Publications

Wireless environmental sensors have become integral tools in environmental and conservation research, offeringdiverse data streams that complement traditional inventory-based surveys. Despite advancements in sensortechnology, the ad hoc nature of site selection for sensor deployment often limits the potential of collected data.Here, we argue for the importance of informed site selection to capture environmental variation effectively. Weintroduce a comprehensive step-by-step practical guide for environmental sensor site selection and networkdeployment, drawing on experiences from diverse geographic locations and focusing specifically on microclimatesensors as a representative environmental variable. The workflow integrates Geographic Information Systems(GIS) tools, local community-based knowledge, and statistical methods to provide adaptive and iterativeguidelines for both new and expanded sensor deployments. We demonstrate how the workflow facilitatedresearch across three distinct settings: measuring heat waves in urban and rural gardens in Belgium, informingplant conservation in arid montane deserts in Oman, and monitoring amphibian distributions in humid forestedlandscapes in Madagascar. To facilitate the workflow’s implementation and reproducibility worldwide, weprovide a modular software supplement with flexible user input for robust, data-driven and interactive site se-lection. Critically, our workflow underscores the importance of equitable collaboration with local stakeholders,addresses challenges in sensor deployment, and offers a practical tool to enhance the effectiveness and efficiencyof environmental sensing across disciplines including ecology, meteorology, agriculture, and landscape design.

Climate change is altering species’ distributions globally. Increasing frequency ofextreme weather and climate events (EWCEs) is one of the hallmarks of climatechange. Despite species redistribution being widely studied in response to long-term climatic trends, the contribution of EWCEs to range shifts is not well under-stood. We outline how EWCEs can trigger rapid and unexpected range boundaryfluctuations by impacting dispersal, establishment, and survival. Whether thesemechanisms cause temporary or persistent range shifts depends on the spatio-temporal context and exposure to EWCEs. Using the increasing availability ofdata and statistical tools to examine EWCE impacts at fine spatiotemporal resolu-tions on species redistribution will be critical for informing conservation manage-ment of ecologically, economically, and culturally important species.

Community-based monitoring (CBM) could enable long-term biodiversity monitoring in remote areas and benefit local communities, yet is rarely used to facilitate conservation efforts often due to mistrust in the data collected. We use a multi-criteria decision analysis framework to systematically examine the scientific and socioeconomic values and financial costs associated with biodiversity monitoring for vertebrates by scientists and local community members in six protected areas (PAs) in Madagascar, encompassing diverse ecosystems spanning tropical rainforests to spiny deserts. We compare the number of species observed during scientist and community surveys, identify the ‘ideal’ number of scientist and community surveys that would be required to maximize the scientific and socioeconomic values of monitoring efforts while minimizing financial cost, and compare monitoring plans across several conservation philosophies representing “ecocentric” and “people-centered” perspectives. Scientists generally observed more species than community members. However, including a greater proportion of surveys conducted by community members lowered the financial cost of travel and compensation while maximizing ecological and social objectives associated with diverse conservation philosophies. While the valuation schemes we use are simplistic representations of the complex costs and values associated with CBM, this study indicates the benefits of community monitoring regardless of the conservation philosophy used to anchor valuation and decision-making. Increasing integration of CBM into existing conservation management could therefore offer a financially viable method to consistently monitor biodiversity and benefit local communities in the face of limited funding and global challenges.

Climate change is already leaving a broad footprint of impacts on biodiversity, from an individual caterpillar emerging earlier in spring to dominant plant communities migrating poleward. Despite the various modes of how species are on the move, we primarily document shifting species along only one gradient (e.g., latitude or phenology) and along one dimension (space or time). In this opinion article we present a unifying framework for integrating the study of species on the move over space and time and from micro to macro scales. Future conservation planning and natural resource management will depend on our ability to use this framework to improve understanding, attribution, and prediction of species on the move.

Scientists have long categorized the planet’s climate using the Köppen-Geiger (KG) classification to research climate-change impacts, biogeographical realms, agricultural suitability, and conservation. However, global KG maps primarily rely on macroclimate data collected by weather stations, which may not represent microclimatic conditions experienced by most life on Earth. Few studies have explored microclimate at broad scales, largely due to data and computational constraints. Here, we predicted KG classes separately from macroclimate and microclimate for more than 32 million locations across six continents. As compared to macroclimate, microclimate had 14-fold lower error and reclassified 38% of the total area. Microclimate-derived KG classes were not only more spatially variable but also encompassed a broader range of latitudes, relative to macroclimate-derived KG classes. By redrawing the lines of climate classes, our study prompts a reevaluation of the importance of meteorological drivers of ecology across scales, shedding light on how natural, agricultural, and social systems experience and respond to global change.

