From species distributions to meta‐communities

Thuiller, Wilfried; Pollock, Laura J.; Guéguen, Maya; Münkemüller, Tamara

doi:10.1111/ele.12526

Cited by 99 publications

(117 citation statements)

References 42 publications

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“…This is in line with previous findings that co-occurrence patterns are highly scale-dependent (Araújo and Rozenfeld 2014) and also that the effects of local interspecific interactions vanish at coarser spatial scales (Thuiller et al 2015). We found that with increasingly coarser resolution, both the signals of positive and negative interactions became indiscernible by JSDMs, whereby the signal of negative interactions was more sensitive to scale.…”

Section: Discussionsupporting

confidence: 92%

Do joint species distribution models reliably detect interspecific interactions from co‐occurrence data in homogenous environments?

2018

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Whether species interactions influence species response to environment and species ranges has always been a central question in ecology. Joint species distribution models (JSDMs) simultaneously model the species-environment relationships of multiple species and the residual correlation between these species. These residual correlations are assumed to depict whether species co-occur less or more often than expected by the modelled species-environment relationships, which could ultimately be attributed to species interactions, or hidden environmental information. Here, we propose to specifically test the capacity of JSDMs to detect species interactions from co-occurrence data, at different scales of data aggregation. Using a recently published point-process model, we simulated equilibrium co-occurrence patterns of species pairs by varying the strength and type of interactions (e.g. competition, predator-prey, mutualism) as well as the prevalence of the interacting species in homogeneous environments (assuming the environment does not influence the species responses and co-occurrence). Then, we fitted JSDMs without environmental predictors, and compared the estimated residual correlations against the known interaction coefficients. JSDMs detected competition and mutualism well, but failed with predator-prey interactions. For the latter, JSDMs predicted both negative and positive residual correlations for these kinds of interactions, depending on the prevalence of the interacting species. Interestingly, the estimated residual correlation was strongly influenced by species' prevalence and can thus not be translated to interaction strength. At increasingly coarser data resolution, the signals of negative and positive interactions became indiscernible by JSDMs, butreassuringly -were rarely confounded. The underlying point-process model simulates the consequences rather than the mechanisms of interspecific interactions, and thus is better at corroborating rather than discrediting JSDMs. Nevertheless, our simple theoretical exercise pinpoints important limitations of JSDMs. In conclusion, we caution against interpreting residual correlations from JSDMs as interaction strength and against comparing these across different species and communities.

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Section: Discussionsupporting

confidence: 92%

Do joint species distribution models reliably detect interspecific interactions from co‐occurrence data in homogenous environments?

2018

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“…This was advantageous in providing a balance between improved model performance (coarse resolution) (Thuiller et al, 2015) and representation of Fig. This was advantageous in providing a balance between improved model performance (coarse resolution) (Thuiller et al, 2015) and representation of Fig.…”

Section: Methodsmentioning

confidence: 99%

“…Species distribution models (SDMs), on the other hand, fit environmental predictors to observed presence-absence or abundance, bypassing uncertainties in parameters (reviews presented in Elith & Leathwick, 2009;Merow et al, 2014;Ehrlen & Morris, 2015;Guillera-Arroita et al, 2015). Although the former is arguably more important for abundance than occurrence, and at smaller spatial scales (Boulangeat et al, 2012;Fordham et al, 2012;Araujo & Rozenfeld, 2014;Thuiller et al, 2015;Schweiger & Beierkuhnlein, 2016), migration is a first-order constraint on changing plant distributions at regional to continental scales. SDMs typically provide strong fits to observations and are relatively easy to implement.…”

