Find research datasets worth reusing

MUSES Leaf Area Index (LAI) 8-Day Global 500m SIN Grid in 2008

The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (<a href="https://muses.bnu.edu.cn/">https://muses.bnu.edu.cn/</a>). This dataset is the MUSES global LAI product at 500m spatial resolution and 8-day temporal resolution. The MUSES LAI product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance product using general regression neural networks (GRNNs)  (Xiao et al., 2014; Xiao et al., 2016). The MUSES LAI product is spatially complete and temporally continuous. This dataset is the MUSES LAI product in 2008. Please <a href="https://zenodo.org/record/7606552#.Y9-m5XZByUk">click here</a> to download the MUSES LAI product in 2007, and <a href="https://zenodo.org/record/7608458#.Y-DyDnZByUk">click here</a> to download the MUSES LAI product in 2009. Dataset Characteristics: <ul> <li>Spatial Coverage: Global</li> <li>Temporal Coverage: 2008</li> <li>Spatial Resolution: 500m</li> <li>Temporal Resolution: 8 days</li> <li>Projection: Sinusoidal</li> <li>Data Format: HDF</li> <li>Scale: 0.01</li> <li>Valid Range: 0 – 1000</li> </ul> Citation (Please cite this paper whenever these data are used): <ol> <li>Xiao Zhiqiang, et al. (2014). Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product From Time-Series MODIS Surface Reflectance. IEEE Transactions on Geoscience and Remote Sensing, 52, 209-223.</li> <li>Xiao Zhiqiang, et al. (2016). Long-time-series global land surface satellite leaf area index product derived from MODIS and AVHRR surface reflectance. IEEE Transactions on Geoscience and Remote Sensing, 54, 5301-5318.</li> <li>Xiao Zhiqiang, Jinling Song, Hua Yang, Rui Sun and Juan Li. (2022). A 250 m resolution global leaf area index product derived from MODIS surface reflectance data. International Journal of Remote Sensing, 43(4), 1199-1225.</li> <li>Xiao Zhiqiang, et al. (2017). Evaluation of four long time-series global leaf area index products. Agricultural and Forest Meteorology, 246, 218-230.</li> </ol> If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).

BLD Leaf Physiology

Data sets associated with the publication: Physiological impacts of beech leaf disease across a gradient of symptom severity among understory American beech.

MUSES Leaf Area Index (LAI) 8-Day Global 500m SIN Grid in 2009

The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (<a href="https://muses.bnu.edu.cn/">https://muses.bnu.edu.cn/</a>). This dataset is the MUSES global LAI product at 500m spatial resolution and 8-day temporal resolution. The MUSES LAI product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance product using general regression neural networks (GRNNs)  (Xiao et al., 2014; Xiao et al., 2016). The MUSES LAI product is spatially complete and temporally continuous. This dataset is the MUSES LAI product in 2009. Please <a href="https://zenodo.org/record/7607281#.Y-BpU3ZByUk">click here</a> to download the MUSES LAI product in 2008, and <a href="https://zenodo.org/record/7611973#.Y-GcknZByUk">click here</a> to download the MUSES LAI product in 2010. Dataset Characteristics: <ul> <li>Spatial Coverage: Global</li> <li>Temporal Coverage: 2009</li> <li>Spatial Resolution: 500m</li> <li>Temporal Resolution: 8 days</li> <li>Projection: Sinusoidal</li> <li>Data Format: HDF</li> <li>Scale: 0.01</li> <li>Valid Range: 0 – 1000</li> </ul> Citation (Please cite this paper whenever these data are used): <ol> <li>Xiao Zhiqiang, et al. (2014). Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product From Time-Series MODIS Surface Reflectance. IEEE Transactions on Geoscience and Remote Sensing, 52, 209-223.</li> <li>Xiao Zhiqiang, et al. (2016). Long-time-series global land surface satellite leaf area index product derived from MODIS and AVHRR surface reflectance. IEEE Transactions on Geoscience and Remote Sensing, 54, 5301-5318.</li> <li>Xiao Zhiqiang, Jinling Song, Hua Yang, Rui Sun and Juan Li. (2022). A 250 m resolution global leaf area index product derived from MODIS surface reflectance data. International Journal of Remote Sensing, 43(4), 1199-1225.</li> <li>Xiao Zhiqiang, et al. (2017). Evaluation of four long time-series global leaf area index products. Agricultural and Forest Meteorology, 246, 218-230.</li> </ol> If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).