Climate influences when leaves change colour and fall, but not all trees lose their leaves at the same time. Combining field data, mathematical models and remote sensing, researchers show how local-scale variation in tree canopies and understory temperatures alters the start and duration of autumn leaf colouration and forecast reduced autumn delays under climate change.

Thermoregulatory behaviour determines an organism’s body temperature and therefore its physiological condition, and may differ for organisms situated across climate gradients. Species’ preferred or selected temperatures may be higher in warmer locations – referred to as coadaptation – or lower in warmer temperatures – countergradient variation. Here, we tested if rainforest amphibians exhibited coadaptation or countergradient thermal selection across an underappreciated spatial climate gradient (vertical height from forest floor to canopy) and separating diel activity (diurnal versus nocturnal behaviour). We captured 2,534 amphibians over 216 ground-to-canopy surveys, and conducted 282 thermal selection assays for 37 species, while pairing microclimate measurements and mechanistic model predictions to understand vertical and daily thermal variation in the field. Amphibians exhibited countergradient thermal selection: species occupying cool nocturnal conditions in canopies selected warmer temperatures than species occupying hot diurnal conditions at the forest floor. Furthermore, amphibians selected warmer temperatures than the average conditions that they were exposed to when active, and this divergence was especially high for nocturnal arboreal species (8.68 °C). This suggests that rainforest amphibians dramatically underfill the warm end of their thermal niches, a trend across local thermal gradients that reflects recent findings across elevational and latitudinal gradients. We show that considering multidimensional climate gradients is important to evaluate thermoregulatory behaviour, and its evolutionary underpinnings, for understanding species’ niches and community assembly.

Demonstrated via a global synthesis that relevant “microclimate” has more to do with representing the proximal microhabitats for a species than the spatial or temporal resolution of climate data.

1. Most biodiversity dynamics and ecosystem processes on land take place in microclimates that are decoupled from the climate as measured by standardised weather stations in open, unshaded locations. As a result, microclimate monitor- ing is increasingly being integrated in many studies in ecology and evolution. 2. Overviews of the protocols and measurement methods related to microclimate are needed, especially for those starting in the field and to achieve more general- ity and standardisation in microclimate studies. 3. Here, we present 10 practical guidelines for ground-based research of terrestrial microclimates, covering methods and best practices from initial conceptualisation of the study to data analyses. 4. Our guidelines encompass the significance of microclimates; the specifics of what, where, when and how to measure them; the design of microclimate studies; and the optimal approaches for analysing and sharing data for future use and collaborations. The paper is structured as a chronological guide, leading the reader through each step necessary to conduct a comprehensive microclimate study. At the end, we also discuss further research avenues and development in this field. 5. With these 10 guidelines for microclimate monitoring, we hope to stimulate and advance microclimate research in ecology and evolution, especially under the pressing need to account for buffering or amplifying abilities of contrasting microhabitats in the context of global climate change.

Tropical forest biodiversity is potentially at high risk from climate change, but most species reside within or beneath the canopy, where they are buffered from extreme temperatures, implying that forest canopies may reduce the severity of warming impacts. Using a mechanistic microclimate model, we model hourly below-canopy climate conditions of 300,000 tropical forest locations globally between 1990–2019. We show that while temperature extremes are buffered below canopy, recent small increases in beneath-canopy temperature (<1ºC) have led to highly novel temperature regimes across most of the tropics. This is the case even within contiguous forest, suggesting that tropical forests are sensitive to climate change. However, across the globe, some forest areas have experienced relatively non-novel temperature regimes and thus serve as important climate refugia. These areas require urgent protection and restoration. By conducting the first pan-tropical analyses of changes in below-canopy climatic conditions, we challenge the prevailing notion that tropical forest canopies reduce the severity of climate change impacts.