Section: Introductionmentioning

confidence: 99%

Vulnerability of eastern US tree species to climate change

Rogers

Jantz

Goetz

2017

Global Change Biology

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Climate change is expected to alter the distribution of tree species because of critical environmental tolerances related to growth, mortality, reproduction, disturbances, and biotic interactions. How this is realized in 21st century remains uncertain, in large part due to limitations on plant migration and the impacts of landscape fragmentation. Understanding these changes is of particular concern for forest management, which requires information at an appropriately fine spatial resolution. Here we provide a framework and application for tree species vulnerability to climate change in the eastern United States that accounts for influential drivers of future distributions. We used species distribution models to project changes in habitat suitability at 800 m for 40 tree species that vary in physiology, range, and environmental niche. We then developed layers of adaptive capacity based on migration potential, forest fragmentation, and propagule pressure. These were combined into metrics of vulnerability, including an overall index and spatially explicit categories designed to inform management. Despite overall favorable changes in suitability, the majority of species and the landscape were considered vulnerable to climate change. Vulnerability was significantly exacerbated by projections of pests and pathogens for some species. Northern and high-elevation species tended to be the most vulnerable. There were, however, some notable areas of particular resilience, including most of West Virginia. Our approach combines some of the most important considerations for species vulnerability in a straightforward framework, and can be used as a tool for managers to prioritize species, areas, and actions.

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“…For instance, they commonly ignore biotic interactions that strongly affect species composition [but see Thuiller et al . ()]. Moreover, niche models assume that species track changing climatic conditions instantaneously, disregarding time‐lags and indirect effects of climate change such as habitat changes and disturbance‐driven perturbations (Elith & Leathwick ).…”

Section: Introductionmentioning

confidence: 99%

The impacts of climate change and disturbance on spatio‐temporal trajectories of biodiversity in a temperate forest landscape

Thom

Rammer

Dirnböck

et al. 2016

Journal of Applied Ecology

161

136

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Summary1. The ongoing changes to climate challenge the conservation of forest biodiversity. Yet, in thermally limited systems, such as temperate forests, not all species groups might be affected negatively. Furthermore, simultaneous changes in the disturbance regime have the potential to mitigate climate-related impacts on forest species. Here, we (i) investigated the potential long-term effect of climate change on biodiversity in a mountain forest landscape, (ii) assessed the effects of different disturbance frequencies, severities and sizes and (iii) identified biodiversity hotspots at the landscape scale to facilitate conservation management.2. We employed the model iLand to dynamically simulate the tree vegetation on 13 865 ha of the Kalkalpen National Park in Austria over 1000 years, and investigated 36 unique combinations of different disturbance and climate scenarios. We used simulated changes in tree cover and composition as well as projected temperature and precipitation to predict changes in the diversity of Araneae, Carabidae, ground vegetation, Hemiptera, Hymenoptera, Mollusca, saproxylic beetles, Symphyta and Syrphidae, using empirical response functions.3. Our findings revealed widely varying responses of biodiversity indicators to climate change. Five indicators showed overall negative effects, with Carabidae, saproxylic beetles and tree species diversity projected to decrease by more than 33%. Six indicators responded positively to climate change, with Hymenoptera, Mollusca and Syrphidae diversity projected to increase more than twofold.4. Disturbances were generally beneficial for the studied indicators of biodiversity. Our results indicated that increasing disturbance frequency and severity have a positive effect on biodiversity, while increasing disturbance size has a moderately negative effect. Spatial hotspots of biodiversity were currently found in low- to mid-elevation areas of the mountainous study landscape, but shifted to higher-elevation zones under changing climate conditions.5. Synthesis and applications. Our results highlight that intensifying disturbance regimes may alleviate some of the impacts of climate change on forest biodiversity. However, the projected shift in biodiversity hotspots is a challenge for static conservation areas. In this regard, overlapping hotspots under current and expected future conditions highlight priority areas for robust conservation management.

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From species distributions to meta‐communities

Cited by 99 publications

References 42 publications

Do joint species distribution models reliably detect interspecific interactions from co‐occurrence data in homogenous environments?

Do joint species distribution models reliably detect interspecific interactions from co‐occurrence data in homogenous environments?

Vulnerability of eastern US tree species to climate change

The impacts of climate change and disturbance on spatio‐temporal trajectories of biodiversity in a temperate forest landscape

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