Biodiversity loss is one of the most relevant consequences of climate change. Therefore, identifying areas and environmental features that allow certain organisms to be less exposed to the effects of the current global warming is priority for biodiversity conservation. In this study, we describe a novel approach for the identification of microclimatic refugia in rugged mountain areas, specifically for the detection of most thermally stable areas, using an unmanned aerial vehicle (UAV) capable of recording in the visible and thermal infrared spectral bands. We estimated land surface temperatures (LST) at very-high spatial resolution in six topographically complex sectors of the Pyrenees (NE Spain), across seasons with vegetative activity (summer 2020, autumn 2020, spring 2021, and summer 2021), and at two thermally contrasted times of the day (early in the morning: LSTmin, and in the afternoon: LSTmax). LST were validated with a network of miniaturized temperature sensors in the field. LSTmin and LSTmax allowed us to calculate the daily thermal range of each sector across the seasons, and thus the most thermally stable areas over the year. To reveal the importance of different variables on low and narrow thermal ranges we applied Gradient Boosted Models to seven terrain variables derived from ALS-LiDAR (slope, northness, eastness, heat load, wind exposure index, SAGA's topographic wetness index, and vector ruggedness measure) and a proxy of forest density through the three-dimensional point clouds of the UAV data. The northness was the variable that most promoted thermal stability, followed by the slope and forest density, so that microclimatic refugia resulted to be located in northern slopes, small sites under rocky cliffs, and forested areas. Our results demonstrate that thermal UAVs can become promising tools for the identification of microclimatic refugia in topographically complex areas, providing information at unprecedented spatial resolution, and thus of high interest for biodiversity conservation., This work was supported by the Spanish Association of Terrestrial Ecology (AEET) through the program “Grants for research projects led by young researchers 2019”; the Spanish Ministry of Science, Innovation, and Universities through an FPU predoctoral contract granted to R.H. (FPU18/05027); and the Spanish Research Agency through the VULBIMON (CGL2017-90040-R) and REFUGIA (PID2021-129056OB-I00) projects to M.B.G., Peer reviewed

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 important 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., The Microclimate Ecology & Biogeography conference in Antwerp, Belgium in 2022 was supported by the Research Foundation Flanders (project W001919N). JK acknowledges funding from the Academy of Finland (grant no. 349606). JJL and IN acknowledge funding from the Research Foundation Flanders (project 12P1819N) and from BiodivERsA (ASICS project (ANR-20-EBI5-0004, BiodivERsA, BiodivClim call 2019–2020)). JC acknowledges the funding from PID2020-117636GB-C21 and TED2021-132007B-I00. NIC was funded by a Swiss National Science Foundation Postdoc Mobility Fellowship (Grant ID: 194331). PK acknowledges funding from European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement no. 864287 —THRESHOLD—ERC-2019-COG). JL acknowledges funding from the Agence Nationale de la Recherche (ANR), under the framework of the young investigators' funding scheme (JCJC Grant N°ANR-19-CE32-0005-01: IMPRINT project). DL is financially supported by the FWF Austrian Science Foundation (Lise Meitner Programme M2714-B29). JP and MV were supported by BiodivClim Call 2019 (TACR SS70010001; Technology Agency of the Czech Republic), the project DivLand (TACR SS02030018) and long-term research development project RVO 67985939 (Czech Academy of Sciences). SS is supported by the project no. 2022/45/B/ST10/03423 funded by the National Science Centre in Poland. VV acknowledges funding from the Research Council of Norway (grant no. 315249, 274712, 244525). SN and JvO acknowledge funding from the Independent Research Fund of Denmark (grant no. 7027-00133B to SN). JA acknowledges the Academy of Finland Flagship funding (grant no. 337552). RB acknowledges funding from the Saxon State Ministry for Science, Culture and Tourism (SMWK)—[3-7304/35/6-2021/48880]. JeBo acknowledges the funding from the AgroParisTech/ Région Grand-Est joint grant 19_GE8_01020p05035. JoBr was supported by the Czech Science Foundation (project 20-28119S) and the long-term research development project RVO 67985939 (Czech Academy of Sciences). ACK acknowledges funding from FOVI 210043 and ANILLO ACONCAGUA ANID ACT 210021. KDP acknowledges funding from the Research Foundation Flanders (FWO) (K.D.P. ASP035-19). LD acknowledges