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The role of modern climatic microrefugia is a neglected aspect in the study of biotic responses to anthropogenic climate change. Current projections of species redistribution at continental extent are based on climatic grids of coarse (≥ 1 km) resolutions that fail to capture spatiotemporal dynamics associated with climatic microrefugia. Here, we review recent methods to model the climatic component of potential microrefugia and highlight research gaps in accounting for the buffering capacity due to biophysical processes operating at very fine (< 1 m) resolutions (e.g. canopy cover) and the associated microclimatic stability over time (i.e. decoupling). To overcome this challenge, we propose a spatially hierarchical downscaling framework combining a free‐air temperature grid at 1 km resolution, a digital elevation model at 25 m resolution and small‐footprint light detection‐and‐ranging (LiDAR) data at 50 cm resolution with knowledge from the literature to mechanistically model sub‐canopy temperatures and account for microclimatic decoupling. We applied this framework on a virtual sub‐canopy species and simulated the impact of a warming scenario on its potential distribution. Modelling sub‐canopy temperatures at 50 cm resolution and accounting for microclimatic stability over time enlarges the range of temperature conditions towards the cold end of the gradient, mitigates regional temperature changes and decreases extirpation risks. Incorporating these spatiotemporal dynamics into species redistribution models, being correlative, mechanistic or hybrid, will increase the probability of local persistence, which has important consequences in the understanding of the capacity of species to adapt. We finally provide a synthesis on additional ways that the field could move towards effectively considering potential climatic microrefugia for species redistribution.

Aim Although species distribution models (SDMs) traditionally link species occurrences to free‐air temperature data at coarse spatio‐temporal resolution, the distribution of organisms might instead be driven by temperatures more proximal to their habitats. Several solutions are currently available, such as downscaled or interpolated coarse‐grained free‐air temperatures, satellite‐measured land surface temperatures (LST) or in‐situ‐measured soil temperatures. A comprehensive comparison of temperature data sources and their performance in SDMs is, however, currently lacking. Location Northern Scandinavia. Time period 1970–2017. Major taxa studied Higher plants. Methods We evaluated different sources of temperature data (WorldClim, CHELSA, MODIS, E‐OBS, topoclimate and soil temperature from miniature data loggers), differing in spatial resolution (from 1″ to 0.1°), measurement focus (free‐air, ground‐surface or soil temperature) and temporal extent (year‐long versus long‐term averages), and used them to fit SDMs for 50 plant species with different growth forms in a high‐latitudinal mountain region. Results Differences between these temperature data sources originating from measurement focus and temporal extent overshadow the effects of temporal climatic differences and spatio‐temporal resolution, with elevational lapse rates ranging from −0.6°C per 100 m for long‐term free‐air temperature data to −0.2°C per 100 m for in‐situ soil temperatures. Most importantly, we found that the performance of the temperature data in SDMs depended on the growth forms of species. The use of in‐situ soil temperatures improved the explanatory power of our SDMs (R2 on average +16%), especially for forbs and graminoids (R2 +24 and +21% on average, respectively) compared with the other data sources. Main conclusions We suggest that future studies using SDMs should use the temperature dataset that best reflects the ecology of the species, rather than automatically using coarse‐grained data from WorldClim or CHELSA.

