• Energy Research

AbstractKnowing the distribution of fish larvae can inform fisheries science and resource management in several ways, by: 1) providing information on spawning areas; 2) identifying key areas to manage and conserve; and 3) helping to understand how fish populations are affected by anthropogenic pressures, such as overfishing and climate change. With the expansion of industrial fishing activity after 1945, there was increased sampling of fish larvae to help better understand variation in fish stocks. However, large-scale larval records are rare and often unavailable. Here we digitize data from Nishikawa et al. (1985), which were collected from 1956–1981 and are near-global (50°N–50°S), seasonal distribution maps of fish larvae of 18 mainly commercial pelagic taxa of the families Scombridae, Xiphiidae, Istiophoridae, Scombrolabracidae, and Scomberesocidae. Data were collected from the Pacific, Atlantic, and Indian Oceans. We present four seasonal 1° × 1° resolution maps per taxa representing larval abundance per grid cell and highlight some of the main patterns. Data are made available as delimited text, raster, and vector files.

AbstractKnowing the distribution of fish larvae can inform fisheries science and resource management in several ways, by: 1) providing information on spawning areas; 2) identifying key areas to manage and conserve; and 3) helping to understand how fish populations are affected by anthropogenic pressures, such as overfishing and climate change. With the expansion of industrial fishing activity after 1945, there was increased sampling of fish larvae to help better understand variation in fish stocks. However, large-scale larval records are rare and often unavailable. Here we digitize data from Nishikawa et al. (1985), which were collected from 1956–1981 and are near-global (50°N–50°S), seasonal distribution maps of fish larvae of 18 mainly commercial pelagic taxa of the families Scombridae, Xiphiidae, Istiophoridae, Scombrolabracidae, and Scomberesocidae. Data were collected from the Pacific, Atlantic, and Indian Oceans. We present four seasonal 1° × 1° resolution maps per taxa representing larval abundance per grid cell and highlight some of the main patterns. Data are made available as delimited text, raster, and vector files.

AbstractClimate 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.

AbstractClimate 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.