habitat suitability – The OceanCanada Partnership

Projecting global mariculture diversity under climate change

Posted on February 10, 2020April 15, 2020 by d.burnside

Previous studies have focused on changes in the geographical distribution of terrestrial biomes and species targeted by marine capture fisheries due to climate change impacts. Given mariculture’s substantial contribution to global seafood production and its growing significance in recent decades, it is essential to evaluate the effects of climate change on mariculture and their socio‐economic consequences. Here, we projected climate change impacts on the marine aquaculture diversity for 85 of the currently most commonly farmed fish and invertebrate species in the world’s coastal and/or open ocean areas. Results of ensemble projections from three Earth system models and three species distribution models show that climate change may lead to a substantial redistribution of mariculture species richness potential, with an average of 10%–40% decline in the number of species being potentially suitable to be farmed in tropical to subtropical regions. In contrast, mariculture species richness potential is projected to increase by about 40% at higher latitudes under the ‘no mitigation policy’ scenario (RCP 8.5) by the mid‐21st century. In Exclusive Economic Zones where mariculture is currently undertaken, we projected an average future decline of 1.3% and 5% in mariculture species richness potential under RCP 2.6 (‘strong mitigation’) and RCP 8.5 scenarios, respectively, by the 2050s relative to the 2000s. Our findings highlight the opportunities and challenges for climate adaptation in the mariculture sector through the redistribution of farmed species and expansion of mariculture locations. Our results can help inform adaptation planning and governance mechanisms to minimize local environmental impacts and potential conflicts with other marine and coastal sectors in the future.

Why less complexity produces better forecasts: an independent data evaluation of kelp habitat models

Posted on July 2, 2018July 18, 2019 by d.burnside

Understanding how species are distributed in the environment is increasingly important for natural resource management, particularly for keystone and habitat – forming species, and those of conservation concern. Habitat suitability models are fundamental to developing this understanding; however their use in management continues to be limited due to often‐vague model objectives and inadequate evaluation methods. Along the Northeast Pacific coast, canopy kelps (Macrocystis pyrifera and Nereocystis luetkeana) provide biogenic habitat and considerable primary production to nearshore ecosystems. We investigated the distribution of these species by examining a series of increasingly complex habitat suitability models ranging from process‐based models based on species’ ecology to complex generalised additive models applied to purpose‐collected survey data. Seeking empirical limits to model complexity, we explored the relationship between model complexity and forecast skill, measured using both cross‐validation and independent data evaluation. Our analysis confirmed the importance of predictors used in models of coastal kelp distributions developed elsewhere (i.e. depth, bottom type, bottom slope, and exposure); it also identified additional important factors including salinity, and potential interactions between exposure and salinity, and slope and tidal energy. Comparative results showed how cross‐validation can lead to over‐fitting, while independent data evaluation clearly identified the appropriate model complexity for generating habitat forecasts. Our results also illustrate that, depending on the evaluation data, predictions from simpler models can out‐perform those from more complex models. Collectively, the insights from evaluating multiple models with multiple data sets contribute to the holistic assessment of model forecast skill. The continued development of methods and metrics for evaluating model forecasts with independent data, and the explicit consideration of model objectives and assumptions, promise to increase the utility of model forecasts to decision makers.