Articles | Volume 15, issue 4
https://doi.org/10.5194/essd-15-1871-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-15-1871-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
27 Apr 2023
Data description paper |
| 27 Apr 2023
| 27 Apr 2023
A remote-sensing-based dataset to characterize the ecosystem functioning and functional diversity in the Biosphere Reserve of the Sierra Nevada (southeastern Spain)
Andalusian Center for the Assessment and Monitoring of Global Change, University of Almería, 04120 Almería, Spain
Department of Biology and Geology, University of Almería,
04120 Almería, Spain
iecolab, Andalusian Institute for Earth System Research (IISTA-CEAMA), University of Granada, Av. Mediterráneo s/n, 18006 Granada, Spain
Javier Cabello
Andalusian Center for the Assessment and Monitoring of Global Change, University of Almería, 04120 Almería, Spain
Department of Biology and Geology, University of Almería,
04120 Almería, Spain
Andrés Reyes
Andalusian Center for the Assessment and Monitoring of Global Change, University of Almería, 04120 Almería, Spain
Emilio Guirado
Andalusian Center for the Assessment and Monitoring of Global Change, University of Almería, 04120 Almería, Spain
Multidisciplinary Institute for Environment Studies “Ramon
Margalef”, University of Alicante, 03690 Alicante, Spain
Julio Peñas
Andalusian Center for the Assessment and Monitoring of Global Change, University of Almería, 04120 Almería, Spain
Department of Botany, University of Granada, Av. de Fuentenueva
s/n, 18071 Granada, Spain
Antonio J. Pérez-Luque
iecolab, Andalusian Institute for Earth System Research (IISTA-CEAMA), University of Granada, Av. Mediterráneo s/n, 18006 Granada, Spain
Terrestrial Ecology Research Group, Department of Ecology, Faculty
of Science, University of Granada, Av. Fuentenueva s/n, 18071 Granada, Spain
Domingo Alcaraz-Segura
Andalusian Center for the Assessment and Monitoring of Global Change, University of Almería, 04120 Almería, Spain
Department of Botany, University of Granada, Av. de Fuentenueva
s/n, 18071 Granada, Spain
iecolab, Andalusian Institute for Earth System Research (IISTA-CEAMA), University of Granada, Av. Mediterráneo s/n, 18006 Granada, Spain
Related authors
Beatriz P. Cazorla, Javier Cabello, Andrés Reyes, Emilio Guirado, Julio Peñas, Antonio J. Pérez-Luque, and Domingo Alcaraz-Segura
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2019-198, https://doi.org/10.5194/essd-2019-198, 2020
Revised manuscript not accepted
Short summary
Short summary
Our dataset characterizes the spatial patterns and temporal dynamics of ecosystem functioning and functional diversity in Sierra Nevada (Spain) from time-series of satellite images of vegetation greenness from 2001 to 2018. The dataset brings to scientists, managers and citizens the first characterization of the functional diversity at ecosystem level developed in a Mediterranean biodiversity hotspot, useful to improve the understanding, monitoring and management of ecosystem processes.
Rohaifa Khaldi, Domingo Alcaraz-Segura, Emilio Guirado, Yassir Benhammou, Abdellatif El Afia, Francisco Herrera, and Siham Tabik
Earth Syst. Sci. Data, 14, 1377–1411, https://doi.org/10.5194/essd-14-1377-2022, https://doi.org/10.5194/essd-14-1377-2022, 2022
Short summary
Short summary
This dataset with millions of 22-year time series for seven spectral bands was built by merging Terra and Aqua satellite data and annotated for 29 LULC classes by spatial–temporal agreement across 15 global LULC products. The mean F1 score was 96 % at the coarsest classification level and 87 % at the finest one. The dataset is born to develop and evaluate machine learning models to perform global LULC mapping given the disagreement between current global LULC products.
Beatriz P. Cazorla, Javier Cabello, Andrés Reyes, Emilio Guirado, Julio Peñas, Antonio J. Pérez-Luque, and Domingo Alcaraz-Segura
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2019-198, https://doi.org/10.5194/essd-2019-198, 2020
Revised manuscript not accepted
Short summary
Short summary
Our dataset characterizes the spatial patterns and temporal dynamics of ecosystem functioning and functional diversity in Sierra Nevada (Spain) from time-series of satellite images of vegetation greenness from 2001 to 2018. The dataset brings to scientists, managers and citizens the first characterization of the functional diversity at ecosystem level developed in a Mediterranean biodiversity hotspot, useful to improve the understanding, monitoring and management of ecosystem processes.
M. A. Aguilar, F. J. Aguilar, A. García Lorca, E. Guirado, M. Betlej, P. Cichon, A. Nemmaoui, A. Vallario, and C. Parente
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B7, 145–152, https://doi.org/10.5194/isprs-archives-XLI-B7-145-2016, https://doi.org/10.5194/isprs-archives-XLI-B7-145-2016, 2016
Related subject area
Domain: ESSD – Land | Subject: Biogeosciences and biodiversity
Spatiotemporally consistent global dataset of the GIMMS Normalized Difference Vegetation Index (PKU GIMMS NDVI) from 1982 to 2022
CLIM4OMICS: a geospatially comprehensive climate and multi-OMICS database for maize phenotype predictability in the United States and Canada
Quantifying exchangeable base cations in permafrost: a reserve of nutrients about to thaw
Routine monitoring of western Lake Erie to track water quality changes associated with cyanobacterial harmful algal blooms
The Portuguese Large Wildfire Spread database (PT-FireSprd)
Thirty-meter map of young forest age in China
GRiMeDB: the Global River Methane Database of concentrations and fluxes
A gridded dataset of a leaf-age-dependent leaf area index seasonality product over tropical and subtropical evergreen broadleaved forests
Fire weather index data under historical and shared socioeconomic pathway projections in the 6th phase of the Coupled Model Intercomparison Project from 1850 to 2100
A global long-term, high-resolution satellite radar backscatter data record (1992–2022+): merging C-band ERS/ASCAT and Ku-band QSCAT
A global database on holdover time of lightning-ignited wildfires
National CO2 budgets (2015–2020) inferred from atmospheric CO2 observations in support of the global stocktake
Spatiotemporally consistent global dataset of the GIMMS Leaf Area Index (GIMMS LAI4g) from 1982 to 2020
Mammals in the Chornobyl Exclusion Zone's Red Forest: a motion-activated camera trap study
Maps with 1 km resolution reveal increases in above- and belowground forest biomass carbon pools in China over the past 20 years
AnisoVeg: anisotropy and nadir-normalized MODIS multi-angle implementation atmospheric correction (MAIAC) datasets for satellite vegetation studies in South America
TiP-Leaf: a dataset of leaf traits across vegetation types on the Tibetan Plateau
Forest structure and individual tree inventories of northeastern Siberia along climatic gradients
Global climate-related predictors at kilometer resolution for the past and future
A daily and 500 m coupled evapotranspiration and gross primary production product across China during 2000–2020
Global land surface 250 m 8 d fraction of absorbed photosynthetically active radiation (FAPAR) product from 2000 to 2021
Rates and timing of chlorophyll-a increases and related environmental variables in global temperate and cold-temperate lakes
Harmonized gap-filled datasets from 20 urban flux tower sites
Holocene spatiotemporal millet agricultural patterns in northern China: a dataset of archaeobotanical macroremains
The biogeography of relative abundance of soil fungi versus bacteria in surface topsoil
Airborne SnowSAR data at X and Ku bands over boreal forest, alpine and tundra snow cover
The Landscape Fire Scars Database: mapping historical burned area and fire severity in Chile
Aridec: an open database of litter mass loss from aridlands worldwide with recommendations on suitable model applications
LegacyPollen 1.0: a taxonomically harmonized global late Quaternary pollen dataset of 2831 records with standardized chronologies
Muyi Li, Sen Cao, Zaichun Zhu, Zhe Wang, Ranga B. Myneni, and Shilong Piao
Earth Syst. Sci. Data, 15, 4181–4203, https://doi.org/10.5194/essd-15-4181-2023, https://doi.org/10.5194/essd-15-4181-2023, 2023
Short summary
Short summary
Long-term global Normalized Difference Vegetation Index (NDVI) products support the understanding of changes in vegetation under environmental changes. This study generates a consistent global NDVI product (PKU GIMMS NDVI) from 1982–2022 that eliminates the issue of orbital drift and sensor degradation in Advanced Very High Resolution Radiometer (AVHRR) data. More accurate than its predecessor (GIMMS NDVI3g), it shows high temporal consistency with MODIS NDVI in describing vegetation trends.