Local community members can contribute to biodiversity conservation, especially for rural yet critically biodiverse locations such as in Southern Madagascar. While collaborations with local communities were initiated by Madagascar National Parks (MNP) in 1996 to build local support for protected area management, such community-based approaches to monitoring biodiversity were underdeveloped, and to date their efficacy has not been studied. The objective of our study was to develop community-based monitoring of vertebrate biodiversity within six protected areas in Madagascar, and to assess whether the data recorded by local communities can be used for the monitoring of protected areas spanning dry to wet tropical rainforest ecosystems. We implemented a training program for each local community and validated community observations via surveys performed by professional scientists with taxonomic expertise. Across two years of surveys and six protected areas, scientists observed more species per survey (9.04) than community members (6.09). Yet collectively, community members observed more species (373) than scientists (354). Furthermore via multivariate modeling, we found that whether a biodiversity monitoring team was composed of scientists or community members had a non-significant effect on the number of species observed, which was more sensitive to the vegetation and climate of a location. Our study suggests that for biodiversity monitoring in Madagascar, professional scientists are likely more efficient, yet with sufficient survey effort, local community members can provide comparable estimates of species richness. We discuss the benefits and limits of incorporating community-based monitoring into surveys of vertebrate biodiversity in speciose tropical systems.

Models have become a key component of scientific hypothesis testing and climate and sustainability planning, as enabled by increased data availability and computing power. As a result, understanding how the perceived ‘complexity’ of a model corresponds to its accuracy and predictive power has become a prevalent research topic. However, a wide variety of definitions of model complexity have been proposed and used, leading to an imprecise understanding of what model complexity is and its consequences across research studies, study systems, and disciplines. Here, we propose a more explicit definition of model complexity, incorporating four facets—model class, model inputs, model parameters, and computational complexity—which are modulated by the complexity of the real-world process being modelled. We illustrate these facets with several examples drawn from ecological literature. Overall, we argue that precise terminology and metrics of model complexity (e.g., number of parameters, number of inputs) may be necessary to characterize the emergent outcomes of complexity, including model comparison, model performance, model transferability and decision support.

Brief introduction: What are microclimates and why are they important? Microclimate science has developed into a global discipline. Microclimate science is increasingly used to understand and mitigate climate and biodiversity shifts. Here, we provide an overview of the current status of microclimate ecology and biogeography in terrestrial ecosystems, and where this field is heading next. Microclimate investigations in ecology and biogeography: We highlight the latest research on interactions between microclimates and organisms, including how microclimates influence individuals, and through them populations, communities and entire ecosystems and their processes. We also briefly discuss recent research on how organisms shape microclimates from the tropics to the poles. Microclimate applications in ecosystem management: Microclimates are also mportant in ecosystem management under climate change. We showcase new research in microclimate management with examples from biodiversity conservation, forestry and urban ecology. We discuss the importance of microrefugia in conservation and how to promote microclimate heterogeneity. Methods for microclimate science: We showcase the recent advances in data acquisition, such as novel field sensors and remote sensing methods. We discuss microclimate modelling, mapping and data processing, including accessibility of modelling tools, advantages of mechanistic and statistical modelling and solutions for computational challenges that have pushed the state-of-the-art of the field. What's next?We identify major knowledge gaps that need to be filled for further advancing microclimate investigations, applications and methods. These gaps include spatiotemporal scaling of microclimate data, mismatches between macroclimate and microclimate in predicting responses of organisms to climate change, and the need for more evidence on the outcomes of microclimate management.

Quantifying carbon fluxes into and out of coastal soils is critical to meeting greenhouse gas reduction and coastal resiliency goals. Numerous ‘blue carbon’ studies have generated, or benefitted from, synthetic datasets. However, the community those efforts inspired does not have a centralized, standardized database of disaggregated data used to estimate carbon stocks and fluxes. In this paper, we describe a data structure designed to standardize data reporting, maximize reuse, and maintain a chain of credit from synthesis to original source. We introduce version 1.0.0. of the Coastal Carbon Library, a global database of 6723 soil profiles representing blue carbon-storing systems including marshes, mangroves, tidal freshwater forests, and seagrasses. We also present the Coastal Carbon Atlas, an R-shiny application that can be used to visualize, query, and download portions of the Coastal Carbon Library. The majority (4815) of entries in the database can be used for carbon stock assessments without the need for interpolating missing soil variables, 533 are available for estimating carbon burial rate, and 326 are useful for fitting dynamic soil formation models. Organic matter density significantly varied by habitat with tidal freshwater forests having the highest density, and seagrasses having the lowest. Future work could involve expansion of the synthesis to include more deep stock assessments, increasing the representation of data outside of the U.S., and increasing the amount of data available for mangroves and seagrasses, especially carbon burial rate data. We present proposed best practices for blue carbon data including an emphasis on disaggregation, data publication, dataset documentation, and use of standardized vocabulary and templates whenever appropriate. To conclude, the Coastal Carbon Library and Atlas serve as a general example of a grassroots F.A.I.R. (Findable, Accessible, Interoperable, and Reusable) data effort demonstrating how data producers can coordinate to develop tools relevant to policy and decision-making.

