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Aim: Future climate change threatens marine forests across the world, potentially disrupting ecosystem function and services. Nonetheless, the direction and intensity of climate‐induced changes in kelp forest biodiversity remain unknown, precluding well‐informed conservation and management practices. Location: Global. Methods: We use machine‐learning models to forecast global changes in species richness and community composition of 105 kelp forest species under contrasting Shared Socioeconomic Pathway (SSP) scenarios of climate change (decade 2090–2100): one aligned with the Paris Agreement and another of substantially higher emissions. Results: A poleward and depth shift in species distributions is forecasted, translating into ~15% less area in the extent of the global biome, coupled with marked regional biodiversity changes. Community composition changes are mostly projected in the Arctic, the Northern Pacific and Atlantic, and Australasia, owing to poleward range expansions and wide low latitude losses. Main Conclusions: By surpassing the Paris Agreement expectations, species reshuffling may simplify and impair ecosystem services in numerous temperate regions of Australasia, Southern Africa, Southern America and the Northern Atlantic, and in the tropical Pacific, where complete species losses were projected without replacement. These estimates, flagging threatened regions and species, as well as refugial areas of population persistence, can now inform conservation, management and restoration practices considering future climate change. [ABSTRACT FROM AUTHOR]

Due to rising global surface temperatures, Arctic habitats are becoming thermally suitable for temperate species. Whether a temperate species can immigrate into an ice-free Arctic depends on its ability to tolerate extreme seasonal fluctuations in daylength. Thus, understanding adaptations to polar light conditions can improve the realism of models predicting poleward range expansions in response to climate change. Plant adaptations to polar light have rarely been studied and remain unknown in seagrasses. If these ecosystem engineers can migrate polewards, seagrasses will enrich biodiversity, and carbon capture potential in shallow coastal regions of the Arctic. Eelgrass (Zostera marina) is the most widely distributed seagrass in the northern hemisphere. As the only seagrass species growing as far north as 70°N, it is the most likely candidate to first immigrate into an ice-free Arctic. Here, we describe seasonal (and diurnal) changes in photosynthetic characteristics, and in genome-wide gene expression patterns under strong annual fluctuations of daylength. We compared PAM measurements and RNA-seq data between two populations at the longest and shortest day of the year: (1) a Mediterranean population exposed to moderate annual fluctuations of 10–14 h daylength and (2) an Arctic population exposed to high annual fluctuations of 0–24 h daylength. Most of the gene expression specificities of the Arctic population were found in functions of the organelles (chloroplast and mitochondrion). In winter, Arctic eelgrass conserves energy by repressing respiration and reducing photosynthetic energy fluxes. Although light-reactions, and genes involved in carbon capture and carbon storage were upregulated in summer, enzymes involved in CO2 fixation and chlorophyll-synthesis were upregulated in winter, suggesting that winter metabolism relies not only on stored energy resources but also on active use of dim light conditions. Eelgrass is unable to use excessive amounts of light during summer and demonstrates a significant reduction in photosynthetic performance under long daylengths, possibly to prevent photoinhibition constrains. Our study identified key mechanisms that allow eelgrass to survive under Arctic light conditions and paves the way for experimental research to predict whether and up to which latitude eelgrass can potentially migrate polewards in response to climate change. [ABSTRACT FROM AUTHOR]

To improve understanding and management of the consequences of current rapid environmental change, ecologists advocate using long-term monitoring data series to generate iterative near-term predictions of ecosystem responses. This approach allows scientific evidence to increase rapidly and management strategies to be tailored simultaneously. Iterative near-term forecasting may therefore be particularly useful for adaptive monitoring of ecosystems subjected to rapid climate change. Here, we show how to implement near-term forecasting in the case of a harvested population of rock ptarmigan in high-arctic Svalbard, a region subjected to the largest and most rapid climate change on Earth. We fitted state-space models to ptarmigan counts from point transect distance sampling during 2005-2019 and developed two types of predictions: (1) explanatory predictions to quantify the effect of potential drivers of ptarmigan population dynamics, and (2) anticipatory predictions to assess the ability of candidate models of increasing complexity to forecast next-year population density. Based on the explanatory predictions, we found that a recent increasing trend in the Svalbard rock ptarmigan population can be attributed to major changes in winter climate. Currently, a strong positive effect of increasing average winter temperature on ptarmigan population growth outweighs the negative impacts of other manifestations of climate change such as rain-on-snow events. Moreover, the ptarmigan population may compensate for current harvest levels. Based on the anticipatory predictions, the near-term forecasting ability of the models improved nonlinearly with the length of the time series, but yielded good forecasts even based on a short time series. The inclusion of ecological predictors improved forecasts of sharp changes in next-year population density, demonstrating the value of ecosystem-based monitoring. Overall, our study illustrates the power of integrating near-term forecasting in monitoring systems to aid understanding and management of wildlife populations exposed to rapid climate change. We provide recommendations for how to improve this approach., (© 2021 The Authors. Global Change Biology published by John Wiley & Sons Ltd.)