Parisa Sarzaeim, Francisco Muñoz-Arriola, Diego Jarquin, Hasnat Aslam, and Natalia De Leon Gatti
Earth Syst. Sci. Data, 15, 3963–3990, https://doi.org/10.5194/essd-15-3963-2023, https://doi.org/10.5194/essd-15-3963-2023, 2023
Short summary
Short summary
A genomic, phenomic, and climate database for maize phenotype predictability in the US and Canada is introduced. The database encompasses climate from multiple sources and OMICS from the Genomes to Fields initiative (G2F) data from 2014 to 2021, including codes for input data quality and consistency controls. Earth system modelers and breeders can use CLIM4OMICS since it interconnects the climate and biological system sciences. CLIM4OMICS is designed to foster phenotype predictability.
Elisabeth Mauclet, Maëlle Villani, Arthur Monhonval, Catherine Hirst, Edward A. G. Schuur, and Sophie Opfergelt
Earth Syst. Sci. Data, 15, 3891–3904, https://doi.org/10.5194/essd-15-3891-2023, https://doi.org/10.5194/essd-15-3891-2023, 2023
Short summary
Short summary
Permafrost ecosystems are limited in nutrients for vegetation development and constrain the biological activity to the active layer. Upon Arctic warming, permafrost degradation exposes organic and mineral soil material that may directly influence the capacity of the soil to retain key nutrients for vegetation growth and development. Here, we demonstrate that the average total exchangeable nutrient density (Ca, K, Mg, and Na) is more than 2 times higher in the permafrost than in the active layer.
Anna G. Boegehold, Ashley M. Burtner, Andrew C. Camilleri, Glenn Carter, Paul DenUyl, David Fanslow, Deanna Fyffe Semenyuk, Casey M. Godwin, Duane Gossiaux, Thomas H. Johengen, Holly Kelchner, Christine Kitchens, Lacey A. Mason, Kelly McCabe, Danna Palladino, Dack Stuart, Henry Vanderploeg, and Reagan Errera
Earth Syst. Sci. Data, 15, 3853–3868, https://doi.org/10.5194/essd-15-3853-2023, https://doi.org/10.5194/essd-15-3853-2023, 2023
Short summary
Short summary
Western Lake Erie suffers from cyanobacterial harmful algal blooms (HABs) despite decades of international management efforts. In response, the US National Oceanic and Atmospheric Administration (NOAA) Great Lakes Environmental Research Laboratory (GLERL) and the Cooperative Institute for Great Lakes Research (CIGLR) created an annual sampling program to detect, monitor, assess, and predict HABs. Here we describe the data collected from this monitoring program from 2012 to 2021.
Akli Benali, Nuno Guiomar, Hugo Gonçalves, Bernardo Mota, Fábio Silva, Paulo M. Fernandes, Carlos Mota, Alexandre Penha, João Santos, José M. C. Pereira, and Ana C. L. Sá
Earth Syst. Sci. Data, 15, 3791–3818, https://doi.org/10.5194/essd-15-3791-2023, https://doi.org/10.5194/essd-15-3791-2023, 2023
Short summary
Short summary
We reconstructed the spread of 80 large wildfires that burned recently in Portugal and calculated metrics that describe how wildfires behave, such as rate of spread, growth rate, and energy released. We describe the fire behaviour distribution using six percentile intervals that can be easily communicated to both research and management communities. The database will help improve our current knowledge on wildfire behaviour and support better decision making.
Yuelong Xiao, Qunming Wang, Xiaohua Tong, and Peter M. Atkinson
Earth Syst. Sci. Data, 15, 3365–3386, https://doi.org/10.5194/essd-15-3365-2023, https://doi.org/10.5194/essd-15-3365-2023, 2023
Short summary
Short summary
Forest age is closely related to forest production, carbon cycles, and other ecosystem services. Existing stand age products in China derived from remote-sensing images are of a coarse spatial resolution and are not suitable for applications at the regional scale. Here, we mapped young forest ages across China at an unprecedented fine spatial resolution of 30 m. The overall accuracy (OA) of the generated map of young forest stand ages across China was 90.28 %.
Emily H. Stanley, Luke C. Loken, Nora J. Casson, Samantha K. Oliver, Ryan A. Sponseller, Marcus B. Wallin, Liwei Zhang, and Gerard Rocher-Ros
Earth Syst. Sci. Data, 15, 2879–2926, https://doi.org/10.5194/essd-15-2879-2023, https://doi.org/10.5194/essd-15-2879-2023, 2023
Short summary
Short summary
The Global River Methane Database (GRiMeDB) presents CH4 concentrations and fluxes for flowing waters and concurrent measures of CO2, N2O, and several physicochemical variables, plus information about sample locations and methods used to measure gas fluxes. GRiMeDB is intended to increase opportunities to understand variation in fluvial CH4, test hypotheses related to greenhouse gas dynamics, and reduce uncertainty in future estimates of gas emissions from world streams and rivers.
Xueqin Yang, Xiuzhi Chen, Jiashun Ren, Wenping Yuan, Liyang Liu, Juxiu Liu, Dexiang Chen, Yihua Xiao, Qinghai Song, Yanjun Du, Shengbiao Wu, Lei Fan, Xiaoai Dai, Yunpeng Wang, and Yongxian Su
Earth Syst. Sci. Data, 15, 2601–2622, https://doi.org/10.5194/essd-15-2601-2023, https://doi.org/10.5194/essd-15-2601-2023, 2023
Short summary
Short summary
We developed the first time-mapped, continental-scale gridded dataset of monthly leaf area index (LAI) in three leaf age cohorts (i.e., young, mature, and old) from 2001–2018 data (referred to as Lad-LAI). The seasonality of three LAI cohorts from the new Lad-LAI product agrees well at eight sites with very fine-scale collections of monthly LAI. The proposed satellite-based approaches can provide references for mapping finer spatiotemporal-resolution LAI products with different leaf age cohorts.
Yann Quilcaille, Fulden Batibeniz, Andreia F. S. Ribeiro, Ryan S. Padrón, and Sonia I. Seneviratne
Earth Syst. Sci. Data, 15, 2153–2177, https://doi.org/10.5194/essd-15-2153-2023, https://doi.org/10.5194/essd-15-2153-2023, 2023
Short summary
Short summary
We present a new database of four annual fire weather indicators over 1850–2100 and over all land areas. In a 3°C warmer world with respect to preindustrial times, the mean fire weather would increase on average by at least 66% in both intensity and duration and even triple for 1-in-10-year events. The dataset is a freely available resource for fire danger studies and beyond, highlighting that the best course of action would require limiting global warming as much as possible.
Shengli Tao, Zurui Ao, Jean-Pierre Wigneron, Sassan Saatchi, Philippe Ciais, Jérôme Chave, Thuy Le Toan, Pierre-Louis Frison, Xiaomei Hu, Chi Chen, Lei Fan, Mengjia Wang, Jiangling Zhu, Xia Zhao, Xiaojun Li, Xiangzhuo Liu, Yanjun Su, Tianyu Hu, Qinghua Guo, Zhiheng Wang, Zhiyao Tang, Yi Y. Liu, and Jingyun Fang
Earth Syst. Sci. Data, 15, 1577–1596, https://doi.org/10.5194/essd-15-1577-2023, https://doi.org/10.5194/essd-15-1577-2023, 2023
Short summary
Short summary
We provide the first long-term (since 1992), high-resolution (8.9 km) satellite radar backscatter data set (LHScat) with a C-band (5.3 GHz) signal dynamic for global lands. LHScat was created by fusing signals from ERS (1992–2001; C-band), QSCAT (1999–2009; Ku-band), and ASCAT (since 2007; C-band). LHScat has been validated against independent ERS-2 signals. It could be used in a variety of studies, such as vegetation monitoring and hydrological modelling.