_Short:_ In several years of community and scientist observations across six parks in Madagascar, we show that 1) women perform better than men at biodiversity surveys, and 2) amount of formal education is not a strong predictor of success. We discuss how to co-design projects with diverse community partners in an effort towards decoloniality

Community-based monitoring (CBM)– programs that integrate community members and their values into biodiversity and/or natural resource monitoring– is an effective tool for conservation. Wide inequities exist in CBM collaboration, and monitoring abilities may vary between collaborators of different backgrounds. Therefore exploring the demographic composition of CBM collaborators, and how demographics shape individual monitoring efficacy, can help improve both diversity in CBM representation and program outcomes. Yet, few studies have focused on CBM collaborator demographics, especially in low-income countries. We implemented a CBM project co-designed by protected area managers and local community members in the geographically, biologically, and culturally diverse Southern Madagascar. The project involved 27 scientists and 83 community members who collectively generated 69,429 observations of birds, mammals, amphibians and reptiles across two years (2917 surveys). Using linear regressions and mixed-effects models, we examined how collaborators’ demographics (gender, age, and level of formal education) and their prior amount of biological monitoring experience impacted their efficacy, measured as the number of observed species. For both scientists and community members, monitoring teams with women, despite being underrepresented, on average observed more species than male-only teams. Among community members, age and level of formal education had smaller positive effects on efficacy. Our results suggest that CBM projects should actively engage a broad array of community members, including those with marginalized identities, to provide diverse perspectives. Inclusive initiatives offer both tangible (lower project costs) and intangible (community engagement, education, and enhanced collaboration) benefits for local communities and conservation managers alike.

Tropical forests harbour the highest levels of terrestrial biodiversity and represent some of the most complex ecosystems on Earth, with a significant portion of this diversity above ground. Although the vertical dimension is a central aspect of the ecology of forest communities, there is little consensus as to prominence, evenness, and consistency of communitylevel stratification from ground to canopy. Here, we gather the results of 62 studies across the tropics to synthesise and assess broad patterns of vertical stratification of abundance and richness in vertebrates, the best studied taxonomic group for which results have not been collated previously. Our review of the literature yielded sufficient data for bats, small mammals, birds and amphibians. We show that variation in the stratification of abundance and richness exists within and among all taxa considered. Bat richness stratification was variable among studies, although bat abundance was weighted towards the canopy. Both bird richness and abundance stratification were variable, with no overriding pattern. On the contrary, both amphibians and small mammals showed consistent patterns of decline in abundance and richness towards the canopy. We descriptively characterise research trends in drivers of stratification cited or investigated within studies, finding local habitat structure and food distribution/foraging to be the most commonly attributed drivers. Further, we analyse the influence of macroecological variables on stratification patterns, finding latitude and elevation to be key predictors of bird stratification in particular. Prominent differences among taxa are likely due to taxon-specific interactions with local drivers such as vertical habitat structure, food distribution, and vertical climate gradients, which may vary considerably across macroecological gradients such as elevation and biogeographic realm. Our study showcases the complexity with which animal communities organise within tropical forest ecosystems, while demonstrating the canopy as a critical niche space for tropical vertebrates, thereby highlighting the inherent vulnerability of tropical vertebrate communities to forest loss and canopy disturbance. We recognise that analyses were constrained due to variation in study designs and methods which produced a variety of abundance and richness metrics recorded across different arrangements of vertical strata. We therefore suggest the application of best practices for data reporting and highlight the significant effort required to fill research gaps in terms of under-sampled regions, taxa, and environments.