funding from the Research Foundation Flanders (FWO) (1221523 N). DES acknowledges funding from NASA FINESST (80NSSC22K1535). RE acknowledges funding: UPV/EHU-GV IT-1648–22 and PID2020–113244GA-C22 (funded by MCIN/ AEI /10.13039/501100011033). EG is funded by the Natural Environment Research Council (NERC) NE/S007407/1. MBG acknowledges the support of the REFUGIA project (PID2021-129056OB-I00). CG received funding from FORMAS [project nr. 2021- 01993]. EG's PhD was funded by the Agence Nationale de la Recherche (ANR), under the framework of the young investigators' funding scheme (JCJC Grant N°ANR-19-CE32-0005-01: IMPRINT project). SH received funding from a FLOF fellowship of the KU Leuven (project nr. 3E190655). HH research is funded by national funds by FCT, under the transitional rule of Decree Law 57/2016—DL57/2016/CP 1334 CT0005. RH acknowledges funding from the Spanish Association of Terrestrial Ecology (AEET) through the programme ‘Grants for research projects led by young researchers 2019’. The research was supported by a grant from Formas to KH [2021-00816]. BJA is funded by grant MCI-20-PID2019-108636GA-I00 of the Spanish Research Agency. TJ was supported by a NERC Independent Research Fellowship (grant code: NE/S01537X/1). MK was supported by the Czech Science Foundation (project 20-28119S) and the Czech Academy of Sciences (project RVO 67985939). BK was supported by the ÚNKP-22-4 New National Excellence Program of the Ministry for Culture and Innovation from the source of the National Research, Development and Innovation Fund (ÚNKP-22-4-II-ELTE-318). FM was supported by Slovak Research and Development Agency project APVV-19-0319. MM was supported by the Czech Science Foundation (project 20-28119S) and the Czech Academy of Sciences (project RVO 67985939). CM acknowledges the IntegSaatprojekt and its funders (FKZ: 2220WK65X4), as well as the Honours Programme for Future Researchers at the Friedrich-Schiller-University. EM acknowledges the funding from the Région Sud Provence-Alpes-Côte d'Azur (AAP 2020 n°02697 MICROMED project). JJL and IN acknowledge funding from the Research Foundation Flanders (project 12P1819N) and from BiodivERsA (ASICS project (ANR-20-EBI5-0004, BiodivERsA, BiodivClim call 2019–2020)). DR is supported by the ASICS project (ANR-20-EBI5-0004, BiodivERsA, BiodivClim call 2019–2020), the French Polar Institute (Project 136-SUBANTECO), Zone Atelier CNRS Antarctique et Terres Australes (ZATA ‘Antarctic’) and CNRS (IRP PRICES). TR acknowledges funding from the Doctoral Programme in Geosciences. JMSD was supported by the ANR-JCJC (Agence Nationale de la Recherche, jeunes chercheuses et jeunes chercheurs) SEEDFOR (ANR-21-CE32-0003). JMSD acknowledges the support from NASA for UConn's Ecological Modelling Institute (#80NSSC 22 K0883). JCS considers this work a contribution to the Center for Ecological Dynamics in a Novel Biosphere (ECONOVO), funded by the Danish National Research Foundation (grant DNRF173) and his VILLUM Investigator project ‘Biodiversity Dynamics in a Changing World’, funded by VILLUM FONDEN (grant 16549). SVDV is a PhD fellow supported by the Research Foundation Flanders (FWO; 1S90923N). LvdB acknowledges Conaf, and Comunidad agricola Quebrada de Talca, Chile. MV was supported by a research grant from the BiodivClim Call 2019 (grant nr. TACR SS70010001) and long-term research development project RVO 67985939 from the Czech Academy of Sciences. can have the same acknowledgement as Jan Pergl. MV-I was supported by the Spanish Ministry of Science and Innovation through a doctoral grant (FPU17/05869). JW was supported by the Czech Science Foundation (project 20-28119S) and the Czech Academy of Sciences (project RVO 67985939). F.Z. was funded by the Swiss National Science Foundation (project number 193645). E.Z. was funded by the Global Challenges Research Fund through the Open University and the EarthWatch Community Science Camp (NERC-UKRI Grant no. NE/S017437/1)., Peer reviewed

Cliffs are marginal and poorly studied habitats that are home to a high proportion of endemic or threatened species. Here, we reviewed the survival patterns and population dynamics of plants growing on vertical cliffs and compared them to other plants with similar life forms that grow on the ground. To this end, we have compiled studies of cliff and ground-level plants from two main sources: MONITO and COMPADRE. The resulting dataset after applying some filters included a total of 243 populations of 134 plant species monitored for several years in the northern hemisphere. We tested whether survival rates of seedlings and older stages, population growth rates, and their temporal variability showed similar patterns between cliff and ground plants, and the relationship between those demographic