Improving species distribution models using biotic interactions: a case study of parasites, pollinators and plants

Giannini, Tereza Cristina; Chapman, Daniel S.; Saraiva, Antônio Mauro; Alves-dos-Santos, Isabel; Biesmeijer, Jacobus C.

doi:10.1111/j.1600-0587.2012.07191.x

Cited by 148 publications

(148 citation statements)

References 63 publications

(82 reference statements)

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“…Host information restricts our model to high suitability predicted in southern California, Hawaii, and several islands in the Caribbean. Contrary to previous work suggesting that climate and host information provides the best model fit for modeling parasite distribution (Giannini et al 2013), our work suggests that for an obligate generalist brood-parasitic bird, host information alone provides the best fit.…”

Section: Discussioncontrasting

confidence: 51%

“…In these prior studies, the latter approach (using only known pointoccurrence records) consistently produced a better-performing model for the focal species (Giannini et al 2013). For the whydah, its co-introduced hosts may be far from distributional equilibrium (Elith et al 2010).…”

Section: Attribute Variablesmentioning

confidence: 98%

“…is necessary for defining suitable habitat for the focal species (Hof et al 2012, Giannini et al 2013. In these prior studies, the latter approach (using only known pointoccurrence records) consistently produced a better-performing model for the focal species (Giannini et al 2013).…”

Section: Attribute Variablesmentioning

confidence: 99%

“…These often involve consensus among several models (Giannini et al 2013). If whydahs, as host generalists, can ultimately exploit a greater range of hosts than they currently do, a model based solely on knownhost presence will underestimate their potential distribution and, thus, invasion risk.…”

Section: Model Set and Selectionmentioning

confidence: 99%

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Fifty-seventh Supplement to the American Ornithologists' UnionCheck-list of North American Birds

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Section: Discussioncontrasting

confidence: 51%

Section: Attribute Variablesmentioning

confidence: 98%

Section: Attribute Variablesmentioning

confidence: 99%

Section: Model Set and Selectionmentioning

confidence: 99%

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Fifty-seventh Supplement to the American Ornithologists' UnionCheck-list of North American Birds

Chesser

Burns

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et al. 2016

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“…Shevtsova et al 1995;Shevtsova et al 1997;Liancourt et al 2005;Aerts 2010; Bråthen and Lortie in press), and modelling studies (e.g. Araújo and Luoto 2007;Meier et al 2010;Giannini et al 2013;González-Salazar et al 2013;le Roux et al 2014;Mod et al in press).…”

Section: Introductionmentioning

confidence: 99%

Contrasting effects of biotic interactions on richness and distribution of vascular plants, bryophytes and lichens in an arctic–alpine landscape

et al. 2015

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Biotic interactions may strongly affect the distribution of individual species and the resulting patterns of species richness. However, the impacts can vary depending on the species or taxa examined, suggesting that the influences of interactions on species distributions and diversity are not always straightforward and can be taxon-contingent. The aim of this study was therefore to examine how the importance of biotic interactions varies within a community. We incorporated three biotic predictors (cover of the dominant vascular species) into two correlative species richness modelling frameworks to predict spatial variation in the number of vascular plants, bryophytes and lichens in arctic-alpine Fennoscandia, in N Europe. In addition, predictions based on single-species distribution models were used to determine the nature of the impact (negative vs. positive outcome) of the three dominant species on individual vascular plant, bryophyte and lichen species. Our results suggest that biotic variables can be as important as abiotic variables, but their relative contributions in explaining the richness of sub-dominant species varies among dominant species, species group and the modelling framework implemented. Similarly, the impacts of biotic interactions on individual species varied among the three species groups and dominant species, with the observed patterns partly reflecting species' biogeographic range. Our study provides additional support for the importance of biotic interactions in modifying arctic-alpine biodiversity patterns, and highlights that the impacts of interactions are not constant across taxa or biotic drivers. The influence of biotic interactions, including the taxon-contingency and range-based impacts, should therefore be accounted for when developing biodiversity forecasts.

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Novel model coupling approach for resilience analysis of coastal plant communities

Schibalski

Körner

Maier

et al. 2018

Ecological Applications

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Resilience is a major research focus covering a wide range of topics from biodiversity conservation to ecosystem (service) management. Model simulations can assess the resilience of, for example, plant species, measured as the return time to conditions prior to a disturbance. This requires process-based models (PBM) that implement relevant processes such as regeneration and reproduction and thus successfully reproduce transient dynamics after disturbances. Such models are often complex and thus limited to either short-term or small-scale applications, whereas many research questions require species predictions across larger spatial and temporal scales. We suggest a framework to couple a PBM and a statistical species distribution model (SDM), which transfers the results of a resilience analysis by the PBM to SDM predictions. The resulting hybrid model combines the advantages of both approaches: the convenient applicability of SDMs and the relevant process detail of PBMs in abrupt environmental change situations. First, we simulate dynamic responses of species communities to a disturbance event with a PBM. We aggregate the response behavior in two resilience metrics: return time and amplitude of the response peak. These metrics are then used to complement long-term SDM projections with dynamic short-term responses to disturbance. To illustrate our framework, we investigate the effect of abrupt short-term groundwater level and salinity changes on coastal vegetation at the German Baltic Sea. We found two example species to be largely resilient, and, consequently, modifications of SDM predictions consisted mostly of smoothing out peaks in the occurrence probability that were not confirmed by the PBM. Discrepancies between SDM- and PBM-predicted species responses were caused by community dynamics simulated in the PBM and absent from the SDM. Although demonstrated with boosted regression trees (SDM) and an existing individual-based model, IBC-grass (PBM), our flexible framework can easily be applied to other PBM and SDM types, as well as other definitions of short-term disturbances or long-term trends of environmental change. Thus, our framework allows accounting for biological feedbacks in the response to short- and long-term environmental changes as a major advancement in predictive vegetation modeling.

show abstract

Improving species distribution models using biotic interactions: a case study of parasites, pollinators and plants

Cited by 148 publications

References 63 publications

Fifty-seventh Supplement to the American Ornithologists' UnionCheck-list of North American Birds

Fifty-seventh Supplement to the American Ornithologists' UnionCheck-list of North American Birds

Contrasting effects of biotic interactions on richness and distribution of vascular plants, bryophytes and lichens in an arctic–alpine landscape

Novel model coupling approach for resilience analysis of coastal plant communities

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