Some Noteworthy Issues in Joint Species Distribution Modeling for Plant Data

Gelfand, Alan E.

doi:10.51387/22-NEJSDS11

The New England Journal of Statistics in Data Science

Some Noteworthy Issues in Joint Species Distribution Modeling for Plant Data

Volume 1, Issue 1 (2023), pp. 102–109

Alan E. Gelfand

https://doi.org/10.51387/22-NEJSDS11

Pub. online: 19 October 2022 Type: Spatial And Environmental Statistics

Open Access

Accepted
21 July 2022

Published
19 October 2022

Abstract

Joint species distribution modeling is attracting increasing attention in the literature these days, recognizing the fact that single species modeling fails to take into account expected dependence/interaction between species. This short paper offers discussion that attempts to illuminate five noteworthy technical issues associated with such modeling in the context of plant data. In this setting, the joint species distribution work in the literature considers several types of species data collection. For convenience of discussion, we focus on joint modeling of presence/absence data. For such data, the primary modeling strategy has been through introduction of latent multivariate normal random variables.

These issues address the following: (i) how the observed presence/absence data is linked to the latent normal variables as well as the resulting implications with regard to modeling the data sites as independent or spatially dependent, (ii) the incompatibility of point referenced and areal referenced presence/absence data in spatial modeling of species distribution, (iii) the effect of modeling species independently/marginally rather than jointly within site, with regard to assessing species distribution, (iv) the interpretation of species dependence under the use of latent multivariate normal specification, and (v) the interpretation of clustering of species associated with specific joint species distribution modeling specifications.

It is hoped that, by attempting to clarify these issues, ecological modelers and quantitative ecologists will be able to better appreciate some subtleties that are implicit in this growing collection of modeling ideas. In this regard, this paper can serve as a useful companion piece to the recent survey/comparison article by [33] in Methods in Ecology and Evolution.

References

[1]

Aarts, G., Fieberg, J. and Matthiopoulos, J. (2012). Comparative interpretation of count, presence-absence and point methods for species distribution models. Methods in Ecology and Evolution 3, 177–187.

[2]

Banerjee, S., Carlin, B. P. and Gelfand, A. E. (2014). Hierarchical modeling and analysis for spatial data. 2nd edn. Chapman & Hall/CRC, Boca Raton, FL, USA. MR3362184

[3]

Barner, A. K., Coblentz, K. E., Hacker, S. D. and Menge, B. A. (2018). Fundamental contradictions among observational and experimental estimates of non-trophic species interactions. Ecology 99, 557–566.

[4]

Bhattacharya, A. and Dunson, D. B. (2011). Sparse Bayesian infinite factor models. Biometrika 291–306. https://doi.org/10.1093/biomet/asr013. MR2806429

[5]

Blanchet, F. G., Cazelles, K. and Gravel, D. (2020). Co-occurrence is not evidence of ecological interactions. Ecology Letters 23, 1050–1063.

[6]

Calabrese, J. M., Certain, G., Kraan, C. and Dormann, C. F. (2014). Stacking species distribution models and adjusting bias by linking them to macroecological models. Global Ecology and Biogeography 23(1) 99–112.

[7]

Chakraborty, A., Gelfand, A. E., Wilson, A. M., Latimer, A. M. and Silander, J. A. (2011). Point pattern modelling for degraded presence-only data over large regions. JRSS-C 60, 757–776. https://doi.org/10.1111/j.1467-9876.2011.00769.x. MR2844854

[8]

Chib, S. and Greenberg, E. (1998). Analysis of multivariate probit models. Biometrika 85(2) 347–361.

[9]

Clark, J. S., Nemergut, D., Seyednasrollah, B., Turner, P. J. and Zhang, S. (2017). Generalized joint attribute modeling for biodiversity analysis: Median-zero, multivariate, multifarious data. Ecological Monographs 87(1) 34–56.

[10]

De Oliveira, V. (2000). Bayesian prediction of clipped Gaussian random fields. CompStatDataAnal 34, 299–314.

[11]

Gelfand, A. E. and Shirota, S. (2019). Preferential sampling for presence/absence data and for fusion of presence/absence data with presence–only data. Ecological Monographs 89(3).

[12]

Gelfand, A. E. and Shirota, S. (2021). On the role of odds ratios in joint species distribution modeling. Environmental and Ecological Statistics (forthcoming).

[13]

Guisan, A. and Rahbek, C. (2011). SESAM–a new framework integrating macroecological and species distribution models for predicting spatio-temporal patterns of species assemblages. Journal of Biogeography 38(8) 1433–1444.

[14]

Hefley, T. (2020). Model selection for ecological community data using tree shrinkage priors. ArXiv preprint, 2005.14303.

