Using automated vegetation cover estimation from close-range photogrammetric point clouds to compare vegetation location properties in mountain terrain

In this paper we present a low-cost approach to mapping vegetation cover by means of high-resolution close-range terrestrial photogrammetry. A total of 249 clusters of nine 1 m2 plots each, arranged in a 3 × 3 grid, were set up on 18 summits in Mediterranean mountain regions and in the Alps to capture images for photogrammetric processing and in-situ vegetation cover estimates. This was done with a hand-held pole-mounted digital single-lens reflex (DSLR) camera. Low-growing vegetation was automatically segmented using high-resolution point clouds. For classifying vegetation we used a two-step semi-supervised Random Forest approach. First, we applied an expert-based rule set using the Excess Green index (ExG) to predefine non-vegetation and vegetation points. Second, we applied a Random Forest classifier to further enhance the classification of vegetation points using selected topographic parameters (elevation, slope, aspect, roughness, potential solar irradiation) and additional vegetation indices (Excess Green Minus Excess Red (ExGR) and the vegetation index VEG). For ground cover estimation the photogrammetric point clouds were meshed using Screened Poisson Reconstruction. The relative influence of the topographic parameters on the vegetation cover was determined with linear mixed-effects models (LMMs). Analysis of the LMMs revealed a high impact of elevation, aspect, solar irradiation, and standard deviation of slope. The presented approach goes beyond vegetation cover values based on conventional orthoimages and in-situ vegetation cover estimates from field surveys in that it is able to differentiate complete 3D surface areas, including overhangs, and can distinguish between vegetation-covered and other surfaces in an automated manner. The results of the Random Forest classification confirmed it as suitable for vegetation classification, but the relative feature importance values indicate that the classifier did not leverage the potential of the included topographic parameters. In contrast, our application of LMMs utilized the topographic parameters and was able to reveal dependencies in the two biomes, such as elevation and aspect, which were able to explain between 87% and 92.5% of variance

Primary succession and its driving variables – a sphere-spanning approach applied in proglacial areas in the upper Martell Valley (Eastern Italian Alps)

Climate change and the associated glacier retreat lead to considerable enlargement and alterations of the proglacial systems. The colonisation of plants in this ecosystem was found to be highly dependent on terrain age, initial site conditions and geomorphic disturbances. Although the explanatory variables are generally well understood, there is little knowledge on their collinearities and resulting influence on proglacial primary succession. To develop a sphere-spanning understanding of vegetation development, a more interdisciplinary approach was adopted. In the proglacial areas of Fürkeleferner, Zufallferner and Langenferner (Martell Valley, Eastern Italian Alps), in total 65 plots of 5×2 m were installed to perform the vegetation analysis on vegetation cover, species number and species composition. For each of those, 39 potential explanatory variables were collected, selected through an extensive literature review. To analyse and further avoid multicollinearities, 33 of the explanatory variables were clustered via principal component analysis (PCA) to five components. Subsequently, generalised additive models (GAMs) were used to analyse the potential explanatory factors of primary succession. The results showed that primary succession patterns were highly related to the first component (elevation and time), the second component (solar radiation), the third component (soil chemistry), the fifth component (soil physics) and landforms. In summary, the analysis of all explanatory variables together provides an overview of the most important influencing variables and their interactions; thus it provides a basis for the debate on future vegetation development in a changing climate.</p

Effects of climate and atmospheric nitrogen deposition on early to mid-term stage litter decomposition across biomes