parameters. Cliff plants showed higher survival rates for both seedlings and older life stages, as well as lower distance to population equilibrium and more stable population trends over time, compared to plants growing in the ground. Population equilibrium and temporal stability was highly correlated with the survival of post-seedling stages in both groups. These results suggest that cliff plants may be better adapted to withstand climatic variability than the more common ground-level plants occurring in less stressful environments., The long-term information used here has been funded by different projects led by MBG through the years: RESECOM (LIFE+12 NAT/ES/000180), Natural Patrimony (RECEUSGL2021), REFUGIA (PID2021–129056OB-I00) and BIOTREND (TED2021–131513B-I00)., Peer reviewed

Estimates of population dynamics and risk of extinction are sensitive to both mean rates of annual change and also the variation in these rates caused by environmental stochasticity. The analytical machinery to incorporate the latter into estimates of long-term stochastic growth and quasi-extinction risk are well developed for count-based population data. However, analytical methods rarely account for the effects of observation error during the sampling process, which can inflate apparent stochasticity and thus alter estimates of population behavior. Here, we applied a Bayesian stochastic population model to estimate the growth rates and quasi-extinction risk of over 157 plant populations monitored through a collaborative science program in NE Spain, and calculated the effect of incorporating direct measures of the observation error into our estimates. We found that including the observation error into models reduced the estimated temporal variation of all populations, which in turn resulted in modest increases in estimated long-term growth rates but considerable reductions in quasi-extinction risk. In this study we show how adjusting sampling designs to the size, detectability and density of plant populations, and repeating surveys in one or more years substantially improves estimates of population growth and viability, thus contributing to guide a better conservation practice., This paper has been made possible by the support of many projects and >400 collaborators over time. In recent years it has been supported by projects VUBI (PROY_E35_24), BIOTREND (TED2021-131513B-I00), NATURAL PATRIMONY (RECEUSGL2021), and REFUGIA (PID2021-129056OB-I00). HMC was supported by a grant from the Spanish Ministry of Science (grant no. PRE2018-083868). DFD was supported by a grant from the US National Science Foundation no. 1753954., Peer reviewed

Although protected areas are designed to halt human pressures and ensure the persistence of species and habitats of interest, they may experience land cover changes as a result of recovery from pre-protection disturbances. In this study, a two-step methodology was used to analyse the long-term landscape change and its potential impact on biodiversity in a steep southern European National Park. First, we used the WSL Monoplotting Tool software in conjunction with 18 centennial / current ground-based image pairs distributed over a vertical range of 2000 m to examine the fate of 1269 patches of seven land cover types. The use of a three-dimensional model allowed us to estimate real changes in an abrupt terrain where 27 % of its extent is hidden for classical or newer aerial imagery, and to assess the effect of topographic factors such as altitude, slope and perimeter-to-area ratio of the patch. The results showed an overall trend towards more advanced successional stages, with forests being the absolute winners at the expense of more open land cover types such as grasslands. Cliffs, on the other hand, showed the least change over the last century. Then, we analyzed 1108 plant inventories taken in these land cover types to assess how landscape change has potentially affected richness and the presence of plants of conservation interest (endemic, threatened, or species at the northern or southern limit of their distribution). The results suggest an overall loss of plants of interest over the last century, and highlight the conservation value of rocky habitats as refugia. Our analysis provides insights into the consequences of secondary succession of protected areas on biodiversity, and recommend some specific actions for an efficient management towards the most singular and vulnerable plant species., This research was funded by CSIC (grant number RECEUSGL2021 - SGL2104002), Government of Aragon (VUBI, PROY_E35_24), the European Comission (eLTER PLUS grant INFRAIA-01-2018-2019, and LIFE4PYRENEES), MICIU (grants PID2021-129056OB-I00 and RED2022-134958-E), and LIFE-SIP PYRENEES4CLIMA., Peer reviewed