Biotic interactions in species distribution modelling: 10 questions to guide interpretation and avoid false conclusions

Dormann, Carsten F.; Bobrowski, Maria; Dehling, D. Matthias; Harris, David J.; Härtig, Florian; Lischke, Heike; Moretti, Marco; Pagel, Jörn; Pinkert, Stefan; Schleuning, Matthias; Schmidt, Susanne I.; Sheppard, Christine S.; Steinbauer, Manuel J.; Zeuss, Dirk; Kraan, Casper

doi:10.1111/geb.12759

Cited by 240 publications

(229 citation statements)

References 130 publications

Supporting

Mentioning

202

Contrasting

Unclassified

Order By: Relevance

“…Studies using observational co‐occurrence or co‐abundance data have sometimes also been interpreted as species interactions, including in Harris () and Morueta‐Holme et al (). However, some care has to be taken with this interpretation (Dormann et al, ). Species which appear to interact may both be responding to unmeasured environmental variables.…”

Section: Discussionmentioning

confidence: 99%

Untangling direct species associations from indirect mediator species effects with graphical models

et al. 2019

View full text Add to dashboard Cite

Ecologists often investigate co‐occurrence patterns in multi‐species data in order to gain insight into the ecological causes of observed co‐occurrences. Apart from direct associations between the two species of interest, they may co‐occur because of indirect effects, where both species respond to another variable, whether environmental or biotic (e.g. a mediator species). A wide variety of methods are now available for modelling how environmental filtering drives species distributions. In contrast, methods for studying other causes of co‐occurence are much more limited. “Graphical” methods, which can be used to study how mediator species impact co‐occurrence patterns, have recently been proposed for use in ecology. However, available methods are limited to presence/absence data or methods assuming multivariate normality, which is problematic when analysing abundances. We propose Gaussian copula graphical models (GCGMs) for studying the effect of mediator species on co‐occurence patterns. GCGMs are a flexible type of graphical model which naturally accommodates all data types, for example binary (presence/absence), counts, as well as ordinal data and biomass, in a unified framework. Simulations demonstrate that GCGMs can be applied to a much broader range of data types than the methods currently used in ecology, and perform as well as or better than existing methods in many settings. We apply GCGMs to counts of hunting spiders, in order to visualise associations between species. We also analyse abundance data of New Zealand native forest cover (on an ordinal scale) to show how GCGMs can be used analyse large and complex datasets. In these data, we were able to reproduce known species relationships as well as generate new ecological hypotheses about species associations.

show abstract

Section: Discussionmentioning

confidence: 99%

Untangling direct species associations from indirect mediator species effects with graphical models

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Figure S1.12) as the effects of unknown, external explanatory variables and the type of biotic interactions (e.g. competition vs. predation) cannot be distinguished (Clark et al, ; Dormann et al, ). Although the joint model did not improve predictive performance in this particular application, joint models that do not show a significant deterioration in predictive performance can be used to test for prior hypotheses of interactions between specific species, and to facilitate discussions with biologists to stimulate additional model development (Dormann et al, ).…”

Section: Discussionmentioning

confidence: 99%

“…competition vs. predation) cannot be distinguished (Clark et al, ; Dormann et al, ). Although the joint model did not improve predictive performance in this particular application, joint models that do not show a significant deterioration in predictive performance can be used to test for prior hypotheses of interactions between specific species, and to facilitate discussions with biologists to stimulate additional model development (Dormann et al, ). An investigation of the ecological context in which the inclusion of residual dependencies among distributions of taxa can lead to improved predictive performance would be an interesting topic for future research.…”

Section: Discussionmentioning

confidence: 99%

“…Ovaskainen, Abrego, Halme, & Dunson, ). Studies applying jSDMs have emphasized that the residual correlations between taxa do not directly quantify biotic interactions, and should be interpreted with caution (Clark et al, ; Dormann et al, ). For example, a positive correlation between two species may be due to mutualism or competition (e.g.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

From individual to joint species distribution models: A comparison of model complexity and predictive performance