Litter decomposition is a key process for carbon and nutrient cycling in terrestrial ecosystems and is mainly controlled by environmental conditions, substrate quantity and quality as well as microbial community abundance and composition. In particular, the effects of climate and atmospheric nitrogen (N) deposition on litter decomposition and its temporal dynamics are of significant importance, since their effects might change over the course of the decomposition process. Within the TeaComposition initiative, we incubated Green and Rooibos teas at 524 sites across nine biomes. We assessed how macroclimate and atmospheric inorganic N deposition under current and predicted scenarios (RCP 2.6, RCP 8.5) might affect litter mass loss measured after 3 and 12 months. Our study shows that the early to mid-term mass loss at the global scale was affected predominantly by litter quality (explaining 73% and 62% of the total variance after 3 and 12 months, respectively) followed by climate and N deposition. The effects of climate were not litter-specific and became increasingly significant as decomposition progressed, with MAP explaining 2% and MAT 4% of the variation after 12 months of incubation. The effect of N deposition was litter-specific, and significant only for 12-month decomposition of Rooibos tea at the global scale. However, in the temperate biome where atmospheric N deposition rates are relatively high, the 12-month mass loss of Green and Rooibos teas decreased significantly with increasing N deposition, explaining 9.5% and 1.1% of the variance, respectively. The expected changes in macroclimate and N deposition at the global scale by the end of this century are estimated to increase the 12-month mass loss of easily decomposable litter by 1.1-3.5% and of the more stable substrates by 3.8-10.6%, relative to current mass loss. In contrast, expected changes in atmospheric N deposition will decrease the mid-term mass loss of high-quality litter by 1.4-2.2% and that of low-quality litter by 0.9-1.5% in the temperate biome. Our results suggest that projected increases in N deposition may have the capacity to dampen the climate-driven increases in litter decomposition depending on the biome and decomposition stage of substrate

A Novel Testis-specific GTPase Serves as a Link to Proteasome Biogenesis: Functional Characterization of RhoS/RSA-14-44 in Spermatogenesis

We functionally characterized RhoS/RSA-14-44 as a new member of Rho GTPase subfamily in spermatogenesis, which provides a direct link between Rho family GTPase and the proteasome biogenesis

Global maps of soil temperature

Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2 m 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 8500 unique temperature 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

Global maps of soil temperature

Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2&nbsp;m 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\u20135 and 5\u201315&nbsp;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 temperature 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\ub0C (mean&nbsp;=&nbsp;3.0&nbsp;\ub1&nbsp;2.1\ub0C), with substantial variation across biomes and seasons. Over the year, soils in cold and/or dry biomes are substantially warmer (+3.6&nbsp;\ub1&nbsp;2.3\ub0C) than gridded air temperature, whereas soils in warm and humid environments are on average slightly cooler ( 120.7&nbsp;\ub1&nbsp;2.3\ub0C). 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

Effects of Climate and Atmospheric Nitrogen Deposition on Early to Mid-Term Stage Litter Decomposition Across Biomes

