A climatic basis for microrefugia: the influence of terrain on climate

There is compelling evidence from glacial and interglacial periods of the Quaternary of the utilization of microrefugia. Microrefugia are sites that support locally favorable climates amidst unfavorable regional climates, which allow populations of species to persist outside of their main distributions. Knowledge of the location of microrefugia has important implications for climate change research as it will influence our understanding of the spatial distribution of species through time, their patterns of genetic diversity, and potential dispersal rates in response to climate shifts. Indeed, the implications of microrefugia are profound and yet we know surprisingly little about their climatic basis; what climatic processes can support their subsistence, where they may occur, their climatic traits, and the relevance of these locations for climate change research. Here I examine the climatic basis for microrefugia and assert that the interaction between regional advective influences and local terrain influences will define the distribution and nature of microrefugia. I review the climatic processes that can support their subsistence and from this climatic basis: (1) infer traits of the spatial distribution of microrefugia and how this may change through time; (2) review assertions about their landscape position and what it can tell us about regional climates; and (3) demonstrate an approach to forecasting where microrefugia may occur in the future. This synthesis highlights the importance of landscape physiography in shaping the adaptive response of biota to climate change.     

A novel approach to quantify and locate potential microrefugia using topoclimate,climate stability,and isolation from the matrix

Ecologists are increasingly recognizing the conservation significance of microrefugia, but it is inherently difficult to locate these small patches with unusual climates, and hence they are also referred to as cryptic refugia. Here we introduce a new methodology to quantify and locate potential microrefugia using fine‐scale topoclimatic grids that capture extreme conditions, stable climates, and distinct differences from the surrounding matrix. We collected hourly temperature data from 150 sites in a large (200 km by 300 km) and diverse region of New South Wales, Australia, for a total of 671 days over 2 years. Sites spanned a range of habitats including coastal dune shrublands, eucalypt forests, exposed woodland ridges, sheltered rainforest gullies, upland swamps, and lowland pastures. Climate grids were interpolated using a regional regression approach based on elevation, distance to coast, canopy cover, latitude, cold‐air drainage, and topographical exposure to winds and radiation. We identified extreme temperatures on two separate climatic gradients: the 5th percentile of minimum temperatures and the 95th percentile of maximum temperatures. For each gradient, climatic stability was assessed on three different time scales (intra‐seasonal, intra‐annual and inter‐annual). Differences from the matrix were assessed using a moving window with a 5 km radius. We averaged the Z‐scores for these extreme, stable and isolated climates to identify potential locations of microrefugia. We found that our method successfully predicted the location of communities that were considered to occupy refugia, such as rainforests that have progressively contracted in distribution over the last 2.5 million years, and alpine grasslands that have contracted over the last 15 thousand years. However, the method was inherently sensitive to the gradient selected and other aspects of the modelling process. These uncertainties could be dealt with in a conservation planning context by repeating the methodology with various parameterizations and identifying areas that were consistently identified as microrefugia.     

Climate change at the landscape scale: predicting fine-grained spatial heterogeneity in warming and potential refugia for vegetation

Current predictions of how species will respond to climate change are based on coarse‐grained climate surfaces or idealized scenarios of uniform warming. These predictions may erroneously estimate the risk of extinction because they neglect to consider spatially heterogenous warming at the landscape scale or identify refugia where species can persist despite unfavourable regional climate. To address this issue, we investigated the heterogeneity in warming that has occurred in a 10 km × 10 km area from 1972 to 2007. We developed estimates by combining long‐term daily observations from a limited number of weather stations with a more spatially comprehensive dataset (40 sites) obtained during 2005–2006. We found that the spatial distribution of warming was greater inland, at lower elevations, away from streams, and at sites exposed to the northwest (NW). These differences corresponded with changes in weather patterns, such as an increasing frequency of hot, dry NW winds. As plant species were biased in the topographic and geographic locations they occupied, these differences meant that some species experienced more warming than others, and are at greater risk from climate change. This species bias could not be detected at coarser scales. The uneven seasonal nature of warming (e.g. more warming in winter, minimums increased more than maximums) means that climate change predictions will vary according to which predictors are selected in species distribution models. Models based on a limited set of predictors will produce erroneous predictions when the correct limiting factor is not selected, and this is difficult to avoid when temperature predictors are correlated because they are produced using elevation‐sensitive interpolations. The results reinforce the importance of downscaling coarse‐grained (∼50 km) temperature surfaces, and suggest that the accuracy of this process could be improved by considering regional weather patterns (wind speed, direction, humidity) and topographic exposure to key wind directions.     