Caradima

Schuwirth

Reichert

2019

Journal of Biogeography

View full text Add to dashboard Cite

Aim Species distribution models (SDMs) are widely used to study geographic distributions of taxa in response to natural and anthropogenic environmental conditions. For a community, common approaches include fitting individual SDMs (iSDMs) to all taxa or directly modelling community properties such as richness. However, the parameters of iSDMs are difficult to identify for rare taxa, and community properties do not reveal taxon‐specific responses. Individual models can be combined into a hierarchical multispecies distribution model (mSDM) that constrains taxon‐specific parameters according to overarching community parameters, or a joint model (jSDM) in which interdependencies between taxa are jointly inferred. We compare how individual, hierarchical multispecies and joint SDMs differ in quality of fit, explanatory power and predictive performance, and analyse how these properties depend on the prevalence of taxa. Taxa Presence–absence observations of 245 benthic macroinvertebrate taxa identified at a mixed taxonomic resolution. Location Four hundred and ninety‐two sites in rivers throughout Switzerland. Methods Individual, hierarchical and joint hierarchical generalized linear models (GLM) were developed for all taxa. Parameters were estimated using maximum likelihood estimation or Bayesian inference with Hamiltonian Markov chain Monte Carlo simulations. Predictive performance was assessed with cross‐validation. In addition, the predicted family and species richness of the models was compared with a GLM for richness. Results Individual models show a slightly higher quality of fit largely due to overfitting for rare taxa. The mSDM achieves a similar quality of fit and explanatory power, mitigates overfitting for rare taxa and considerably improves predictive performance over the whole community. The joint models further improve the quality of fit, but decrease predictive performance and increase predictive uncertainty. Main conclusions We show that even a relatively simple mSDM combines many of the analytical capabilities of iSDMs and improves predictive performance. Increasingly complex mSDMs and jSDMs provide additional analytical possibilities, but depending on the data and research questions, different levels of complexity may be appropriate.

show abstract

“…These differences in growth rates were shown to be the result of interference competition. Understanding the magnitude of the interactions is thus crucial for accurate projection of future distributions of animals (Dormann et al, 2018), as well as the development of conservation programmes for threatened species (Preston, Rotenberry, Redak, & Allen, 2008). Temperature-dependent competition has been studied in other salmonids as well, although almost exclusively in laboratory studies, and the results are ambiguous.…”

Section: Discussionmentioning

confidence: 99%

Temperature‐dependent competition between juvenile salmonids in small streams

et al. 2019

View full text Add to dashboard Cite

Biotic interactions affect species distributions, and environmental factors that influence these interactions can play a key role when range shifts in response to environmental change are modelled. In a field experiment using enclosures, we studied the effects of the thermal habitat on intra‐ versus inter‐specific competition of juvenile Dolly Varden Salvelinus malma and white‐spotted charr Salvelinus leucomaenis, as measured by differences in specific growth rates during summer in allopatric and sympatric treatments. Previous laboratory experiments have shown mixed results regarding the importance of temperature‐dependent competitive abilities as a main driver for spatial segregation in stream fishes, and no study so far has confirmed its existence in natural streams. Under natural conditions in areas where the two species occur in sympatry, Dolly Varden dominate spring‐fed tributaries (cold, stable thermal regime), whereas both species often coexist in non‐spring‐fed tributaries (warm, unstable thermal regime). Enclosures (charr density = 6 per m2) were placed in non‐spring‐fed (10–14°C) and spring‐fed (7–8°C) tributaries. In enclosures placed in non‐spring‐fed tributaries, Dolly Varden grew 0.81% per day in allopatry and had negative growth (−0.33% per day) in sympatry, whereas growth rates were similar in allopatry and sympatry in spring‐fed tributaries (0.68 and 0.58% per day). White‐spotted charr grew better in sympatry than in allopatry in both thermal habitats. In non‐spring‐fed tributaries, they grew 0.17 and 0.79% per day and in spring‐fed tributaries 0.46 and 0.75% per day in allopatry and sympatry, respectively. The negative effect of inter‐specific competition from white‐spotted charr on Dolly Varden thus depended on the thermal habitat. However, there was no strong evidence of a temperature‐dependent effect of intra‐ and inter‐specific competition on white‐spotted charr growth. Multiple factors may shape species distribution patterns, and we show that temperature may mediate competitive outcomes and thus coexistence in stream fish. These effects of temperature will be important to incorporate into mechanistic and dynamic species distribution models.

show abstract

Biotic interactions in species distribution modelling: 10 questions to guide interpretation and avoid false conclusions

Cited by 240 publications

References 130 publications

Untangling direct species associations from indirect mediator species effects with graphical models

Untangling direct species associations from indirect mediator species effects with graphical models

From individual to joint species distribution models: A comparison of model complexity and predictive performance

Temperature‐dependent competition between juvenile salmonids in small streams

Contact Info

Product

Resources

About