Search for: All records

Habitat‐forming organisms provide three‐dimensional structure that supports abundant and diverse communities. Variation in the morphological traits of habitat formers will therefore likely influence how they facilitate associated communities, either via food and habitat provisioning, or by altering predator–prey interactions. These mechanisms, however, are typically studied in isolation, and thus, we know little of how they interact to affect associated communities. In response to this, we used naturally occurring morphological variability in the algaSargassum vestitumto create habitat units of distinct morphotypes to test whether variation in the morphological traits (frond size and thallus size) ofS. vestitumor the interaction between these traits affects their value as habitat for associated communities in the presence and absence of predation. We found morphological traits did not interact, instead having independent effects on epifauna that were negligible in the absence of predation. However, when predators were present, habitat units with large fronds were found to host significantly lower epifaunal abundances than other morphotypes, suggesting that large frond alga provided low‐value refuge from predators. The presence of predators also influenced the size structure of epifaunal communities from habitat units of differing frond size, suggesting that the refuge value ofS. vestitumwas also related to epifauna body size. This suggests that habitat formers may chiefly structure associated communities by mediating size‐selective predation, and not through habitat provisioning. Furthermore, these results also highlight that habitat traits cannot be considered in isolation, for their interaction with biotic processes can have significant implications for associated communities.

 

For each assessment cycle of the Intergovernmental Panel on Climate Change (IPCC), researchers in the life sciences are called upon to provide evidence to policymakers planning for a changing future. This research increasingly relies on highly technical and complex outputs from climate models. The strengths and weaknesses of these data may not be fully appreciated beyond the climate modelling community; therefore, uninformed use of raw or preprocessed climate data could lead to overconfident or spurious conclusions. We provide an accessible introduction to climate model outputs that is intended to empower the life science community to robustly address questions about human and natural systems in a changing world. 

Climate change is already having profound effects on biodiversity, but climate change adaptation has yet to be fully incorporated into area‐based management tools used to conserve biodiversity, such as protected areas. One main obstacle is the lack of consensus regarding how impacts of climate change can be included in spatial conservation plans. We propose a climate‐smart framework that prioritizes the protection of climate refugia—areas of low climate exposure and high biodiversity retention—using climate metrics. We explore four aspects of climate‐smart conservation planning: (1) climate model ensembles; (2) multiple emission scenarios; (3) climate metrics; and (4) approaches to identifying climate refugia. We illustrate this framework in the Western Pacific Ocean, but it is equally applicable to terrestrial systems. We found that all aspects of climate‐smart conservation planning considered affected the configuration of spatial plans. The choice of climate metrics and approaches to identifying refugia have large effects in the resulting climate‐smart spatial plans, whereas the choice of climate models and emission scenarios have smaller effects. As the configuration of spatial plans depended on climate metrics used, a spatial plan based on a single measure of climate change (e.g., warming) will not necessarily be robust against other measures of climate change (e.g., ocean acidification). We therefore recommend using climate metrics most relevant for the biodiversity and region considered based on a single or multiple climate drivers. To include the uncertainty associated with different climate futures, we recommend using multiple climate models (i.e., an ensemble) and emission scenarios. Finally, we show that the approaches we used to identify climate refugia feature trade‐offs between: (1) the degree to which they are climate‐smart, and (2) their efficiency in meeting conservation targets. Hence, the choice of approach will depend on the relative value that stakeholders place on climate adaptation. By using this framework, protected areas can be designed with improved longevity and thus safeguard biodiversity against current and future climate change. We hope that the proposed climate‐smart framework helps transition conservation planning toward climate‐smart approaches.