_Short:_ Motivation, description, & validation of an R package to download and process ERA5 data for ecology. 1. Microclimate models predict temperature and other meteorological variables at scales relevant to individual organisms. The broad application of microclimate models requires gridded macroclimatic variables as input. However, the spatial and temporal resolution of such inputs can be a limiting factor on the accuracy of microclimate predictions. Due to its fine resolution and accuracy, the ERA5 reanalysis dataset is emerging as the favoured resource for global historical weather and climate data and has great potential for aiding microclimate modelling. 2. Here we describe mcera 5, an R language package that provides convenient access to, and wrangling of, the ERA5 climate datasets for use in microclimate models. Through this package, we provide functions to query ERA5 data for desired spatial and temporal extents, to correct for spatial biases and process outputs for easy interpretation by ecologists, thereby allowing faster and more accurate microclimate predictions. 3. By validating with empirical observations from multiple biomes globally, we demonstrate that the use of ERA5 climate forcing via mcera 5 improves the prediction accuracy of soil moisture, air temperature and relative humidity as compared to forcing with other globally available data and offers comparable performance when predicting soil temperatures. 4. Through the provision of fine-resolution ERA5 data, the mcera 5 package fits into an ecosystem of tools for modelling microclimate in a spatio- temporally explicit fashion, advancing our ability to efficiently predict microclimate for any place on Earth for the past, present or future. The package also provides convenient access to ERA5 datasets for a range of other applications.

Snow is an important driver of ecosystem processes in cold biomes. Snow accumulation determines ground temperature, light conditions, and moisture availability during winter. It also affects the growing season’s start and end, and plant access to moisture and nutrients. Here, we review the current knowledge of the snow cover’s role for vegetation, plant-animal interactions, permafrost conditions, microbial processes, and biogeochemical cycling. We also compare studies of natural snow gradients with snow experimental manipulation studies to assess time scale difference of these approaches. The number of tundra snow studies has increased considerably in recent years, yet we still lack a comprehensive overview of how altered snow conditions will affect these ecosystems. Specifically, we found a mismatch in the timing of snowmelt when comparing studies of natural snow gradients with snow manipulations. We found that snowmelt timing achieved by snow addition and snow removal manipulations (average 7.9 days advance and 5.5 days delay, respectively) were substantially lower than the temporal variation over natural spatial gradients within a given year (mean range 56 days) or among years (mean range 32 days). Differences between snow study approaches need to be accounted for when projecting snow dynamics and their impact on ecosystems in future climates.

Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2m above the ground. These climatic grids do not reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions occur and most terrestrial species reside. Here, we provide global maps of soil temperature and bioclimatic variables at a 1-km2 resolution for 0–5 and 5–15 cm soil depth. These maps were created by calculating the difference (i.e. offset) between in situ soil temperature measurements, based on time series from over 1200 1-km2 pixels (summarized from 8519 unique tempera - ture sensors) across all the world's major terrestrial biomes, and coarse-grained air temperature estimates from ERA5-Land (an atmospheric reanalysis by the European Centre for Medium-Range Weather Forecasts). We show that mean annual soil temperature differs markedly from the corresponding gridded air temperature, by up to 10°C (mean = 3.0 ± 2.1°C), with substantial variation across biomes and seasons. Over the year, soils in cold and/or dry biomes are substantially warmer (+3.6 ± 2.3°C) than gridded air temperature, whereas soils in warm and humid environments are on average slightly cooler (−0.7 ± 2.3°C). The observed substantial and biome-specific offsets emphasize that the projected impacts of climate and climate change on near-surface biodiversity and ecosystem functioning are inaccurately assessed when air rather than soil temperature is used, especially in cold environments. The global soil-related bioclimatic variables provided here are an important step forward for any application in ecology and related disciplines. Nevertheless, we highlight the need to fill remaining geographic gaps by collecting more in situ measurements of microclimate conditions to further enhance the spatiotemporal resolution of global soil temperature products for ecological applications.

In the age of big data, soil data are more available and richer than ever, but – outside of a few large soil survey resources – they remain largely unusable for informing soil management and understanding Earth system processes beyond the original study. Data science has promised a fully reusable research pipeline where data from past studies are used to contextualize new findings and reanalyzed for new insight. Yet synthesis projects encounter challenges at all steps of the data reuse pipeline, including unavailable data, labor-intensive transcription of datasets, incomplete metadata, and a lack of communication between collaborators. Here, using insights from a diversity of soil, data, and climate scientists, we summarize current practices in soil data synthesis across all stages of database creation: availability, input, harmonization, curation, and publication. We then suggest new soil-focused semantic tools to improve existing data pipelines, such as ontologies, vocabulary lists, and community practices. Our goal is to provide the soil data community with an overview of current practices in soil data and where we need to go to fully leverage big data to solve soil problems in the next century.