Jose V. Moris, Pedro Álvarez-Álvarez, Marco Conedera, Annalie Dorph, Thomas D. Hessilt, Hugh G. P. Hunt, Renata Libonati, Lucas S. Menezes, Mortimer M. Müller, Francisco J. Pérez-Invernón, Gianni B. Pezzatti, Nicolau Pineda, Rebecca C. Scholten, Sander Veraverbeke, B. Mike Wotton, and Davide Ascoli
Earth Syst. Sci. Data, 15, 1151–1163, https://doi.org/10.5194/essd-15-1151-2023, https://doi.org/10.5194/essd-15-1151-2023, 2023
Short summary
Short summary
This work describes a database on holdover times of lightning-ignited wildfires (LIWs). Holdover time is defined as the time between lightning-induced fire ignition and fire detection. The database contains 42 datasets built with data on more than 152 375 LIWs from 13 countries in five continents from 1921 to 2020. This database is the first freely-available, harmonized and ready-to-use global source of holdover time data, which may be used to investigate LIWs and model the holdover phenomenon.
Brendan Byrne, David F. Baker, Sourish Basu, Michael Bertolacci, Kevin W. Bowman, Dustin Carroll, Abhishek Chatterjee, Frédéric Chevallier, Philippe Ciais, Noel Cressie, David Crisp, Sean Crowell, Feng Deng, Zhu Deng, Nicholas M. Deutscher, Manvendra K. Dubey, Sha Feng, Omaira E. García, David W. T. Griffith, Benedikt Herkommer, Lei Hu, Andrew R. Jacobson, Rajesh Janardanan, Sujong Jeong, Matthew S. Johnson, Dylan B. A. Jones, Rigel Kivi, Junjie Liu, Zhiqiang Liu, Shamil Maksyutov, John B. Miller, Scot M. Miller, Isamu Morino, Justus Notholt, Tomohiro Oda, Christopher W. O'Dell, Young-Suk Oh, Hirofumi Ohyama, Prabir K. Patra, Hélène Peiro, Christof Petri, Sajeev Philip, David F. Pollard, Benjamin Poulter, Marine Remaud, Andrew Schuh, Mahesh K. Sha, Kei Shiomi, Kimberly Strong, Colm Sweeney, Yao Té, Hanqin Tian, Voltaire A. Velazco, Mihalis Vrekoussis, Thorsten Warneke, John R. Worden, Debra Wunch, Yuanzhi Yao, Jeongmin Yun, Andrew Zammit-Mangion, and Ning Zeng
Earth Syst. Sci. Data, 15, 963–1004, https://doi.org/10.5194/essd-15-963-2023, https://doi.org/10.5194/essd-15-963-2023, 2023
Short summary
Short summary
Changes in the carbon stocks of terrestrial ecosystems result in emissions and removals of CO2. These can be driven by anthropogenic activities (e.g., deforestation), natural processes (e.g., fires) or in response to rising CO2 (e.g., CO2 fertilization). This paper describes a dataset of CO2 emissions and removals derived from atmospheric CO2 observations. This pilot dataset informs current capabilities and future developments towards top-down monitoring and verification systems.
Sen Cao, Muyi Li, Zaichun Zhu, Junjun Zha, Weiqing Zhao, Zeyu Duanmu, Jiana Chen, Yaoyao Zheng, and Yue Chen
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-68, https://doi.org/10.5194/essd-2023-68, 2023
Revised manuscript accepted for ESSD
Short summary
Short summary
The long-term global LAI products are critical supports to characterize vegetation dynamics under environmental changes. This study presents an updated GIMMS LAI product (GIMMS LAI4g; 1982−2020) based on PKU GIMMS NDVI and massive Landsat LAI samples. With higher accuracy than other LAI products, GIMMS LAI4g removes the effects of orbital drift and sensor degradation in AVHRR data. It also presents a better temporal consistency before and after 2000 and a more reasonable global vegetation trend.
Nicholas A. Beresford, Sergii Gashchak, Michael D. Wood, and Catherine L. Barnett
Earth Syst. Sci. Data, 15, 911–920, https://doi.org/10.5194/essd-15-911-2023, https://doi.org/10.5194/essd-15-911-2023, 2023
Short summary
Short summary
Camera traps were established in a highly contaminated area of the Chornobyl Exclusion Zone (CEZ) to capture images of mammals. Over 1 year, 14 mammal species were recorded. The number of species observed did not vary with estimated radiation exposure. The data will be of value from the perspectives of effects of radiation on wildlife and also rewilding in this large, abandoned area. They may also have value in future studies investigating impacts of recent Russian military action in the CEZ.
Yongzhe Chen, Xiaoming Feng, Bojie Fu, Haozhi Ma, Constantin M. Zohner, Thomas W. Crowther, Yuanyuan Huang, Xutong Wu, and Fangli Wei
Earth Syst. Sci. Data, 15, 897–910, https://doi.org/10.5194/essd-15-897-2023, https://doi.org/10.5194/essd-15-897-2023, 2023
Short summary
Short summary
This study presented a long-term (2002–2021) above- and belowground biomass dataset for woody vegetation in China at 1 km resolution. It was produced by combining various types of remote sensing observations with adequate plot measurements. Over 2002–2021, China’s woody biomass increased at a high rate, especially in the central and southern parts. This dataset can be applied to evaluate forest carbon sinks across China and the efficiency of ecological restoration programs in China.
Ricardo Dalagnol, Lênio Soares Galvão, Fabien Hubert Wagner, Yhasmin Mendes de Moura, Nathan Gonçalves, Yujie Wang, Alexei Lyapustin, Yan Yang, Sassan Saatchi, and Luiz Eduardo Oliveira Cruz Aragão
Earth Syst. Sci. Data, 15, 345–358, https://doi.org/10.5194/essd-15-345-2023, https://doi.org/10.5194/essd-15-345-2023, 2023
Short summary
Short summary
The AnisoVeg dataset brings 22 years of monthly satellite data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor for South America at 1 km resolution aimed at vegetation applications. It has nadir-normalized data, which is the most traditional approach to correct satellite data but also unique anisotropy data with strong biophysical meaning, explaining 55 % of Amazon forest height. We expect this dataset to help large-scale estimates of vegetation biomass and carbon.
Yili Jin, Haoyan Wang, Jie Xia, Jian Ni, Kai Li, Ying Hou, Jing Hu, Linfeng Wei, Kai Wu, Haojun Xia, and Borui Zhou
Earth Syst. Sci. Data, 15, 25–39, https://doi.org/10.5194/essd-15-25-2023, https://doi.org/10.5194/essd-15-25-2023, 2023
Short summary
Short summary
The TiP-Leaf dataset was compiled from direct field measurements and included 11 leaf traits from 468 species of 1692 individuals, covering a great proportion of species and vegetation types on the highest plateau in the world. This work is the first plant trait dataset that represents all of the alpine vegetation on the TP, which is not only an update of the Chinese plant trait database, but also a great contribution to the global trait database.
Timon Miesner, Ulrike Herzschuh, Luidmila A. Pestryakova, Mareike Wieczorek, Evgenii S. Zakharov, Alexei I. Kolmogorov, Paraskovya V. Davydova, and Stefan Kruse
Earth Syst. Sci. Data, 14, 5695–5716, https://doi.org/10.5194/essd-14-5695-2022, https://doi.org/10.5194/essd-14-5695-2022, 2022
Short summary
Short summary
We present data which were collected on expeditions to the northeast of the Russian Federation. One table describes the 226 locations we visited during those expeditions, and the other describes 40 289 trees which we recorded at these locations. We found out that important information on the forest cannot be predicted precisely from satellites. Thus, for anyone interested in distant forests, it is important to go to there and take measurements or use data (as presented here).