open263siWe acknowledge support by the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, funded by the German Research Foundation (FZT 118), Scientific Grant Agency VEGA(GrantNo.2/0101/18), as well as by the European Research Council under the European Union’s Horizon 2020 Research and Innovation Program (Grant Agreement No. 677232)Litter decomposition is a key process for carbon and nutrient cycling in terrestrial ecosystems and is mainly controlled by environmental conditions, substrate quantity and quality as well as microbial community abundance and composition. In particular, the effects of climate and atmospheric nitrogen (N) deposition on litter decomposition and its temporal dynamics are of significant importance, since their effects might change over the course of the decomposition process. Within the TeaComposition initiative, we incubated Green and Rooibos teas at 524 sites across nine biomes. We assessed how macroclimate and atmospheric inorganic N deposition under current and predicted scenarios (RCP 2.6, RCP 8.5) might affect litter mass loss measured after 3 and 12 months. Our study shows that the early to mid-term mass loss at the global scale was affected predominantly by litter quality (explaining 73% and 62% of the total variance after 3 and 12 months, respectively) followed by climate and N deposition. The effects of climate were not litter-specific and became increasingly significant as decomposition progressed, with MAP explaining 2% and MAT 4% of the variation after 12 months of incubation. The effect of N deposition was litter-specific, and significant only for 12-month decomposition of Rooibos tea at the global scale. However, in the temperate biome where atmospheric N deposition rates are relatively high, the 12-month mass loss of Green and Rooibos teas decreased significantly with increasing N deposition, explaining 9.5% and 1.1% of the variance, respectively. The expected changes in macroclimate and N deposition at the global scale by the end of this century are estimated to increase the 12-month mass loss of easily decomposable litter by 1.1-3.5% and of the more stable substrates by 3.8-10.6%, relative to current mass loss. In contrast, expected changes in atmospheric N deposition will decrease the mid-term mass loss of high-quality litter by 1.4-2.2% and that of low-quality litter by 0.9-1.5% in the temperate biome. Our results suggest that projected increases in N deposition may have the capacity to dampen the climate-driven increases in litter decomposition depending on the biome and decomposition stage of substrate.openKwon T.; Shibata H.; Kepfer-Rojas S.; Schmidt I.K.; Larsen K.S.; Beier C.; Berg B.; Verheyen K.; Lamarque J.-F.; Hagedorn F.; Eisenhauer N.; Djukic I.; Caliman A.; Paquette A.; Gutierrez-Giron A.; Petraglia A.; Augustaitis A.; Saillard A.; Ruiz-Fernandez A.C.; Sousa A.I.; Lillebo A.I.; Da Rocha Gripp A.; Lamprecht A.; Bohner A.; Francez A.-J.; Malyshev A.; Andric A.; Stanisci A.; Zolles A.; Avila A.; Virkkala A.-M.; Probst A.; Ouin A.; Khuroo A.A.; Verstraeten A.; Stefanski A.; Gaxiola A.; Muys B.; Gozalo B.; Ahrends B.; Yang B.; Erschbamer B.; Rodriguez Ortiz C.E.; Christiansen C.T.; Meredieu C.; Mony C.; Nock C.; Wang C.