Going with the flow? Threatened species management and legislation in the face of climate change

Summary   The potential impacts of climate change on threatened species, populations and communities are considered. It is suggested that minor changes to legislation will be required to address the consequences of movement of threatened species but that threatened species legislation will remain relevant as an important tool for prioritizing conservation actions. The importance of taking proactive steps now to permit future movement of species and communities across fragmented landscapes is emphasized.     

Topographic and vegetation drivers of thermal heterogeneity along the boreal–grassland transition zone in western Canada: Implications for climate change refugia

Climate change refugia are areas that are relatively buffered from contemporary climate change and may be important safe havens for wildlife and plants under anthropogenic climate change. Topographic variation is an important driver of thermal heterogeneity, but it is limited in relatively flat landscapes, such as the boreal plain and prairie regions of western Canada. Topographic variation within this region is mostly restricted to river valleys and hill systems, and their effects on local climates are not well documented. We sought to quantify thermal heterogeneity as a function of topography and vegetation cover within major valleys and hill systems across the boreal–grassland transition zone.Using iButton data loggers, we monitored local temperature at four hills and 12 river valley systems that comprised a wide range of habitats and ecosystems in Alberta, Canada (N = 240), between 2014 and 2020. We then modeled monthly temperature by season as a function of topography and different vegetation cover types using general linear mixed effect models.Summer maximum temperatures (T _max) varied nearly 6°C across the elevation gradient sampled. Local summer mean (T _mean) and maximum (T _max) temperatures on steep, north‐facing slopes (i.e., low levels of potential solar radiation) were up to 0.70°C and 2.90°C cooler than highly exposed areas, respectively. T _max in incised valleys was between 0.26 and 0.28°C cooler than other landforms, whereas areas with greater terrain roughness experienced maximum temperatures that were up to 1.62°C cooler. We also found that forest cover buffered temperatures locally, with coniferous and mixedwood forests decreasing summer T _mean from 0.23 to 0.72°C and increasing winter T _min by up to 2°C, relative to non‐forested areas.Spatial predictions of temperatures from iButton data loggers were similar to a gridded climate product (ClimateNA), but the difference between them increased with potential solar radiation, vegetation cover, and terrain roughness.Species that can track their climate niche may be able to compensate for regional climate warming through local migrations to cooler microsites. Topographic and vegetation characteristics that are related to cooler local climates should be considered in the evaluation of future climate change impacts and to identify potential refugia from climate change.     

Choice of predictor variables as a source of uncertainty in continental‐scale species distribution modelling under climate change

Aim Species distribution modelling is commonly used to guide future conservation policies in the light of potential climate change. However, arbitrary decisions during the model‐building process can affect predictions and contribute to uncertainty about where suitable climate space will exist. For many species, the key climatic factors limiting distributions are unknown. This paper assesses the uncertainty generated by using different climate predictor variable sets for modelling the impacts of climate change. Location Europe, 10° W to 50° E and 30° N to 60° N. Methods Using 1453 presence pixels at 30 arcsec resolution for the great bustard (Otis tarda), predictions of future distribution were made based on two emissions scenarios, three general climate models and 26 sets of predictor variables. Twenty‐six current models were created, and 156 for both 2050 and 2080. Map comparison techniques were used to compare predictions in terms of the quantity and the location of presences (map comparison kappa, MCK) and using a range change index (RCI). Generalized linear models (GLMs) were used to partition explained deviance in MCK and RCI among sources of uncertainty. Results The 26 different variable sets achieved high values of AUC (area under the receiver operating characteristic curve) and yet introduced substantial variation into maps of current distribution. Differences between maps were even greater when distributions were projected into the future. Some 64–78% of the variation between future maps was attributable to choice of predictor variable set alone. Choice of general climate model and emissions scenario contributed a maximum of 15% variation and their order of importance differed for MCK and RCI. Main conclusions Generalized variable sets produce an unmanageable level of uncertainty in species distribution models which cannot be ignored. The use of sound ecological theory and statistical methods to check predictor variables can reduce this uncertainty, but our knowledge of species may be too limited to make more than arbitrary choices. When all sources of modelling uncertainty are considered together, it is doubtful whether ensemble methods offer an adequate solution. Future studies should explicitly acknowledge uncertainty due to arbitrary choices in the model‐building process and develop ways to convey the results to decision‐makers.     