Philipp Brun, Niklaus E. Zimmermann, Chantal Hari, Loïc Pellissier, and Dirk Nikolaus Karger
Earth Syst. Sci. Data, 14, 5573–5603, https://doi.org/10.5194/essd-14-5573-2022, https://doi.org/10.5194/essd-14-5573-2022, 2022
Short summary
Short summary
Using mechanistic downscaling, we developed CHELSA-BIOCLIM+, a set of 15 biologically relevant, climate-related variables at unprecedented resolution, as a basis for environmental analyses. It includes monthly time series for 38+ years and 30-year averages for three future periods and three emission scenarios. Estimates matched well with station measurements, but few biases existed. The data allow for detailed assessments of climate-change impact on ecosystems and their services to societies.
Shaoyang He, Yongqiang Zhang, Ning Ma, Jing Tian, Dongdong Kong, and Changming Liu
Earth Syst. Sci. Data, 14, 5463–5488, https://doi.org/10.5194/essd-14-5463-2022, https://doi.org/10.5194/essd-14-5463-2022, 2022
Short summary
Short summary
This study developed a daily, 500 m evapotranspiration and gross primary production product (PML-V2(China)) using a locally calibrated water–carbon coupled model, PML-V2, which was well calibrated against observations at 26 flux sites across nine land cover types. PML-V2 (China) performs satisfactorily in the plot- and basin-scale evaluations compared with other mainstream products. It improved intra-annual ET and GPP dynamics, particularly in the cropland ecosystem.
Han Ma, Shunlin Liang, Changhao Xiong, Qian Wang, Aolin Jia, and Bing Li
Earth Syst. Sci. Data, 14, 5333–5347, https://doi.org/10.5194/essd-14-5333-2022, https://doi.org/10.5194/essd-14-5333-2022, 2022
Short summary
Short summary
The fraction of absorbed photosynthetically active radiation (FAPAR) is one of the essential climate variables. This study generated a global land surface FAPAR product with a 250 m resolution based on a deep learning model that takes advantage of the existing FAPAR products and MODIS time series of observation information. Direct validation and intercomparison revealed that our product better meets user requirements and has a greater spatiotemporal continuity than other existing products.
Hannah Adams, Jane Ye, Bhaleka D. Persaud, Stephanie Slowinski, Homa Kheyrollah Pour, and Philippe Van Cappellen
Earth Syst. Sci. Data, 14, 5139–5156, https://doi.org/10.5194/essd-14-5139-2022, https://doi.org/10.5194/essd-14-5139-2022, 2022
Short summary
Short summary
Climate warming and land-use changes are altering the environmental factors that control the algal
productivityin lakes. To predict how environmental factors like nutrient concentrations, ice cover, and water temperature will continue to influence lake productivity in this changing climate, we created a dataset of chlorophyll-a concentrations (a compound found in algae), associated water quality parameters, and solar radiation that can be used to for a wide range of research questions.
Mathew Lipson, Sue Grimmond, Martin Best, Winston T. L. Chow, Andreas Christen, Nektarios Chrysoulakis, Andrew Coutts, Ben Crawford, Stevan Earl, Jonathan Evans, Krzysztof Fortuniak, Bert G. Heusinkveld, Je-Woo Hong, Jinkyu Hong, Leena Järvi, Sungsoo Jo, Yeon-Hee Kim, Simone Kotthaus, Keunmin Lee, Valéry Masson, Joseph P. McFadden, Oliver Michels, Wlodzimierz Pawlak, Matthias Roth, Hirofumi Sugawara, Nigel Tapper, Erik Velasco, and Helen Claire Ward
Earth Syst. Sci. Data, 14, 5157–5178, https://doi.org/10.5194/essd-14-5157-2022, https://doi.org/10.5194/essd-14-5157-2022, 2022
Short summary
Short summary
We describe a new openly accessible collection of atmospheric observations from 20 cities around the world, capturing 50 site years. The observations capture local meteorology (temperature, humidity, wind, etc.) and the energy fluxes between the land and atmosphere (e.g. radiation and sensible and latent heat fluxes). These observations can be used to improve our understanding of urban climate processes and to test the accuracy of urban climate models.
Keyang He, Houyuan Lu, Jianping Zhang, and Can Wang
Earth Syst. Sci. Data, 14, 4777–4791, https://doi.org/10.5194/essd-14-4777-2022, https://doi.org/10.5194/essd-14-4777-2022, 2022
Short summary
Short summary
Here we presented the first quantitative spatiotemporal cropping patterns spanning the Neolithic and Bronze ages in northern China. Temporally, millet agriculture underwent a dramatic transition from low-yield broomcorn to high-yield foxtail millet around 6000 cal. a BP under the influence of climate and population. Spatially, millet agriculture spread westward and northward from the mid-lower Yellow River (MLY) to the agro-pastoral ecotone (APE) around 6000 cal. a BP and diversified afterwards.
Kailiang Yu, Johan van den Hoogen, Zhiqiang Wang, Colin Averill, Devin Routh, Gabriel Reuben Smith, Rebecca E. Drenovsky, Kate M. Scow, Fei Mo, Mark P. Waldrop, Yuanhe Yang, Weize Tang, Franciska T. De Vries, Richard D. Bardgett, Peter Manning, Felipe Bastida, Sara G. Baer, Elizabeth M. Bach, Carlos García, Qingkui Wang, Linna Ma, Baodong Chen, Xianjing He, Sven Teurlincx, Amber Heijboer, James A. Bradley, and Thomas W. Crowther
Earth Syst. Sci. Data, 14, 4339–4350, https://doi.org/10.5194/essd-14-4339-2022, https://doi.org/10.5194/essd-14-4339-2022, 2022
Short summary
Short summary
We used a global-scale dataset for the surface topsoil (>3000 distinct observations of abundance of soil fungi versus bacteria) to generate the first quantitative map of soil fungal proportion across terrestrial ecosystems. We reveal striking latitudinal trends. Fungi dominated in regions with low mean annual temperature (MAT) and net primary productivity (NPP) and bacteria dominated in regions with high MAT and NPP.
Juha Lemmetyinen, Juval Cohen, Anna Kontu, Juho Vehviläinen, Henna-Reetta Hannula, Ioanna Merkouriadi, Stefan Scheiblauer, Helmut Rott, Thomas Nagler, Elisabeth Ripper, Kelly Elder, Hans-Peter Marshall, Reinhard Fromm, Marc Adams, Chris Derksen, Joshua King, Adriano Meta, Alex Coccia, Nick Rutter, Melody Sandells, Giovanni Macelloni, Emanuele Santi, Marion Leduc-Leballeur, Richard Essery, Cecile Menard, and Michael Kern
Earth Syst. Sci. Data, 14, 3915–3945, https://doi.org/10.5194/essd-14-3915-2022, https://doi.org/10.5194/essd-14-3915-2022, 2022
Short summary
Short summary
The manuscript describes airborne, dual-polarised X and Ku band synthetic aperture radar (SAR) data collected over several campaigns over snow-covered terrain in Finland, Austria and Canada. Colocated snow and meteorological observations are also presented. The data are meant for science users interested in investigating X/Ku band radar signatures from natural environments in winter conditions.
Alejandro Miranda, Rayén Mentler, Ítalo Moletto-Lobos, Gabriela Alfaro, Leonardo Aliaga, Dana Balbontín, Maximiliano Barraza, Susanne Baumbach, Patricio Calderón, Fernando Cárdenas, Iván Castillo, Gonzalo Contreras, Felipe de la Barra, Mauricio Galleguillos, Mauro E. González, Carlos Hormazábal, Antonio Lara, Ian Mancilla, Francisca Muñoz, Cristian Oyarce, Francisca Pantoja, Rocío Ramírez, and Vicente Urrutia
Earth Syst. Sci. Data, 14, 3599–3613, https://doi.org/10.5194/essd-14-3599-2022, https://doi.org/10.5194/essd-14-3599-2022, 2022
Short summary
Short summary
Achieving a local understanding of fire regimes requires high-resolution, systematic and dynamic data. High-quality information can help to transform evidence into decision-making. Taking advantage of big-data and remote sensing technics we developed a flexible workflow to reconstruct burned area and fire severity data for more than 8000 individual fires in Chile. The framework developed for the database can be applied anywhere in the world with minimal adaptation.