Forest canopies buffer macroclimatic temperature fluctuations. However, we do not know if and how the capacity of canopies to buffer understorey temperature will change with accelerating climate change. Here we map the difference (offset) between temperatures inside and outside forests in the recent past and project these into the future in boreal, temperate and tropical forests. Using linear mixed-effect models, we combined a global database of 714 paired time series of temperatures (mean, minimum and maximum) measured inside forests vs. in nearby open habitats with maps of macroclimate, topography and forest cover to hindcast past (1970–2000) and to project future (2060–2080) temperature differences between free-air temperatures and sub-canopy microclimates. For all tested future climate scenarios, we project that the difference between maximum temperatures inside and outside forests across the globe will increase (i.e. result in stronger cooling in forests), on average during 2060–2080, by 0.27 ± 0.16 °C (RCP2.6) and 0.60 ± 0.14 °C (RCP8.5) due to macroclimate changes. This suggests that extremely hot temperatures under forest canopies will, on average, warm less than outside forests as macroclimate warms. This knowledge is of utmost importance as it suggests that forest microclimates will warm at a slower rate than non-forested areas, assuming that forest cover is maintained. Species adapted to colder growing conditions may thus find shelter and survive longer than anticipated at a given forest site. This highlights the potential role of forests as a whole as microrefugia for biodiversity under future climate change.

Climate strongly influences ecological patterns and processes at scales ranging from local to global. Studies ofecological responses to climate usually rely on data derived from weather stations, where temperature andhumidity may differ substantially from that in the microenvironments in which organisms reside. To help remedythis, we present a model that leverages first principles physics to predict microclimate above, within, and belowthe canopy in any terrestrial location on earth, made freely available as an R software package. The model can berun in one of two modes. In the first, heat and vapour exchange within and below canopy are modelled astransient processes, thus accounting for fine temporal-resolution changes. In the second, steady-state conditionsare assumed, enabling conditions at hourly intervals or longer to be estimated with greater computational ef-ficiency. We validated both modes of the model with empirical below-canopy thermal measurements fromseveral locations globally, resulting in hourly predictions with mean absolute error of 2.77 ◦C and 2.79 ◦C for thetransient and steady-state modes respectively. Alongside the microclimate model, several functions are providedto assist data assimilation, as well as different parameterizations to capture a variety of habitats, allowing flexibleapplication even when little is known about the study location. The model’s modular design in a programminglanguage familiar to ecological researchers provides easy access to the modelling of site-specific climate forcing,in an attempt to more closely unify the fields of micrometeorology and ecology.

The opportunity to participate in and contribute to emerging fields is increasingly prevalentin science. However, simply thinking about stepping outside of your academic silo can leavemany students reeling from the uncertainty. Here, we describe 10 simple rules to success-fully train yourself in an emerging field, based on our experience as students in the emergingfield of ecological forecasting. Our advice begins with setting and revisiting specific goals toachieve your academic and career objectives and includes several useful rules for engagingwith and contributing to an emerging field.

Forest microclimates contrast strongly with the climate outside forests. To fully un-derstand and better predict how forests' biodiversity and functions relate to climateand climate change, microclimates need to be integrated into ecological research.Despite the potentially broad impact of microclimates on the response of forest eco -systems to global change, our understanding of how microclimates within and belowtree canopies modulate biotic responses to global change at the species, communityand ecosystem level is still limited. Here, we review how spatial and temporal variationin forest microclimates result from an interplay of forest features, local water balance,topography and landscape composition. We first stress and exemplify the importanceof considering forest microclimates to understand variation in biodiversity and eco-system functions across forest landscapes. Next, we explain how macroclimate warm-ing (of the free atmosphere) can affect microclimates, and vice versa, via interactionswith land-use changes across different biomes. Finally, we perform a priority rankingof future research avenues at the interface of microclimate ecology and global changebiology, with a specific focus on three key themes: (1) disentangling the abiotic and biotic drivers and feedbacks of forest microclimates; (2) global and regional mappingand predictions of forest microclimates; and (3) the impacts of microclimate on forestbiodiversity and ecosystem functioning in the face of climate change. The availabilityof microclimatic data will significantly increase in the coming decades, characterizingclimate variability at unprecedented spatial and temporal scales relevant to biologicalprocesses in forests. This will revolutionize our understanding of the dynamics, driv-ers and implications of forest microclimates on biodiversity and ecological functions,and the impacts of global changes. In order to support the sustainable use of forestsand to secure their biodiversity and ecosystem services for future generations, microclimates cannot be ignored.