This document is a U.S. government work and is not subject to copyright in the United States. IndiSeas ("Indicators for the Seas") is a collaborative international working group that was established in 2005 to evaluate the status of exploited marine ecosystems using a suite of indicators in a comparative framework. An initial shortlist of seven ecological indicators was selected to quantify the effects of fishing on the broader ecosystem using several criteria (i.e., ecological meaning, sensitivity to fishing, data availability, management objectives and public awareness). The suite comprised: (i) the inverse coefficient of variation of total biomass of surveyed species, (ii) mean fish length in the surveyed community, (iii) mean maximum life span of surveyed fish species, (iv) proportion of predatory fish in the surveyed community, (v) proportion of under and moderately exploited stocks, (vi) total biomass of surveyed species, and (vii) mean trophic level of the landed catch. In line with the Nagoya Strategic Plan of the Convention on Biological Diversity (2011-2020), we extended this suite to emphasize the broader biodiversity and conservation risks in exploited marine ecosystems. We selected a subset of indicators from a list of empirically based candidate biodiversity indicators initially established based on ecological significance to complement the original IndiSeas indicators. The additional selected indicators were: (viii) mean intrinsic vulnerability index of the fish landed catch, (ix) proportion of non-declining exploited species in the surveyed community, (x) catch-based marine trophic index, and (xi) mean trophic level of the surveyed community. Despite the lack of data in some ecosystems, we also selected (xii) mean trophic level of the modelled community, and (xiii) proportion of discards in the fishery as extra indicators. These additional indicators were examined, along with the initial set of IndiSeas ecological indicators, to evaluate whether adding new biodiversity indicators provided useful additional information to refine our understanding of the status evaluation of 29 exploited marine ecosystems. We used state and trend analyses, and we performed correlation, redundancy and multivariate tests. Existing developments in ecosystembased fisheries management have largely focused on exploited species. Our study, using mostly fisheries independent survey-based indicators, highlights that biodiversity and conservation-based indicators are complementary to ecological indicators of fishing pressure. Thus, they should be used to provide additional information to evaluate the overall impact of fishing on exploited marine ecosystems.

Aim: To propose a species distribution modelling framework and its companion "iSDM" R package for predicting the potential and realized distributions of invasive species within the invaded range.Location: Northern France. Methods:The non-equilibrium distribution of invasive species with the environment within the invaded range affects the environmental representativeness of species presenceabsence data collected from the field and introduces uncertainty in observed absences as these may either reflect unsuitable sites or be incidental. To address these issues, we here propose an environmental systematic sampling design to collect presence-absence data from the field and a probability index to sort and subsequently separate environmental absences (EAs: reflecting environmentally unsuitable sites) from dispersal-limited absences (DLAs: reflecting sites out of dispersal reach). We first conducted a comprehensive test based on a virtual species to evaluate the performance of our framework. Then, we applied it on different life stages of a non-native tree species (Prunus serotina Ehrh.) invasive in Europe. Results:Regarding the potential distribution, we found higher model performances for both the virtual species (true skill statistics (TSS) > 0.75) and P. serotina (TSS ≥ 0.68) after carefully selecting absences with a low probability to be DLAs compared with classical models that incorporate both EAs and DLAs (e.g. TSS = 0.11 for P. serotina with 80% of DLAs). On the contrary, both EAs and DLAs as well as dispersal-related covariates were needed to capture the realized distribution of both the virtual species and P. serotina. Main Conclusions:Our framework helps overcoming the conceptual and methodological limitations of the disequilibrium in species' distribution models inherent to invasive species and enables managers to robustly estimate both the realized and potential distributions of invasive species. Although more relevant for modelling the distribution of non-native species, this framework can also be applied to native species. K E Y W O R D Salien species, biological invasions, dispersal limitations, potential niche, realized niche, species distribution modelling, virtual species

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Aim In this paper, we applied the concept of 'hierarchical filters' in community ecology to model marine species distribution at nested spatial scales.Location Global, Mediterranean Sea and the Gulf of Gabes (Tunisia). MethodsWe combined the predictions of bioclimatic envelope models (BEMs) and habitat models to assess the current distribution of 20 exploited marine species in the Gulf of Gabes. BEMs were first built at a global extent to account for the full range of climatic conditions encountered by a given species. Habitat models were then built using fine-grained habitat variables at the scale of the Gulf of Gabes. We also used this hierarchical filtering approach to project the future distribution of these species under both climate change (the A2 scenario implemented with the Mediterranean climatic model NEMOMED8) and habitat loss (the loss of Posidonia oceanica meadows) scenarios. ResultsThe hierarchical filtering approach predicted current species geographical ranges to be on average 56% smaller than those predicted using the BEMs alone. This pattern was also observed under the climate change scenario. Combining the habitat loss and climate change scenarios indicated that the magnitude of range shifts due to climate change was larger than from the loss of P. oceanica meadows. Main conclusionsOur findings emphasize that BEMs may overestimate current and future ranges of marine species if species-habitat relationships are not also considered. A hierarchical filtering approach that accounts for fine-grained habitat variables limits the uncertainty associated with model-based recommendations, thus ensuring their outputs remain applicable within the context of marine resource management.