Agustín Sarquis, Ignacio Andrés Siebenhart, Amy Theresa Austin, and Carlos A. Sierra
Earth Syst. Sci. Data, 14, 3471–3488, https://doi.org/10.5194/essd-14-3471-2022, https://doi.org/10.5194/essd-14-3471-2022, 2022
Short summary
Short summary
Plant litter breakdown in aridlands is driven by processes different from those in more humid ecosystems. A better understanding of these processes will allow us to make better predictions of future carbon cycling. We have compiled aridec, a database of plant litter decomposition studies in aridlands and tested some modeling applications for potential users. Aridec is open for use and collaboration, and we hope it will help answer newer and more important questions as the database develops.
Ulrike Herzschuh, Chenzhi Li, Thomas Böhmer, Alexander K. Postl, Birgit Heim, Andrei A. Andreev, Xianyong Cao, Mareike Wieczorek, and Jian Ni
Earth Syst. Sci. Data, 14, 3213–3227, https://doi.org/10.5194/essd-14-3213-2022, https://doi.org/10.5194/essd-14-3213-2022, 2022
Short summary
Short summary
Pollen preserved in environmental archives such as lake sediments and bogs are extensively used for reconstructions of past vegetation and climate. Here we present LegacyPollen 1.0, a dataset of 2831 fossil pollen records from all over the globe that were collected from publicly available databases. We harmonized the names of the pollen taxa so that all datasets can be jointly investigated. LegacyPollen 1.0 is available as an open-access dataset.
Cited articles
Alcaraz-Segura, D., Paruelo, J., and Cabello, J.: Identification of current
ecosystem functional types in the Iberian Peninsula, Global Ecol. Biogeogr., 15, 200–212, https://doi.org/10.1111/j.1466-822X.2006.00215.x, 2006.
Alcaraz-Segura, D., Cabello, J., Paruelo, J. M., and Delibes, M.: Use of
Descriptors of Ecosystem Functioning for Monitoring a National Park Network:
A Remote Sensing Approach, Environ. Manage., 43, 38–48,
https://doi.org/10.1007/s00267-008-9154-y, 2009.
Alcaraz-Segura, D., Paruelo, J. M., Epstein, H. E., and Cabello, J.:
Environmental and Human Controls of Ecosystem Functional Diversity in Temperate South America, Remote Sens, 5, 127–154, https://doi.org/10.3390/rs5010127,
2013.
Alcaraz-Segura, D., Lomba, A., Sousa-Silva, R., Nieto-Lugilde, D., Alves,
P., Georges, D., Vicente, J. R., and Honrado, J. P.: Potential of
satellite-derived Ecosystem Functional Attributes to anticipate species
range shifts, Int. J. Appl Earth Obs. Geoinf., 57, 86–92, https://doi.org/10.1016/j.jag.2016.12.009, 2017.
Alcaraz-Segura, D., Blanco-Sacristán, J., Bagnato, C., Cintas, J., and Cazorla, B. P.: Ecosystem Functional Types (EFTs) code, Zenodo [code], https://doi.org/10.5281/zenodo.7524973, 2023.
Anderson, C. B.: Biodiversity monitoring, earth observations and the ecology
of scale, Ecol. Lett., 21, 1572–1585, https://doi.org/10.1111/ele.13106, 2018.
Aragones, D., Rodriguez-Galiano, V. F., Caparros-Santiago, J. A., and Navarro-Cerrillo, R. M.: Could land surface phenology be used to discriminate
Mediterranean pine species?, Int. J. Appl. Earth Obs. Geoinf., 78, 281–294, https://doi.org/10.1016/j.jag.2018.11.003, 2019.
Arenas-Castro, S., Gonçalves, J., Alves, P., Alcaraz-Segura, D., and
Honrado, J. P.: Assessing the multiscale predictive ability of Ecosystem
Functional Attributes for species distribution modelling, PLOS ONE, 13,
e0199292, https://doi.org/10.1371/journal.pone.0199292, 2018.
Balvanera, P., Pfisterer, A. B., Buchmann, N., He, J.-S., Nakashizuka, T.,
Raffaelli, D., and Schmid, B.: Quantifying the evidence for biodiversity
effects on ecosystem functioning and services, Ecol. Lett., 9, 1146–1156,
https://doi.org/10.1111/j.1461-0248.2006.00963.x, 2006.
Blanca, G., Cueto, M., Martínez-Lirola, M. J., and Molero-Mesa, J.:
Threatened vascular flora of Sierra Nevada (Southern Spain), Biol. Conserv.,
85, 269–285, https://doi.org/10.1016/S0006-3207(97)00169-9, 1998.
Blanca, G., Cueto, M., and Romero, A. T.: Rareza y endemicidad en la flora vascular de Sierra Nevada, in Biología de la conservación de plantas en Sierra Nevada: Principios y retos para su preservación, Editorial Universidad de Granada, 325–343, ISBN 9788433865120, 2019.
Bennett, E. M., Cramer, W., Begossi, A., Cundill, G., Díaz, S., Egoh, B. N., Geijzendorffer, I. R., Krug, C. B., Lavorel, S., Lazos, E., Lebel, L., Martín-López, B., Meyfroidt, P., Mooney, H. A., Nel, J. L., Pascual, U., Prieur-Richard, A. H., Reyers, B., Roebeling, P., Seppelt, R., Solan, M., Tschakert, P., Tscharntke, T., Turner II, B. L., Veburg, P. H., Viglizzo, E. F., White, P. C. L., and Woodward, G.: Linking biodiversity, ecosystem services, and human well-being: three challenges for designing research for sustainability, Curr. Opin. Environ. Sustain., 14, 76–85, https://doi.org/10.1016/j.cosust.2015.03.007, 2015.
Box, E. O., Holben, B. N., and Kalb, V.: Accuracy of the AVHRR vegetation
index as a predictor of biomass, primary productivity and net CO2 flux, Vegetatio, 80, 71–89, https://doi.org/10.1007/BF00048034, 1989.
Cabello, J., Fernández, N., Alcaraz-Segura, D., Oyonarte, C., Piñeiro, G., Altesor, A., Delibes, M., and Paruelo, J. M.: The ecosystem
functioning dimension in conservation: insights from remote sensing, Biodivers. Conserv., 21, 3287–3305, https://doi.org/10.1007/s10531-012-0370-7, 2012a.
Cabello, J., Alcaraz-Segura, D., Ferrero, R., Castro, A. J., and Liras, E.: The role of vegetation and lithology in the spatial and inter-annual response of EVI to climate in drylands of Southeastern Spain, J. Arid Environ., 79, 76–83, 2012b.
Cabello, J., Lourenço, P., Reyes, A., and Alcaraz-Segura, D.: Ecosystem services assessment of national parks networks for functional diversity and carbon conservation strategies using remote sensing, in: Earth observation of ecosystem services, edited by: Alcaraz-Segura, D., Di Bella, C. M., and Straschnoy, J. V., CRC Press, Boca Raton,179–200, ISBN 13:978-1-4665-0589-6, 2013.
Cabello, J., Alcaraz-Segura, D., Reyes-Díez, A., Lourenço, P.,
Requena-Mullor, J., Bonache, J., Castillo, P., Valencia, S., Naya, J.,
Ramírez, L., and Serrada, J.: Sistema para el Seguimiento del
funcionamiento de ecosistemas en la Red de Parques Nacionales de España
mediante Teledetección, Revista de Teledetección, 46, 119–131,
https://doi.org/10.4995/raet.2016.5731, 2016.
