Solutions for ecosystem‐level protection of ocean systems under climate change

Abstract

The Paris Conference of Parties (COP21) agreement renewed momentum for action against climate change, creating the space for solutions for conservation of the ocean addressing two of its largest threats: climate change and ocean acidification (CCOA). Recent arguments that ocean policies disregard a mature conservation research field and that protected areas cannot address climate change may be oversimplistic at this time when dynamic solutions for the management of changing oceans are needed. We propose a novel approach, based on spatial meta-analysis of climate impact models, to improve the positioning of marine protected areas to limit CCOA impacts. We do this by estimating the vulnerability of ocean ecosystems to CCOA in a spatially explicit manner and then co-mapping human activities such as the placement of renewable energy developments and the distribution of marine protected areas. We test this approach in the NE Atlantic considering also how CCOA impacts the base of the food web which supports protected species, an aspect often neglected in conservation studies. We found that, in this case, current regional conservation plans protect areas with low ecosystem-level vulnerability to CCOA, but disregard how species may redistribute to new, suitable and productive habitats. Under current plans, these areas remain open to commercial extraction and other uses. Here, and worldwide, ocean conservation strategies under CCOA must recognize the longterm importance of these habitat refuges, and studies such as this one are needed to identify them. Protecting these areas creates adaptive, climate-ready and ecosystem-level policy options for conservation, suitable for changing oceans.

Does sex really matter? Explaining intraspecies variation in ocean acidification responses

“…However, the fact that any differences have been demonstrated supports the need for further investigation of this issue. If sex-based differences do exist for economically important species, as seems likely, then capturing this variance is crucial for accurately forecasting the future societal and economic repercussions of OA for dependent sectors, such as coastal management, conservation, fisheries and aquaculture [2]. Unfortunately, the lack of a sufficiently wide evidence base for sex-specific responses currently limits this ambition.…”

Responses of summer phytoplankton biomass to changes in top-down forcing: Insights from comparative modelling

“…by discussing their outcome as a possible, rather than a predicted, reaction of the ecosystem. In the present state of model development, an ensemble of model simulations seems as a reasonable approach to reduce inherent uncertainty in model estimates and provide weight of evidence (Lenhart et al 2010, Meier et al 2012b, Queiros et al 2016, Yun et al 2017. The behavior of the relatively simple ecosystem models used here, should be compared to potentially more complex behavior and trophic responses of trait-based models, which can include many more groups (or a blending across groups in terms of size-spectrum models) to better understand how changes in topdown forcing may cascade through the plankton community.…”

A Review of the Tools Used for Marine Monitoring in the UK: Combining Historic and Contemporary Methods with Modeling and Socioeconomics to Fulfill Legislative Needs and Scientific Ambitions

“…There are some significant challenges surrounding the uptake and use of complex models by decision makers (Hyder et al, 2015a;Lynam et al, 2016) relating to understanding of models in the following ways; production of functional outputs, quantifying uncertainty, and availability of quality standards. The availability of products and decision making timescales are often at odds with model development (Hyder et al, 2015a;Queirós et al, 2016). Communicating the outcomes and limitations of complex models to stakeholders is one of the main challenges when it comes to uptake and should be dealt with as part of the model building process (Cartwright et al, 2016).…”