Joint dynamic species distribution models: a tool for community ordination and spatio‐temporal monitoring

Abstract

Aim Spatial analysis of the distribution and density of species is of continuing interest within theoretical and applied ecology. Species distribution models (SDMs) are being increasingly used to analyse count, presence–absence and presence‐only data sets. There is a growing literature on dynamic SDMs (which incorporate temporal variation in species distribution), joint SDMs (which simultaneously analyse the correlated distribution of multiple species) and geostatistical models (which account for similarity between nearby sites caused by unobserved covariates). However, no previous study has combined all three attributes within a single framework. Innovation We develop spatial dynamic factor analysis for use as a ‘joint, dynamic SDM’ (JDSDM), which uses geostatistical methods to account for spatial similarity when estimating one or more ‘factors’. Each factor evolves over time following a density‐dependent (Gompertz) process, and the log‐density of each species is approximated as a linear combination of different factors. We demonstrate a JDSDM using two multispecies case studies (an annual survey of bottom‐associated species in the Bering Sea and a seasonal survey of butterfly density in the continental USA), and also provide our code publicly as an R package. Main conclusions Case study applications show that that JDSDMs can be used for species ordination, i.e. showing that dynamics for butterfly species within the same genus are significantly more correlated than for species from different genera. We also demonstrate how JDSDMs can rapidly identify dominant patterns in community dynamics, including the decline and recovery of several Bering Sea fishes since 2008, and the ‘flight curves’ typical of early or late‐emerging butterflies. We conclude by suggesting future research that could incorporate phylogenetic relatedness or functional similarity, and propose that our approach could be used to monitor community dynamics at large spatial and temporal scales.

Forecast skill for predicting distribution shifts: A retrospective experiment for marine fishes in the Eastern Bering Sea

“…This annual survey has used a fixed‐station design with over 370 samples per year and consistent gear over this period (Lauth & Conner, ), and I download data from the AFSC website (http://www.afsc.noaa.gov/RACE/groundfish/survey_data/data.htm) using R package FishData (https://github.com/James-Thorson/FishData). Although this case‐study involves a (nearly) identical sampling design across years, future research could apply the same method to data sets that have different sampling intensity or design among years (e.g., Thorson, Ianelli, et al., ).…”

Comparing the prediction of joint species distribution models with respect to characteristics of sampling data

“…However, handling abundance data is not an easy task for JSDMs, as abundance data are commonly sparse for the whole community with irregular distributions. In particular, spatial structure may be considered to narrow down the variation of site-specific LVs for improving predictability (Thorson et al 2016, Ovaskainen et al 2017b, Thorson and Barnett 2017. Boral and Gjam), the large proportion of zero observations may hinder model performances (Clark et al 2017).…”

Within‐lake habitat heterogeneity mediates community response to warming trends

“…(Thorson et al. ). Applied here with an environmental covariate, the SDFA model offers a new probabilistic and predictive approach to multivariate species abundance data that are hierarchical in space and time.…”