Calzado, M. R. F., Mesa, J. M., Merzouki, A., and Porcel, M. C.: Vascular
plant diversity and climate change in the upper zone of Sierra Nevada, Spain, Plant Biosyst.-Plan Biosyst., 146, 1044–1053, https://doi.org/10.1080/11263504.2012.710273, 2012.
Camacho-Olmedo, M., García-Martínez, P., Jiménez-Olivencia, Y., Menor-Toribio, J., and PanizaCabrera, A.: Dinámica evolutiva del paisaje vegetal de la Alta Alpujarra granadina en la segunda mitad del s. XX, Cuad Geogr., 32, 25–42, 2002.
Cañadas, E. M., Fenu, G., Peñas, J., Lorite, J., Mattana, E., and
Bacchetta, G.: Hotspots within hotspots: Endemic plant richness, environmental drivers, and implications for conservation, Biol. Conserv., 170, 282–291, https://doi.org/10.1016/j.biocon.2013.12.007, 2014.
Cazorla, B. P., Cabello, J., Peñas, J., Guirado, E.., Reyes-Díez, A., and Alcaraz-Segura, D.: Funcionamiento de la vegetación y diversidad
funcional de los ecosistemas de Sierra Nevada, in Biología de la
conservación de plantas en Sierra Nevada: Principios y retos para su
preservación, Editorial Universidad de Granada, 325–343, ISBN 9788433865120, 2019.
Cazorla, B. P., Cabello, J., Reyes-Díez, A., Guirado, E., Peñas, J., Pérez-Luque, A J., and Alcaraz-Segura, D.: Ecosystem functioning and
functional diversity of Sierra Nevada (SE Spain), University of Almería
and Granada, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.924792, 2020a.
Cazorla, B. P., Cabello, J., Penas, J., Garcillan, P. P., Reyes, A., and
Alcaraz-Segura, D.: Incorporating Ecosystem Functional Diversity into Geographic Conservation Priorities Using Remotely Sensed Ecosystem Functional Types, Ecosystems, 24, 548–564, https://doi.org/10.1007/s10021-020-00533-4, 2020b.
Coops, N. C., Kearney, S. P., Bolton, D. K., and Radeloff, V. C.: Remotely-sensed productivity clusters capture global biodiversity patterns,
Sci. Rep., 8, 16261, https://doi.org/10.1038/s41598-018-34162-8, 2018.
Costanza, R., Norton, B. G., and Haskell, B. D.: Ecosystem Health: New Goals
for Environmental Management, Island Press, ISBN 1-555963-141-4, 1992.
Didan, K., Munoz, A. B., Solano, R., and Huete, A.: MODIS vegetation index
user's guide (MOD13 series), Vegetation Index and Phenology Lab, University of Arizona, https://modis-land.gsfc.nasa.gov/pdf/MOD13_User_Guide_V61.pdf (last access: 17 April 2023), 2015.
Duro, D. C., Coops, N. C., Wulder, M. A., and Han, T.: Development of a large
area biodiversity monitoring system driven by remote sensing, Prog. Phys. Geogr., 31, 235–260, https://doi.org/10.1177/0309133307079054, 2007.
Fernández, N., Paruelo, J. M., and Delibes, M.: Ecosystem functioning of
protected and altered Mediterranean environments: A remote sensing
classification in Doñana, Spain, Remote Sens. Environ., 114, 211–220,
https://doi.org/10.1016/j.rse.2009.09.001, 2010.
Fernández Calzado, M. R., Molero Mesa, J., Merzouki, A., and Casares Porcel, M.: Vascular plant diversity and climate change in the upper zone of Sierra Nevada, Spain, Plant Biosyst.-Plan Biosyst., 146, 1044–1053,
https://doi.org/10.1080/11263504.2012.710273, 2012.
Geerken, R. A.: An algorithm to classify and monitor seasonal variations in
vegetation phenologies and their inter-annual change, ISPRS J. Photogram. Remots. Sens., 64, 422–431, https://doi.org/10.1016/j.isprsjprs.2009.03.001, 2009.
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., and Moore,
R.: Google Earth Engine: Planetary-scale geospatial analysis for everyone,
Remote Sens. Environ., 202, 18–27, https://doi.org/10.1016/j.rse.2017.06.031, 2017.
Grêt-Regamey, A., Brunner, S. H., and Kienast, F.: Mountain ecosystem
services: who cares?, Mt. Res. Dev., 32, 32–45, https://doi.org/10.1659/MRD-JOURNAL-D-10-00115.S1, 2012.
Haines-Young, R. and Potschin, M.: The links between biodiversity, ecosystem
services and human well-being, Ecosyst. Ecol., 1, 110–139, https://doi.org/10.1017/CBO9780511750458.007, 2010.
Huete, A., Justice, C., and Van Leeuwen, W.: MODIS vegetation index (MOD13),
Algorithm theoretical basis document, NASA EOS-MODIS, 3 pp., https://modis.gsfc.nasa.gov/data/atbd/atbd_mod13.pdf (last access: 17 April 2023), 1999.
Huete, A., Didan, K., Miura, T., Rodriguez, E. P., Gao, X., and Ferreira, L. G.: Overview of the radiometric and biophysical performance of the MODIS vegetation indices, Remote Sens. Environ., 83, 195–213,
https://doi.org/10.1016/S0034-4257(02)00096-2, 2002.
Huete, A. R., Liu, H. Q., Batchily, K. V., and Van Leeuwen, W. J. D. A.: A
comparison of vegetation indices over a global set of TM images for EOS-MODIS, Remote Sens. Environ., 59, 440–451,
https://doi.org/10.1016/S0034-4257(96)00112-5, 1997.
Ivits, E., Cherlet, M., Mehl, W., and Sommer, S.: Ecosystem functional units
characterized by satellite observed phenology and productivity gradients: A
case study for Europe, Ecol Indic., 27, 17–28, https://doi.org/10.1016/j.ecolind.2012.11.010, 2013.
Jaccard, P.: Étude comparative de la distribution florale dans une
portion des Alpes et des Jura, Bull. Soc. Vaudoise Sci. Nat., 37, 547–579,
1901.
Jax, K.: Ecosystem Functioning, Cambridge University Press, Cambridge,
ISBN 978-0-521-70523-3, 2010.
Karlsen, S. R., Elvebakk, A., Høgda, K. A., and Johansen, B.: Satellite-based mapping of the growing season and bioclimatic zones in
Fennoscandia, Global Ecol. Biogeogr., 15, 416–430,
https://doi.org/10.1111/j.1466-822X.2006.00234.x, 2006.
Lee, S.-J., Berbery, E. H., and Alcaraz-Segura, D.: The impact of ecosystem
functional type changes on the La Plata Basin climate, Adv. Atmos. Sci., 30, 1387–1405, https://doi.org/10.1007/s00376-012-2149-x, 2013.
Le Houérou, H. N.: A probabilistic approach to assessing arid rangelands' productivity, carrying capacity and stocking rates, in: Drylands: Sustainable Use of Rangelands into the Twenty First Century, edited by: Squires, V. R. and Sidahmed, A. E., IFAD, Rome, Italy, 1998.
Lorite, J.: An updated checklist of the vascular flora of Sierra Nevada (SE Spain), Phytotaxa, 261, 1–57, 2016.
Lorite, J., Ros-Candeira, A., Alcaraz-Segura, D., Salazar-Mendías, C.:
FloraSNevada: a trait database of the vascular flora of Sierra Nevada,
southeast Spain, Ecology, 101, e03091, https://doi.org/10.1002/ecy.3091, 2020.
Mace, G. M., Reyers, B., Alkemade, R., Biggs, R., Chapin, F. S., Cornell, S.
E., Díaz, S., Jennings, S., Leadley, P., Mumby, P. J., Purvis, A., Scholes, R. J., Seddon, A. W. R., Solan, M., Steffen, W., and Woodward, G.:
Approaches to defining a planetary boundary for biodiversity, Global Environ.
Change, 28, 289–297, https://doi.org/10.1016/j.gloenvcha.2014.07.009, 2014.