Improving species distribution models for climate change studies: variable selection and scale

Statistical species distribution models (SDMs) are widely used to predict the potential changes in species distributions under climate change scenarios. We suggest that we need to revisit the conceptual framework and ecological assumptions on which the relationship between species distributions and environment is based. We present a simple conceptual framework to examine the selection of environmental predictors and data resolution scales. These vary widely in recent papers, with light inconsistently included in the models. Focusing on light as a necessary component of plant SDMs, we briefly review its dependence on aspect and slope and existing knowledge of its influence on plant distribution. Differences in light regimes between north‐ and south‐facing aspects in temperate latitudes can produce differences in temperature equivalent to moves 200 km polewards. Local topography may create refugia that are not recognized in many climate change SDMs using coarse‐scale data. We argue that current assumptions about the selection of predictors and data resolution need further testing. Application of these ideas can clarify many issues of scale, extent and choice of predictors, and potentially improve the use of SDMs for climate change modelling of biodiversity.     

Multi‐scale responses to warming in an experimental insect metacommunity

In metacommunities, diversity is the product of species interactions at the local scale and dispersal between habitat patches at the regional scale. Although warming can alter both species interactions and dispersal, the combined effects of warming on these two processes remains uncertain. To determine the independent and interactive effects of warming‐induced changes to local species interactions and dispersal, we constructed experimental metacommunities consisting of enclosed milkweed patches seeded with five herbivorous milkweed specialist insect species. We treated metacommunities with two levels of warming (unwarmed and warmed) and three levels of connectivity (isolated, low connectivity, high connectivity). Based on metabolic theory, we predicted that if plant resources were limited, warming would accelerate resource drawdown, causing local insect declines and increasing both insect dispersal and the importance of connectivity to neighboring patches for insect persistence. Conversely, given abundant resources, warming could have positive local effects on insects, and the risk of traversing a corridor to reach a neighboring patch could outweigh the benefits of additional resources. We found support for the latter scenario. Neither resource drawdown nor the weak insect‐insect associations in our system were affected by warming, and most insect species did better locally in warmed conditions and had dispersal responses that were unchanged or indirectly affected by warming. Dispersal across the matrix posed a species‐specific risk that led to declines in two species in connected metacommunities. Combined, this scaled up to cause an interactive effect of warming and connectivity on diversity, with unwarmed metacommunities with low connectivity incurring the most rapid declines in diversity. Overall, this study demonstrates the importance of integrating the complex outcomes of species interactions and spatial structure in understanding community response to climate change.     

Are large‐scale distributional shifts of the blue‐winged macaw (Primolius maracana) related to climate change?

Aim To evaluate whether observed geographical shifts in the distribution of the blue‐winged macaw (Primolius maracana) are related to ongoing processes of global climate change. This species is vulnerable to extinction and has shown striking range retractions in recent decades, withdrawing broadly from southern portions of its historical distribution. Its range reduction has generally been attributed to the effects of habitat loss; however, as this species has also disappeared from large forested areas, consideration of other factors that may act in concert is merited. Location Historical distribution of the blue‐winged macaw in Brazil, eastern Paraguay and northern Argentina. Methods We used a correlative approach to test a hypothesis of causation of observed shifts by reduction of habitable areas mediated by climate change. We developed models of the ecological niche requirements of the blue‐winged macaw, based on point‐occurrence data and climate scenarios for pre‐1950 and post‐1950 periods, and tested model predictivity for anticipating geographical distributions within time periods. Then we projected each model to the other time period and compared distributions predicted under both climate scenarios to assess shifts of habitable areas across decades and to evaluate an explanation for observed range retractions. Results Differences between predicted distributions of the blue‐winged macaw over the twentieth century were, in general, minor and no change in suitability of landscapes was predicted across large areas of the species’ original range in different time periods. No tendency towards range retraction in the south was predicted, rather conditions in the southern part of the species’ range tended to show improvement for the species. Main conclusions Our test permitted elimination of climate change as a likely explanation for the observed shifts in the distribution of the blue‐winged macaw, and points rather to other causal explanations (e.g. changing regional land use, emerging diseases).     

