Integrating demography, dispersal and interspecific interactions into bird distribution models

Zurell, Damaris

doi:10.1111/jav.01225

Cited by 50 publications

(43 citation statements)

References 113 publications

(217 reference statements)

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“…can help deriving hypotheses about community assembly processes present in the system and could, thus, serve as screening tool for identifying important biotic interactions in local species pools (Ovaskainen et al, 2017;Zurell, 2017). These relative benefits of SDMs and JSDMs in community predictions and hypothesis testing should be further evaluated in the future.…”

Section: Environmental Correlation Residual Correlationmentioning

confidence: 99%

Testing species assemblage predictions from stacked and joint species distribution models

Zurell

Zimmermann

Groß

et al. 2019

Journal of Biogeography

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Aim Predicting the spatial distribution of species assemblages remains an important challenge in biogeography. Recently, it has been proposed to extend correlative species distribution models (SDMs) by taking into account (a) covariance between species occurrences in so‐called joint species distribution models (JSDMs) and (b) ecological assembly rules within the SESAM (spatially explicit species assemblage modelling) framework. Yet, little guidance exists on how these approaches could be combined. We, thus, aim to compare the accuracy of assemblage predictions derived from stacked and from joint SDMs. Location Switzerland. Taxon Birds, tree species. Methods Based on two monitoring schemes (national forest inventory and Swiss breeding bird atlas), we built SDMs and JSDMs for tree species (at 100 m resolution) and forest birds (at 1 km resolution). We tested accuracy of species assemblage and richness predictions on holdout data using different stacking procedures and ecological assembly rules. Results Despite minor differences, results were consistent between birds and tree species. Cross‐validated species‐level model performance was generally higher in SDMs than JSDMs. Differences in species richness and assemblage predictions were larger between stacking procedures and ecological assembly rules than between stacked SDMs and JSDMs. On average, predictions were slightly better for stacked SDMs compared to JSDMs, probabilistic stacks outperformed binary stacks, and ecological assembly rules yielded best predictions. Main conclusions When predicting the composition of species assemblages, the choice of stacking procedure and ecological assembly rule seems more decisive than differences in underlying model type (SDM vs. JSDM). JSDMs do not seem to improve community predictions compared to SDMs or improve predictions for rare species. Still, JSDMs may provide additional insights into community assembly and may help deriving hypotheses about prevailing biotic interactions in the system. We provide simple rules of thumb for choosing appropriate modelling pathways. Future studies should test these preliminary guidelines for other taxa and biogeographical realms as well as for other JSDM algorithms.

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Section: Environmental Correlation Residual Correlationmentioning

confidence: 99%

Testing species assemblage predictions from stacked and joint species distribution models

Zurell

Zimmermann

Groß

et al. 2019

Journal of Biogeography

Self Cite

102

115

View full text Add to dashboard Cite

show abstract

“…Many investigators are currently developing methods that incorporate more biological and statistical realism into the SDM process, including the integration of physiological and trait data (Pollock et al, 2018) and explicit models of bias (Robinson, Ruiz-Gutierrez, & Fink, 2018), dispersal (Zurell, 2017), plasticity (Bush et al, 2016) and evolutionary history (Smith, Godsoe, Rodriguez-Sanchez, Wang, & Warren, 2019). Many investigators are currently developing methods that incorporate more biological and statistical realism into the SDM process, including the integration of physiological and trait data (Pollock et al, 2018) and explicit models of bias (Robinson, Ruiz-Gutierrez, & Fink, 2018), dispersal (Zurell, 2017), plasticity (Bush et al, 2016) and evolutionary history (Smith, Godsoe, Rodriguez-Sanchez, Wang, & Warren, 2019).…”

Section: Concluding Remarks and Future Directionsmentioning

confidence: 99%

Evaluating presence‐only species distribution models with discrimination accuracy is uninformative for many applications