-P.; Baum C.; Rixen C.; Delire C.; Piscart C.; Andrews C.; Rebmann C.; Branquinho C.; Jan D.; Wundram D.; Vujanovic D.; Adair E.C.; Ordonez-Regil E.; Crawford E.R.; Tropina E.F.; Hornung E.; Groner E.; Lucot E.; Gacia E.; Levesque E.; Benedito E.; Davydov E.A.; Bolzan F.P.; Maestre F.T.; Maunoury-Danger F.; Kitz F.; Hofhansl F.; Hofhansl G.; De Almeida Lobo F.; Souza F.L.; Zehetner F.; Koffi F.K.; Wohlfahrt G.; Certini G.; Pinha G.D.; Gonzlez G.; Canut G.; Pauli H.; Bahamonde H.A.; Feldhaar H.; Jger H.; Serrano H.C.; Verheyden H.; Bruelheide H.; Meesenburg H.; Jungkunst H.; Jactel H.; Kurokawa H.; Yesilonis I.; Melece I.; Van Halder I.; Quiros I.G.; Fekete I.; Ostonen I.; Borovsk J.; Roales J.; Shoqeir J.H.; Jean-Christophe Lata J.; Probst J.-L.; Vijayanathan J.; Dolezal J.; Sanchez-Cabeza J.-A.; Merlet J.; Loehr J.; Von Oppen J.; Loffler J.; Benito Alonso J.L.; Cardoso-Mohedano J.-G.; Penuelas J.; Morina J.C.; Quinde J.D.; Jimnez J.J.; Alatalo J.M.; Seeber J.; Kemppinen J.; Stadler J.; Kriiska K.; Van Den Meersche K.; Fukuzawa K.; Szlavecz K.; Juhos K.; Gerhtov K.; Lajtha K.; Jennings K.; Jennings J.; Ecology P.; Hoshizaki K.; Green K.; Steinbauer K.; Pazianoto L.; Dienstbach L.; Yahdjian L.; Williams L.J.; Brigham L.; Hanna L.; Hanna H.; Rustad L.; Morillas L.; Silva Carneiro L.; Di Martino L.; Villar L.; Fernandes Tavares L.A.; Morley M.; Winkler M.; Lebouvier M.; Tomaselli M.; Schaub M.; Glushkova M.; Torres M.G.A.; De Graaff M.-A.; Pons M.-N.; Bauters M.; Mazn M.; Frenzel M.; Wagner M.; Didion M.; Hamid M.; Lopes M.; Apple M.; Weih M.; Mojses M.; Gualmini M.; Vadeboncoeur M.; Bierbaumer M.; Danger M.; Scherer-Lorenzen M.; Ruek M.; Isabellon M.; Di Musciano M.; Carbognani M.; Zhiyanski M.; Puca M.; Barna M.; Ataka M.; Luoto M.; H. Alsafaran M.; Barsoum N.; Tokuchi N.; Korboulewsky N.; Lecomte N.; Filippova N.; Hlzel N.; Ferlian O.; Romero O.; Pinto-Jr O.; Peri P.; Dan Turtureanu P.; Haase P.; Macreadie P.; Reich P.B.; Petk P.; Choler P.; Marmonier P.; Ponette Q.; Dettogni Guariento R.; Canessa R.; Kiese R.; Hewitt R.; Weigel R.; Kanka R.; Cazzolla Gatti R.; Martins R.L.; Ogaya R.; Georges R.; Gaviln R.G.; Wittlinger S.; Puijalon S.; Suzuki S.; Martin S.; Anja S.; Gogo S.; Schueler S.; Drollinger S.; Mereu S.; Wipf S.; Trevathan-Tackett S.; Stoll S.; Lfgren S.; Trogisch S.; Seitz S.; Glatzel S.; Venn S.; Dousset S.; Mori T.; Sato T.; Hishi T.; Nakaji T.; Jean-Paul T.; Camboulive T.; Spiegelberger T.; Scholten T.; Mozdzer T.J.; Kleinebecker T.; Runk T.; Ramaswiela T.; Hiura T.; Enoki T.; Ursu T.-M.; Di Cella U.M.; Hamer U.; Klaus V.; Di Cecco V.; Rego V.; Fontana V.; Piscov V.; Bretagnolle V.; Maire V.; Farjalla V.; Pascal V.; Zhou W.; Luo W.; Parker W.; Parker P.; Kominam Y.; Kotrocz Z.; Utsumi Y.Kwon T.; Shibata H.; Kepfer-Rojas S.; Schmidt I.K.; Larsen K.S.; Beier C.; Berg B.; Verheyen K.; Lamarque J.-F.; Hagedorn F.; Eisenhauer N.; Djukic I.; Caliman A.; Paquette A.; Gutierrez-Giron A.; Petraglia A.; Augustaitis A.; Saillard A.; Ruiz-Fernandez A.C.; Sousa A.I.; Lillebo A.I.; Da Rocha Gripp A.; Lamprecht A.; Bohner A.; Francez A.-J.; Malyshev A.; Andric A.; Stanisci A.; Zolles A.; Avila A.; Virkkala A.-M.; Probst A.; Ouin A.; Khuroo A.A.; Verstraeten A.; Stefanski A.; Gaxiola A.; Muys B.; Gozalo B.; Ahrends B.; Yang B.; Erschbamer B.; Rodriguez Ortiz C.E.; Christiansen C.T.; Meredieu C.; Mony C.; Nock C.; Wang C.