Molero, J. and Marfil, J. M.: The bioclimates of Sierra Nevada National Park, Int. J. Geobot. Res., 5, 1–11, https://doi.org/10.1007/978-3-319-54867-8_4, 2015.
Mota, J. F., Sola, A. J., Jiménez-Sánchez, M. L., Pérez-García, F., and Merlo, M. E.: Gypsicolous flora, conservation
and restoration of quarries in the southeast of the Iberian Peninsula,
Biodivers. Conserv., 13, 1797–1808, https://doi.org/10.1023/B:BIOC.0000035866.59091.e5, 2004.
Mouchet, M. A., Villéger, S., Mason, N. W. H., and Mouillot, D.: Functional diversity measures: an overview of their redundancy and their
ability to discriminate community assembly rules, Funct. Ecol., 24, 867–876, https://doi.org/10.1111/j.1365-2435.2010.01695.x, 2010.
Mucina, L.: Biome: evolution of a crucial ecological and biogeographical
concept, New Phytol., 222, 97–114, https://doi.org/10.1111/nph.15609, 2019.
Müller, O. V., Berbery, E. H., Alcaraz-Segura, D., and Ek, M. B.: Regional Model Simulations of the 2008 Drought in Southern South America
Using a Consistent Set of Land Surface Properties, J. Climate, 590, 6754–6778, https://doi.org/10.1175/JCLI-D-13-00463.1, 2014.
Oki, T., Blyth, E. M., Berbery, E. H., and Alcaraz-Segura, D.: Land Use and
Land Cover Changes and Their Impacts on Hydroclimate, Ecosystems and
Society, in: Climate Science for Serving Society: Research, Modeling and
Prediction Priorities, edited by: Asrar, G. R. and Hurrell, J. W., Springer Netherlands, Dordrecht, 185–203, https://doi.org/10.1007/978-94-007-6692-1_7, 2013.
Paruelo, J. M. and Lauenroth, W. K.: Regional patterns of normalized difference vegetation index in North American shrublands and grasslands,
Ecology, 76, 1888–1898, https://doi.org/10.2307/1940721, 1995.
Paruelo, J. M., Jobbágy, E. G., and Sala, O. E.: Current Distribution of
Ecosystem Functional Types in Temperate South America, Ecosystems, 4, 683–698, https://doi.org/10.1007/s10021-001-0037-9, 2001.
Peinado, F. J. M., Morales, M. N. J., and Ondoño, E. F.: Los suelos de
Sierra Nevada, su relación con la litología y la vegetación, in
Biología de la conservación de plantas en Sierra Nevada: Principios
y retos para su preservación, Editorial Universidad de Granada, 173–192, ISBN 9788433865120, 2019.
Pelkey, N. W., Stoner, C. J., and Caro, T. M.: Assessing habitat protection
regimes in Tanzania using AVHRR NDVI composites: Comparisons at different
spatial and temporal scales, Int. J. Remote Sens., 24, 2533–2558,
https://doi.org/10.1080/01431160210155929, 2003.
Peñas, J., Sánchez, E. C., and del Río Sánchez, J.:
Fitogeografía de Sierra Nevada e implicaciones para la conservación, in Biología de la conservación de plantas en Sierra Nevada: Principios y retos para su preservación, Editorial Universidad de Granada, 81–116, ISBN 9788433865137, 2019.
Pereira, H. M., Ferrier, S., Walters, M., Geller, G. N., Jongman, R. H. G.,
Scholes, R. J., Bruford, M. W., Brummitt, N., Butchart, S. H. M., Cardoso,
A. C., Coops, N. C., Dulloo, E., Faith, D. P., Freyhof, J., Gregory, 540 R.
D., Heip, C., Höft, R., Hurtt, G., Jetz, W., Karp, D. S., McGeoch, M.
A., Obura, D., Onoda, Y., Pettorelli, N., Reyers, B., Sayre, R., Scharlemann, J. P. W., Stuart, S. N., Turak, E., Walpole, M., and Wegmann, M.: Essential Biodiversity Variables, Science, 339, 277–278, https://doi.org/10.1126/science.1229931, 2013.
Pérez-Hoyos, A., Martínez, B., García-Haro, F. J., Moreno, Á., and Gilabert, M. A.: Identification of Ecosystem Functional Types from Coarse Resolution Imagery Using a Self-Organizing Map Approach: A Case
Study for Spain, Remote Sens., 6, 11391–11419, https://doi.org/10.3390/rs61111391,
2014.
Pérez-Luque, A. J., Bonet, F. J., Pérez-Pérez, R., Aspizua, R.,
Lorite, J., and Zamora, R.: Sinfonevada: Dataset of floristic diversity in
Sierra Nevada forests (SE Spain), PhytoKeys, 35, 1–15,
https://doi.org/10.3897/phytokeys.35.6363, 2014.
Pérez-Luque, A. J., Pérez-Pérez, R., Bonet-García, F. J., and Magaña, P. J.: An ontological system based on MODIS images to assess
ecosystem functioning of Natura 2000 habitats: A case study for Quercus pyrenaica forests, Int. J. Appl. Earth Obs. Geoinf., 37, 142–151,
https://doi.org/10.1016/j.jag.2014.09.003, 2015a.
Pérez-Luque, A. J., Zamora, R., Bonet, F. J., and Pérez-Pérez, R.: Dataset of MIGRAME Project (Global Change, Altitudinal Range Shift and
Colonization of Degraded Habitats in Mediterranean Mountains), PhytoKeys, 56, 61–81, https://doi.org/10.3897/phytokeys.56.5482, 2015b.
Pérez-Luque, A. J., Sánchez-Rojas, C. P., Zamora, R., Pérez-Pérez, R., and Bonet, F. J.: Dataset of Phenology of Mediterranean high-mountain meadows flora (Sierra Nevada, Spain), PhytoKeys,
46, 89–107, https://doi.org/10.3897/phytokeys.46.9116, 2015c.
Pérez-Luque, A. J., Barea-Azcón, J. M., Álvarez-Ruiz, L.,
Bonet-García, F. J., and Zamora, R.: Dataset of Passerine bird communities in a Mediterranean high mountain (Sierra Nevada, Spain), ZooKeys, 552, 137–154, https://doi.org/10.3897/zookeys.552.6934, 2016.
Pérez-Luque, A. J., Bonet-García, F. J., and Zamora Rodríguez, R.: Map of ecosystems types in Sierra Nevada mountain (southern Spain),
PANGAEA [data set], https://doi.org/10.1594/PANGAEA.910176, 2019.
Pérez-Luque, A. J., Cazorla, B. P., Cabello, J., Reyes, A., Guirado, E., Peñas, J., and Alcaraz-Segura, D.: Ecosystem functioning and functional diversity of Sierra Nevada, [data set],
http://obsnev.es/apps/efts_SN.html, last access: 17 April 2023.
Petchey, O. L. and Gaston, K. J.: Functional diversity: back to basics and
looking forward, Ecol. Lett., 9, 741–758, https://doi.org/10.1111/j.1461-0248.2006.00924.x, 2006.
Pettorelli, N., Vik, J. O., Mysterud, A., Gaillard, J.-M., Tucker, C. J., and
Stenseth, N. Chr.: Using the satellite-derived NDVI to assess ecological
responses to environmental change, Trends Ecol. Evol., 20, 503–510, https://doi.org/10.1016/j.tree.2005.05.011, 2005.
Pettorelli, N., Wegmann, M., Skidmore, A., Mücher, S., Dawson, T. P.,
Fernandez, M., Lucas, R., Schaepman, M. E., Wang, T., O'Connor, B., Jongman,
R. H. G., Kempeneers, P., Sonnenschein, R., Leidner, A. K., Böhm, M., He, K. S., Nagendra, H., Dubois, G., Fatoyinbo, T., Hansen, M. C., Paganini, M., de Klerk, H. M., Asner, G. P., Kerr, J. T., Estes, A. B., Schmeller, D. S., Heiden, U., Rocchini, D., Pereira, H. M., Turak, E., Fernandez, N., Lausch, A., Cho, M. A., Alcaraz-Segura, D., McGeoch, M. A., Turner, W., Mueller, A., St-Louis, V., Penner, J., Vihervaara, P., Belward, A., Reyers, B., and Geller, G. N.: Framing the concept of satellite remote sensing essential biodiversity variables: challenges and future directions, Remote Sens. Ecol. Conserv., 2, 122–131, https://doi.org/10.1002/rse2.15, 2016.