_Short_: Revisited Janzen (1967)’s classic “Mountain Passes” hypothesis with a global synthesis (29 mountains) and showed that a mountain’s latitude (tropical vs. temperate) was not important for determining its strength as a climatic barrier; rather local vegetation, snow, and microhabitats were more important.

Full: An extension of the climate variability hypothesis is thatrelatively stable climate, such as that of the tropics, induces distinctthermal bands across elevation that render dispersal over tropical moun-tains difficult compared with temperate mountains. Yet ecosystemsare not thermally static in space-time, especially at small scales, whichmight render some mountains greater thermal isolators than others.Here we provide an extensive investigation of temperature driversfrom fine to coarse scales, and we demonstrate that the degree of sim-ilarity in temperatures at high and low elevations on mountains isdriven by more than just absolute mountain height and latitude. Wecompiled a database of 29 mountains spanning six continents to char-acterize thermal overlap by vertically stratified microhabitats and bi-omes and owing to seasonal changes in foliage, demonstrating via mixedeffects modeling that micro- and mesogeography more strongly in-fluence thermal overlap than macrogeography. Impressively, an in-crease of 1 m of vertical microhabitat height generates an increasein overlap equivalent to a 5.267 change in latitude. In addition, forestedmountains have reduced thermal overlap—149% lower—relative tononforested mountains. We provide evidence in support of a climatehypothesis that emphasizes microgeography as a determinant of dis-persal, demographics, and behavior, thereby refining the classical the-ory of macroclimate variability as a prominent driver of biogeography.

Current analyses and predictions of spatially explicit patterns and processes in ecol-ogy most often rely on climate data interpolated from standardized weather stations.This interpolated climate data represents long-term average thermal conditions atcoarse spatial resolutions only. Hence, many climate-forcing factors that operate atfine spatiotemporal resolutions are overlooked. This is particularly important in rela-tion to effects of observation height (e.g. vegetation, snow and soil characteristics)and in habitats varying in their exposure to radiation, moisture and wind (e.g. topog-raphy, radiative forcing or cold-air pooling). Since organisms living close to the groundrelate more strongly to these microclimatic conditions than to free-air temperatures,microclimatic ground and near-surface data are needed to provide realistic forecastsof the fate of such organisms under anthropogenic climate change, as well as of thefunctioning of the ecosystems they live in. To fill this critical gap, we highlight a callfor temperature time series submissions to SoilTemp, a geospatial database initiativecompiling soil and near-surface temperature data from all over the world. Currently,this database contains time series from 7,538 temperature sensors from 51 countries across all key biomes. The database will pave the way toward an improved global understanding of microclimate and bridge the gap between the available climate data and the climate at fine spatiotemporal resolutions relevant to most organisms and ecosystem processes.

Current analyses and predictions of spatially explicit patterns and processes in ecol-ogy most often rely on climate data interpolated from standardized weather stations.This interpolated climate data represents long-term average thermal conditions atcoarse spatial resolutions only. Hence, many climate-forcing factors that operate atfine spatiotemporal resolutions are overlooked. This is particularly important in rela-tion to effects of observation height (e.g. vegetation, snow and soil characteristics)and in habitats varying in their exposure to radiation, moisture and wind (e.g. topog-raphy, radiative forcing or cold-air pooling). Since organisms living close to the groundrelate more strongly to these microclimatic conditions than to free-air temperatures,microclimatic ground and near-surface data are needed to provide realistic forecastsof the fate of such organisms under anthropogenic climate change, as well as of thefunctioning of the ecosystems they live in. To fill this critical gap, we highlight a callfor temperature time series submissions to SoilTemp, a geospatial database initiativecompiling soil and near-surface temperature data from all over the world. Currently,this database contains time series from 7,538 temperature sensors from 51 countries across all key biomes. The database will pave the way toward an improved global understanding of microclimate and bridge the gap between the available climate data and the climate at fine spatiotemporal resolutions relevant to most organisms and ecosystem processes.