-P.; Baum C.; Rixen C.; Delire C.; Piscart C.; Andrews C.; Rebmann C.; Branquinho C.; Jan D.; Wundram D.; Vujanovic D.; Adair E.C.; Ordonez-Regil E.; Crawford E.R.; Tropina E.F.; Hornung E.; Groner E.; Lucot E.; Gacia E.; Levesque E.; Benedito E.; Davydov E.A.; Bolzan F.P.; Maestre F.T.; Maunoury-Danger F.; Kitz F.; Hofhansl F.; Hofhansl G.; De Almeida Lobo F.; Souza F.L.; Zehetner F.; Koffi F.K.; Wohlfahrt G.; Certini G.; Pinha G.D.; Gonzlez G.; Canut G.; Pauli H.; Bahamonde H.A.; Feldhaar H.; Jger H.; Serrano H.C.; Verheyden H.; Bruelheide H.; Meesenburg H.; Jungkunst H.; Jactel H.; Kurokawa H.; Yesilonis I.; Melece I.; Van Halder I.; Quiros I.G.; Fekete I.; Ostonen I.; Borovsk J.; Roales J.; Shoqeir J.H.; Jean-Christophe Lata J.; Probst J.-L.; Vijayanathan J.; Dolezal J.; Sanchez-Cabeza J.-A.; Merlet J.; Loehr J.; Von Oppen J.; Loffler J.; Benito Alonso J.L.; Cardoso-Mohedano J.-G.; Penuelas J.; Morina J.C.; Quinde J.D.; Jimnez J.J.; Alatalo J.M.; Seeber J.; Kemppinen J.; Stadler J.; Kriiska K.; Van Den Meersche K.; Fukuzawa K.; Szlavecz K.; Juhos K.; Gerhtov K.; Lajtha K.; Jennings K.; Jennings J.; Ecology P.; Hoshizaki K.; Green K.; Steinbauer K.; Pazianoto L.; Dienstbach L.; Yahdjian L.; Williams L.J.; Brigham L.; Hanna L.; Hanna H.; Rustad L.; Morillas L.; Silva Carneiro L.; Di Martino L.; Villar L.; Fernandes Tavares L.A.; Morley M.; Winkler M.; Lebouvier M.; Tomaselli M.; Schaub M.; Glushkova M.; Torres M.G.A.; De Graaff M.-A.; Pons M.-N.; Bauters M.; Mazn M.; Frenzel M.; Wagner M.; Didion M.; Hamid M.; Lopes M.; Apple M.; Weih M.; Mojses M.; Gualmini M.; Vadeboncoeur M.; Bierbaumer M.; Danger M.; Scherer-Lorenzen M.; Ruek M.; Isabellon M.; Di Musciano M.; Carbognani M.; Zhiyanski M.; Puca M.; Barna M.; Ataka M.; Luoto M.; H. Alsafaran M.; Barsoum N.; Tokuchi N.; Korboulewsky N.; Lecomte N.; Filippova N.; Hlzel N.; Ferlian O.; Romero O.; Pinto-Jr O.; Peri P.; Dan Turtureanu P.; Haase P.; Macreadie P.; Reich P.B.; Petk P.; Choler P.; Marmonier P.; Ponette Q.; Dettogni Guariento R.; Canessa R.; Kiese R.; Hewitt R.; Weigel R.; Kanka R.; Cazzolla Gatti R.; Martins R.L.; Ogaya R.; Georges R.; Gaviln R.G.; Wittlinger S.; Puijalon S.; Suzuki S.; Martin S.; Anja S.; Gogo S.; Schueler S.; Drollinger S.; Mereu S.; Wipf S.; Trevathan-Tackett S.; Stoll S.; Lfgren S.; Trogisch S.; Seitz S.; Glatzel S.; Venn S.; Dousset S.; Mori T.; Sato T.; Hishi T.; Nakaji T.; Jean-Paul T.; Camboulive T.; Spiegelberger T.; Scholten T.; Mozdzer T.J.; Kleinebecker T.; Runk T.; Ramaswiela T.; Hiura T.; Enoki T.; Ursu T.-M.; Di Cella U.M.; Hamer U.; Klaus V.; Di Cecco V.; Rego V.; Fontana V.; Piscov V.; Bretagnolle V.; Maire V.; Farjalla V.; Pascal V.; Zhou W.; Luo W.; Parker W.; Parker P.; Kominam Y.; Kotrocz Z.; Utsumi Y

Global maps of soil temperature.

Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2 m 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-km &lt;sup&gt;2&lt;/sup&gt; 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-km &lt;sup&gt;2&lt;/sup&gt; pixels (summarized from 8519 unique temperature 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

Global maps of soil temperature

Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2 m 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 temperature sensors) across all the world\u27s 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

Global maps of soil temperature

Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2 m 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 8500 unique temperature 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