Pettorelli, N., Schulte to Bühne, H., Tulloch, A., Dubois, G., Macinnis-Ng, C., Queirós, A. M., Keith, D. A., Wegmann, M., Schrodt, F.,
Stellmes, M., Sonnenschein, R., Geller, G. N., Roy, S., Somers, B., Murray,
N., Bland, L., Geijzendorffer, I., Kerr, J. T., Broszeit, S., Leitão, P.
J., Duncan, C., Serafy, G. E., He, K. S., Blanchard, J. L., Lucas, R., Mairota, P., Webb, T. J., and Nicholson, E.: Satellite remote sensing of
ecosystem functions: opportunities, challenges and way forward, Remote Sens.
Ecol. Conserv., 4, 71–93, https://doi.org/10.1002/rse2.59, 2018.
Regos, A., Gagne, L., Alcaraz-Segura, D., Honrado, J. P., and Domínguez, J.: Effects of species traits and environmental predictors on performance and transferability of ecological niche models, Sci. Rep., 9, 1–14, https://doi.org/10.1038/s41598-019-40766-5, 2019.
Requena-Mullor, J. M., López, E., Castro, A. J., Alcaraz-Segura, D.,
Castro, H., Reyes, A., and Cabello, J.: Remote-sensing based approach to
forecast habitat quality under climate change scenarios, PLOS ONE, 12, e0172107, https://doi.org/10.1371/journal.pone.0172107, 2017.
Ros-Candeira, A., Pérez-Luque, A. J., Suárez-Muñoz, M.,
Bonet-García, F. J., Hódar, J. A., Giménez de Azcárate, F., and Ortega-Díaz, E.: Dataset of occurrence and incidence of pine
processionary moth in Andalusia, south Spain, ZooKeys, 852, 125–136,
https://doi.org/10.3897/zookeys.852.28567, 2019.
Ros-Candeira, A., Moreno-Llorca, R., Alcaraz-Segura, D., Bonet-García,
F. J., and Vaz, A. S.: Social media photo content for Sierra Nevada: a dataset to support the assessment of cultural ecosystem services in protected areas, Nat. Conserv., 38, 1–12, 2020.
Running, S. W., Thornton, P. E., Nemani, R., and Glassy, J. M.: Global
Terrestrial Gross and Net Primary Productivity from the Earth Observing
System, in: Methods in Ecosystem Science, edited by: Sala, O. E., Jackson, R. B., Mooney, H. A., and Howarth, R. W., Springer New York, New York, NY, 44–57, https://doi.org/10.1007/978-1-4612-1224-9_4, 2000.
Skidmore, A. K., Pettorelli, N., Coops, N. C., Geller, G. N., Hansen, M.,
Lucas, R., Mücher, C. A., O'Connor, B., Paganini, M., Pereira, H. M.,
Schaepman, M. E., Turner, W., Wang, T., and Wegmann, M.: Environmental science: Agree on biodiversity metrics to track from space, Nature, 523, 403–405, https://doi.org/10.1038/523403a, 2015.
Skidmore, A. K., Coops, N. C., Neinavaz, E., Ali, A., Schaepman, M. E., Paganini, M., Kissling, W. D., Vihervaara, P., Darvishzadeh, R., Feihauer, H., Fernandez, M., Fernandez, N., Gorelick, N., Geijzendorffer, I., Heiden, U., Heurich, M., Hobern, D., Holzwarth, S., Muller-Karger, F. E., Van De Kervoche, R., Lausch, A., Leitao, P. J., Lock, M. C., Mücher, C. A., O'Connor, B., Rocchini, D., Roeoesli, C., Turner, W., Vis, J. K., Wang, T., Wegmann, M., and Wingate, V.: Priority list of biodiversity metrics to observe from space, Nat. Ecol. Evol., 5, 896–906, https://doi.org/10.1038/s41559-021-01451-x, 2021.
Steffen, W., Richardson, K., Rockström, J., Cornell, S. E., Fetzer, I.,
Bennett, E. M., Biggs, R., Carpenter, S. R., de Vries, W., de Wit, C. A.,
Folke, C., Gerten, D., Heinke, J., Mace, G. M., Persson, L. M., Ramanathan,
V., Reyers, B., and Sörlin, S.: Planetary boundaries: Guiding human
development on a changing planet, Science, 347, 1259855,
https://doi.org/10.1126/science.1259855, 2015.
Townshend, J. R., Goff, T. E., and Tucker, C. J.: Multitemporal dimensionality of images of normalized difference vegetation index at continental scales, IEEE T. Geosci. Remote, 6, 888–895, https://doi.org/10.1109/TGRS.1985.289474, 1985.
Valle, F., Algarra, J. A., Arrojo, E., Asensi, A., Cabello, J., Cano, E.,
Cañadas Sánchez, E., Cueto, M., Dana, E., and Simón, D.: Mapa de
series de vegetación de Andalucía, ISBN 84-7207-137-5, 2003.
Villarreal, S., Guevara, M., Alcaraz-Segura, D., Brunsell, N. A., Hayes, D.,
Loescher, H. W., and Vargas, R.: Ecosystem functional diversity and the
representativeness of environmental networks across the conterminous United
States, Agr. Forest Meteorol., 262, 423–433, https://doi.org/10.1016/j.agrformet.2018.07.016, 2018.
Virginia, R. A., Wall, D. H., and Levin, S. A.: Principles of ecosystem
function, Encycloped. Biodivers., 2, 345–352, 2001.
Wang, Y. and Huang, F.: Identification and analysis of ecosystem functional
types in the west of Songnen Plain, China, based on moderate resolution
imaging spectroradiometer data, J. Appl. Remote Sens., 9, 096096, https://doi.org/10.1117/1.JRS.9.096096, 2015.
Wilson, M. V. and Shmida, A.: Measuring beta diversity with presence-absence
data, J. Ecol., 72, 1055–1064, https://doi.org/10.2307/2259551, 1984.
Xiao, J., Chevallier, F., Gomez, C., Guanter, L., Hicke, J. A., Huete, A. R., Ichii, K., Ni, W., Pang, Y., Rahman, A. F., Sun, G., Yuan, W., Zhang, L., and Zhang, X.: Remote sensing of the terrestrial carbon cycle: A review of advances over 50 years, Remote Sens. Environ., 233, 111383, https://doi.org/10.1016/j.rse.2019.111383, 2019.
Zamora, R., Pérez-Luque, A. J., Bonet, F. J., Barea-Azcón, J. M., Aspizua, R., Sánchez-Gutiérrez, F. J., Cano-Manuel, F. J., Ramos-Losada, B., and Henares-Civantos, I.: Global Change Impact in the Sierra Nevada Long-Term Ecological Research Site (Southern Spain), Bull. Ecol. Soc. Am., 98, 157–164, https://doi.org/10.1002/bes2.1308, 2017.
Zamora Rodríguez, R. J., Pérez-Luque, A. J., Bonet, F. J.,
Barea-Azcón, J. M., and Aspizua, R.: Global Change Impacts in Sierra
Nevada: Challenges for Conservation, Consejería de Medio Ambiente y
Ordenación del Terriorio, Junta de Andalucía, 208 pp., ISBN 978-84-338-5960-0, 2016.
Short summary
This dataset provides scientists, environmental managers, and the public in general with valuable information on the first characterization of ecosystem functional diversity based on primary production developed in the Sierra Nevada (Spain), a biodiversity hotspot in the Mediterranean basin and an exceptional natural laboratory for ecological research within the Long-Term Social-Ecological Research (LTSER) network.
This dataset provides scientists, environmental managers, and the public in general with...
Altmetrics
Final-revised paper
Preprint