Large‐scale distribution of tuna species in a warming ocean

Tuna are globally distributed species of major commercial importance and some tuna species are a major source of protein in many countries. Tuna are characterized by dynamic distribution patterns that respond to climate variability and long‐term change. Here, we investigated the effect of environmental conditions on the worldwide distribution and relative abundance of six tuna species between 1958 and 2004 and estimated the expected end‐of‐the‐century changes based on a high‐greenhouse gas concentration scenario (RCP8.5). We created species distribution models using a long‐term Japanese longline fishery dataset and two‐step generalized additive models. Over the historical period, suitable habitats shifted poleward for 20 out of 22 tuna stocks, based on their gravity centre (GC) and/or one of their distribution limits. On average, tuna habitat distribution limits have shifted poleward 6.5 km per decade in the northern hemisphere and 5.5 km per decade in the southern hemisphere. Larger tuna distribution shifts and changes in abundance are expected in the future, especially by the end‐of‐the‐century (2080–2099). Temperate tunas (albacore, Atlantic bluefin, and southern bluefin) and the tropical bigeye tuna are expected to decline in the tropics and shift poleward. In contrast, skipjack and yellowfin tunas are projected to become more abundant in tropical areas as well as in most coastal countries' exclusive economic zones (EEZ). These results provide global information on the potential effects of climate change in tuna populations and can assist countries seeking to minimize these effects via adaptive management.     

Large‐scale impact of climate change vs. land‐use change on future biome shifts in Latin America

Climate change and land‐use change are two major drivers of biome shifts causing habitat and biodiversity loss. What is missing is a continental‐scale future projection of the estimated relative impacts of both drivers on biome shifts over the course of this century. Here, we provide such a projection for the biodiverse region of Latin America under four socio‐economic development scenarios. We find that across all scenarios 5–6% of the total area will undergo biome shifts that can be attributed to climate change until 2099. The relative impact of climate change on biome shifts may overtake land‐use change even under an optimistic climate scenario, if land‐use expansion is halted by the mid‐century. We suggest that constraining land‐use change and preserving the remaining natural vegetation early during this century creates opportunities to mitigate climate‐change impacts during the second half of this century. Our results may guide the evaluation of socio‐economic scenarios in terms of their potential for biome conservation under global change.     

Geographic variation in growth response of Douglas‐fir to interannual climate variability and projected climate change

We used 179 tree ring chronologies of Douglas‐fir [Pseudotsuga menziesii (Mirb.) Franco] from the International Tree‐Ring Data Bank to study radial growth response to historical climate variability. For the coastal variety of Douglas‐fir, we found positive correlations of ring width with summer precipitation and temperature of the preceding winter, indicating that growth of coastal populations was limited by summer dryness and that photosynthesis in winter contributed to growth. For the interior variety, low precipitation and high growing season temperatures limited growth. Based on these relationships, we chose a simple heat moisture index (growing season temperature divided by precipitation of the preceding winter and current growing season) to predict growth response for the interior variety. For 105 tree ring chronologies or 81% of the interior samples, we found significant linear correlations with this heat moisture index, and moving correlation functions showed that the response was stable over time (1901–1980). We proceeded to use those relationships to predict regional growth response under 18 climate change scenarios for the 2020s, 2050s, and 2080s with unexpected results: for comparable changes in heat moisture index, the most southern and outlying populations of Douglas‐fir in Mexico showed the least reduction in productivity. Moderate growth reductions were found in the southern United States, and strongly negative response in the central Rocky Mountains. Growth reductions were further more pronounced for high than for low elevation populations. Based on regional differences in the slope of the growth–climate relationship, we propose that southern populations are better adapted to drought conditions and could therefore contain valuable genotypes for reforestation under climate change. The results support the view that climate change may impact species not just at the trailing edges but throughout their range due to genetic adaptation of populations to local environments.     

