extension of current approaches to community modeling to the common case where species presence-absence cannot be detected with certainty.

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“…However, imperfect detection is often an issue in survey data. Extending these models to account for imperfect detection is an important avenue for future JSDM research (Beissinger et al., ; Warton et al., ), as is the evaluation of impacts of imperfect detection in the estimation of residual co‐occurrence. Rota et al.…”

Section: Discussionmentioning

confidence: 99%

A comparison of joint species distribution models for presence–absence data

et al. 2018

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Joint species distribution models (JSDMs) account for biotic interactions and missing environmental predictors in correlative species distribution models. Several different JSDMs have been proposed in the literature, but the use of different or conflicting nomenclature and statistical notation potentially obscures similarities and differences among them. Furthermore, new JSDM implementations have been illustrated with different case studies, preventing direct comparisons of computational and statistical performance. We aim to resolve these outstanding issues by (a) highlighting similarities among seven presence–absence JSDMs using a clearly defined, singular notation; and (b) evaluating the computational and statistical performance of each JSDM using six datasets that vary widely in numbers of sites, species, and environmental covariates considered. Our singular notation shows that many of the JSDMs are very similar, and in turn parameter estimates of different JSDMs are moderate to strongly, positively correlated. In contrast, the different JSDMs clearly differ in computational efficiency and memory limitations. Our framework will allow ecologists to make educated decisions about the JSDM that best suits their objective, and enable wider uptake of JSDM methods among the ecological community.

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

Cited by 46 publications

References 11 publications

How to make more out of community data? A conceptual framework and its implementation as models and software

How to make more out of community data? A conceptual framework and its implementation as models and software

Joint species distribution models with species correlations and imperfect detection

A comparison of joint species distribution models for presence–absence data

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