[15]

Hooten, M. B., Johnson, D. S., McClintock, B. T. and Morales, J. M. (2017). Animal Movement: Statistical Models for Telemetry Data. CRC Press, Boca Raton. MR3889901

[16]

Johnson, D. S. and Sinclair, E. H. (2017). Modeling joint abundance of multiple species using Dirichlet process mixtures. Environmetrics 28, e2440. https://doi.org/10.1002/env.2440. MR3634110

[17]

Lawrence, E., Bingham, D., Liu, C. and Nair, V. N. (2008). Bayesian inference for multivariate ordinal data using parameter expansion. Technometrics 50(2) 182–191. https://doi.org/10.1198/004017008000000064. MR2439877

[18]

Liu, J. S. and Wu, Y. N. (1999). Parameter expansion for data augmentation. Journal of the American Statistical Association 94(448) 1264–1274. https://doi.org/10.2307/2669940. MR1731488

[19]

Ovaskainen, O., Roy, D. B., Fox, R. and Anderson, B. J. (2016). Uncovering hidden spatial structure in species communities with spatially explicit joint species distribution models. Methods in Ecology and Evolution 7(4) 428–436.

[20]

Ovaskainen, O., Tikhonov, G., Norberg, A., Guillaume Blanchet, F., Duan, L., Dunson, D., Roslin, T. and Abrego, N. (2017). How to make more out of community data? A conceptual framework and its implementation as models and software. Ecology Letters 20(5) 561–576.

[21]

Pichler, M., Boreux, V., Klein, A. M., Schleuning, M. and Hartig, F. (2019). Machine learning algorithms to infer trait-matching and predict species interactions in ecological networks. Methods in Ecology and Evolution 11, 281–293.

[22]

Pollock, L. J., Tingley, R., Morris, W. K., Golding, N., O’Hara, R. B., Parris, K. M., Vesk, P. A. and McCarthy, M. A. (2014). Understanding co-occurrence by modelling species simultaneously with a Joint Species Distribution Model (JSDM). Methods in Ecology and Evolution 5(5) 397–406.

[23]

Ren, Q. and Banerjee, S. (2013). Hierarchical factor models for large spatially misaligned data: a low-rank predictive process approach. Biometrics 69(1) 19–30. https://doi.org/10.1111/j.1541-0420.2012.01832.x. MR3058048

[24]

Schliep, E. M., Gelfand, A. E., Mitchell, R. M., Aiello-Lammens, M. E. and Silander Jr, J. A. (2018). Assessing the joint behaviour of species traits as filtered by environment. Methods in Ecology and Evolution 9(3) 716–727.

[25]

Sethuraman, J. (1994). A constructive definition of Dirichlet priors. Statistica sinica 639–650. MR1309433

[26]

Shirota, S., Gelfand, A. E. and Banerjee, S. (2018). Spatial Joint Species Distribution Modeling using Dirichlet Processes. Statistica Sinica. MR3932512

[27]

Taylor-Rodriguez, D., Kaufeld, K., Schliep, E. M., Clark, J. S. and Gelfand, A. E. (2017). Joint species distribution modeling: dimension reduction using Dirichlet processes. Bayesian Analysis 12(4) 939–967. https://doi.org/10.1214/16-BA1031. MR3724974

[28]

Tikhonov, G. (2018). Bayesian latent factor approaches for modeling ecological species communities. Technical Report, Ph.D. Thesis, Faculty of Biological and Environmental Sciences, University of Helsinki.

[29]

Thorson, J. T., Scheuerell, M. D., Shelton, A. O., See, K. E., Skaug, H. J. and Kristensen, K. (2015). Spatial factor analysis: a new tool for estimating joint species distributions and correlations in species range. Methods in Ecology and Evolution 6(6) 627–637.

[30]

Tobler, M. W., Kery, M., Hui, F. K. C., Guillera-Arroita, G., Knaus, P. and Sattler, T. (2019). Joint species distribution models with species correlations and imperfect detection. Ecology 100, e02754.

[31]

Wackernagel, H. (2003). Multivariate Geostatistics: An Introduction with Applications. 3rd edn. Springer-Verlag.

[32]

Warton, D. I. and Shepherd, L. C. (2010). Poisson point process models solve the pseudo-absence problem for presence-only data in ecology. AnnalsAppldStats 4, 1383–1402. https://doi.org/10.1214/10-AOAS331. MR2758333

[33]

Wilkinson, D. P., Golding, N., Guillera-Arroita, G., Tingley, R. and McCarthy, M. A. (2018). A comparison of joint species distribution models for presence–absence data. Methods in Ecology and Evolution.

[34]

Wilkinson, D. P., Golding, N., Guillera-Arroita, G., Tingley, R. and McCarthy, M. A. (2021). Defining and evaluating predictions of joint species distribution models. Methods in Ecology and Evolution 12, 394–404.

[35]

Zobel, M. (1997). The relative of species pools in determining plant species richness: an alternative explanation of species coexistence? Trends in Ecology & Evolution 12(7) 266–269.

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Keywords

Dirichlet process Gaussian process Latent factor analysis Latent variables Model-based clustering Odds ratios Spatial dependence Species richness

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