Mechanistic forecasts of species responses to climate change: The promise of biophysical ecology

A core challenge in global change biology is to predict how species will respond to future environmental change and to manage these responses. To make such predictions and management actions robust to novel futures, we need to accurately characterize how organisms experience their environments and the biological mechanisms by which they respond. All organisms are thermodynamically connected to their environments through the exchange of heat and water at fine spatial and temporal scales and this exchange can be captured with biophysical models. Although mechanistic models based on biophysical ecology have a long history of development and application, their use in global change biology remains limited despite their enormous promise and increasingly accessible software. We contend that greater understanding and training in the theory and methods of biophysical ecology is vital to expand their application. Our review shows how biophysical models can be implemented to understand and predict climate change impacts on species' behavior, phenology, survival, distribution, and abundance. It also illustrates the types of outputs that can be generated, and the data inputs required for different implementations. Examples range from simple calculations of body temperature at a particular site and time, to more complex analyses of species' distribution limits based on projected energy and water balances, accounting for behavior and phenology. We outline challenges that currently limit the widespread application of biophysical models relating to data availability, training, and the lack of common software ecosystems. We also discuss progress and future developments that could allow these models to be applied to many species across large spatial extents and timeframes. Finally, we highlight how biophysical models are uniquely suited to solve global change biology problems that involve predicting and interpreting responses to environmental variability and extremes, multiple or shifting constraints, and novel abiotic or biotic environments.     

Climate‐associated genetic variation in Fagus sylvatica and potential responses to climate change in the French Alps

Local adaptation patterns have been found in many plants and animals, highlighting the genetic heterogeneity of species along their range of distribution. In the next decades, global warming is predicted to induce a change in the selective pressures that drive this adaptive variation, forcing a reshuffling of the underlying adaptive allele distributions. For species with low dispersion capacity and long generation time such as trees, the rapidity of the change could impede the migration of beneficial alleles and lower their capacity to track the changing environment. Identifying the main selective pressures driving the adaptive genetic variation is thus necessary when investigating species capacity to respond to global warming. In this study, we investigate the adaptive landscape of Fagus sylvatica along a gradient of populations in the French Alps. Using a double‐digest restriction‐site‐associated DNA (ddRAD) sequencing approach, we identified 7,000 SNPs from 570 individuals across 36 different sites. A redundancy analysis (RDA)‐derived method allowed us to identify several SNPs that were strongly associated with climatic gradients; moreover, we defined the primary selective gradients along the natural populations of F. sylvatica in the Alps. Strong effects of elevation and humidity, which contrast north‐western and south‐eastern site, were found and were believed to be important drivers of genetic adaptation. Finally, simulations of future genetic landscapes that used these findings allowed identifying populations at risk for F. sylvatica in the Alps, which could be helpful for future management plans.     

Impact of landscape predictors on climate change modelling of species distributions: a case study with Eucalyptus fastigata in southern New South Wales,Australia

Aim We consider three questions. (1) How different are the predicted distribution maps when climate‐only and climate‐plus‐terrain models are developed from high‐resolution data? (2) What are the implications of differences between the models when predicting future distributions under climate change scenarios, particularly for climate‐only models at coarse resolution? (3) Does the use of high‐resolution data and climate‐plus‐terrain models predict an increase in the number of local refugia? Location South‐eastern New South Wales, Australia. Methods We developed two species distribution models for Eucalyptus fastigata under current climate conditions using generalized additive modelling. One used only climate variables as predictors (mean annual temperature, mean annual rainfall, mean summer rainfall); the other used both climate and landscape (June daily radiation, topographic position, lithology, nutrients) variables as predictors. Predictions of the distribution under current climate and climate change were then made for both models at a pixel resolution of 100 m. Results The model using climate and landscape variables as predictors explained a significantly greater proportion of the deviance than the climate‐only model. Inclusion of landscape variables resulted in the prediction of much larger areas of existing optimal habitat. An overlay of predicted future climate on the current climate space indicated that extrapolation of the statistical models was not occurring and models were therefore more robust. Under climate change, landscape‐defined refugia persisted in areas where the climate‐only model predicted major declines. In areas where expansion was predicted, the increase in optimal habitat was always greater with landscape predictors. Recognition of extensive optimal habitat conditions and potential refugia was dependent on the use of high‐resolution landscape data. Main conclusions Using only climate variables as predictors for assessing species responses to climate change ignores the accepted conceptual model of plant species distribution. Explicit statements justifying the selection of predictors based on ecological principles are needed. Models using only climate variables overestimate range reduction under climate change and fail to predict potential refugia. Fine‐scale‐resolution data are required to capture important climate/landscape interactions. Extrapolation of statistical models to regions in climate space outside the region where they were fitted is risky.