Warren

Matzke

Iglesias

2019

Journal of Biogeography

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Aim Species distribution models are used across evolution, ecology, conservation and epidemiology to make critical decisions and study biological phenomena, often in cases where experimental approaches are intractable. Choices regarding optimal models, methods and data are typically made based on discrimination accuracy: a model's ability to predict subsets of species occurrence data that were withheld during model construction. However, empirical applications of these models often involve making biological inferences based on continuous estimates of relative habitat suitability as a function of environmental predictor variables. We term the reliability of these biological inferences ‘functional accuracy.’ We explore the link between discrimination accuracy and functional accuracy. Methods Using a simulation approach we investigate whether models that make good predictions of species distributions correctly infer the underlying relationship between environmental predictors and the suitability of habitat. Results We demonstrate that discrimination accuracy is only informative when models are simple and similar in structure to the true niche, or when data partitioning is geographically structured. However, the utility of discrimination accuracy for selecting models with high functional accuracy was low in all cases. Main conclusions These results suggest that many empirical studies and decisions are based on criteria that are unrelated to models’ usefulness for their intended purpose. We argue that empirical modelling studies need to place significantly more emphasis on biological insight into the plausibility of models, and that the current approach of maximizing discrimination accuracy at the expense of other considerations is detrimental to both the empirical and methodological literature in this active field. Finally, we argue that future development of the field must include an increased emphasis on simulation; methodological studies based on ability to predict withheld occurrence data may be largely uninformative about best practices for applications where interpretation of models relies on estimating ecological processes, and will unduly penalize more biologically informative modelling approaches.

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“…Recent research has recommended forecasting distribution shifts using mechanistic models that represent the impact of environmental changes and biotic interactions on individual demographic rates (Mellin et al., ; Swab, Regan, Matthies, Becker, & Bruun, ; Trainor, Schmitz, Ivan, & Shenk, ; Zurell, ). However, many taxa (e.g., marine fishes, insects and fungi communities, among others) often do not have sufficient individual‐specific demographic information to parameterize stage‐based models of environmental and biological mechanisms (e.g., examples in Parmesan, ).…”

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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

Thorson

2018

Fish and Fisheries

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Forecasting distribution shifts under novel environmental conditions is a major task for ecologists and conservationists. Researchers forecast distribution shifts using several tools including: predicting from an empirical relationship between a summary of distribution (population centroid) and annual time series (“annual regression,” AR); or fitting a habitat‐envelope model to historical distribution and forecasting given predictions of future environmental conditions (“habitat envelope,” HE). However, surprisingly little research has estimated forecast skill by fitting to historical data, forecasting distribution shifts and comparing forecasts with subsequent observations of distribution shifts. I demonstrate the important role of retrospective skill testing by forecasting poleward movement over 1‐, 2‐ or 3‐year periods for 20 fish and crab species in the Eastern Bering Sea and comparing forecasts with observed shifts. I specifically introduce an alternative vector‐autoregressive spatio‐temporal (VAST) forecasting model, which can include species temperature responses, and compare skill for AR, HE and VAST forecasts. Results show that the HE forecast has 30%–43% greater variance than predicting that future distribution is identical to the estimated distribution in the final year (a “persistence” forecast). Meanwhile, the AR explains 2%–6% and VAST explains 8%–25% of variance in poleward movement, and both have better performance than a persistence forecast. HE and AR both generate forecast intervals that are too narrow, while VAST models with or without temperature have appropriate width for forecast intervals. Retrospective skill testing for more regions and taxa should be used as a test bed to guide future improvements in methods for forecasting distribution shifts.

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Integrating demography, dispersal and interspecific interactions into bird distribution models

Cited by 50 publications

References 113 publications

Testing species assemblage predictions from stacked and joint species distribution models

Testing species assemblage predictions from stacked and joint species distribution models

Evaluating presence‐only species distribution models with discrimination accuracy is uninformative for many applications

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

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