MUSES Leaf Area Index (LAI) 8-Day Global 500m SIN Grid in 2010

The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (<a href="https://muses.bnu.edu.cn/">https://muses.bnu.edu.cn/</a>). This dataset is the MUSES global LAI product at 500m spatial resolution and 8-day temporal resolution. The MUSES LAI product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance product using general regression neural networks (GRNNs)  (Xiao et al., 2014; Xiao et al., 2016). The MUSES LAI product is spatially complete and temporally continuous. This dataset is the MUSES LAI product in 2010. Please <a href="https://zenodo.org/record/7608458#.Y-DyDnZByUk">click here</a> to download the MUSES LAI product in 2009, and <a href="https://zenodo.org/record/7613729#.Y-JCz3ZByUk">click here</a> to download the MUSES LAI product in 2011. Dataset Characteristics: <ul> <li>Spatial Coverage: Global</li> <li>Temporal Coverage: 2010</li> <li>Spatial Resolution: 500m</li> <li>Temporal Resolution: 8 days</li> <li>Projection: Sinusoidal</li> <li>Data Format: HDF</li> <li>Scale: 0.01</li> <li>Valid Range: 0 – 1000</li> </ul> Citation (Please cite this paper whenever these data are used): <ol> <li>Xiao Zhiqiang, et al. (2014). Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product From Time-Series MODIS Surface Reflectance. IEEE Transactions on Geoscience and Remote Sensing, 52, 209-223.</li> <li>Xiao Zhiqiang, et al. (2016). Long-time-series global land surface satellite leaf area index product derived from MODIS and AVHRR surface reflectance. IEEE Transactions on Geoscience and Remote Sensing, 54, 5301-5318.</li> <li>Xiao Zhiqiang, Jinling Song, Hua Yang, Rui Sun and Juan Li. (2022). A 250 m resolution global leaf area index product derived from MODIS surface reflectance data. International Journal of Remote Sensing, 43(4), 1199-1225.</li> <li>Xiao Zhiqiang, et al. (2017). Evaluation of four long time-series global leaf area index products. Agricultural and Forest Meteorology, 246, 218-230.</li> </ol> If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).

MUSES Leaf Area Index (LAI) 8-Day Global 500m SIN Grid in 2011

The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (<a href="https://muses.bnu.edu.cn/">https://muses.bnu.edu.cn/</a>). This dataset is the MUSES global LAI product at 500m spatial resolution and 8-day temporal resolution. The MUSES LAI product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance product using general regression neural networks (GRNNs)  (Xiao et al., 2014; Xiao et al., 2016). The MUSES LAI product is spatially complete and temporally continuous. This dataset is the MUSES LAI product in 2011. Please <a href="https://zenodo.org/record/7611973#.Y-GcknZByUk">click here</a> to download the MUSES LAI product in 2010, and <a href="https://zenodo.org/record/7615278#.Y-MBsnZByUk">click here</a> to download the MUSES LAI product in 2012. Dataset Characteristics: <ul> <li>Spatial Coverage: Global</li> <li>Temporal Coverage: 2011</li> <li>Spatial Resolution: 500m</li> <li>Temporal Resolution: 8 days</li> <li>Projection: Sinusoidal</li> <li>Data Format: HDF</li> <li>Scale: 0.01</li> <li>Valid Range: 0 – 1000</li> </ul> Citation (Please cite this paper whenever these data are used): <ol> <li>Xiao Zhiqiang, et al. (2014). Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product From Time-Series MODIS Surface Reflectance. IEEE Transactions on Geoscience and Remote Sensing, 52, 209-223.</li> <li>Xiao Zhiqiang, et al. (2016). Long-time-series global land surface satellite leaf area index product derived from MODIS and AVHRR surface reflectance. IEEE Transactions on Geoscience and Remote Sensing, 54, 5301-5318.</li> <li>Xiao Zhiqiang, Jinling Song, Hua Yang, Rui Sun and Juan Li. (2022). A 250 m resolution global leaf area index product derived from MODIS surface reflectance data. International Journal of Remote Sensing, 43(4), 1199-1225.</li> <li>Xiao Zhiqiang, et al. (2017). Evaluation of four long time-series global leaf area index products. Agricultural and Forest Meteorology, 246, 218-230.</li> </ol> If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).

MUSES Leaf Area Index (LAI) 8-Day Global 500m SIN Grid in 2012

The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (<a href="https://muses.bnu.edu.cn/">https://muses.bnu.edu.cn/</a>). This dataset is the MUSES global LAI product at 500m spatial resolution and 8-day temporal resolution. The MUSES LAI product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance product using general regression neural networks (GRNNs)  (Xiao et al., 2014; Xiao et al., 2016). The MUSES LAI product is spatially complete and temporally continuous. This dataset is the MUSES LAI product in 2012. Please <a href="https://zenodo.org/record/7613729#.Y-JCz3ZByUk">click here</a> to download the MUSES LAI product in 2011, and <a href="https://zenodo.org/record/7619109#.Y-OjTnZByUk">click here</a> to download the MUSES LAI product in 2013. Dataset Characteristics: <ul> <li>Spatial Coverage: Global</li> <li>Temporal Coverage: 2012</li> <li>Spatial Resolution: 500m</li> <li>Temporal Resolution: 8 days</li> <li>Projection: Sinusoidal</li> <li>Data Format: HDF</li> <li>Scale: 0.01</li> <li>Valid Range: 0 – 1000</li> </ul> Citation (Please cite this paper whenever these data are used): <ol> <li>Xiao Zhiqiang, et al. (2014). Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product From Time-Series MODIS Surface Reflectance. IEEE Transactions on Geoscience and Remote Sensing, 52, 209-223.</li> <li>Xiao Zhiqiang, et al. (2016). Long-time-series global land surface satellite leaf area index product derived from MODIS and AVHRR surface reflectance. IEEE Transactions on Geoscience and Remote Sensing, 54, 5301-5318.</li> <li>Xiao Zhiqiang, Jinling Song, Hua Yang, Rui Sun and Juan Li. (2022). A 250 m resolution global leaf area index product derived from MODIS surface reflectance data. International Journal of Remote Sensing, 43(4), 1199-1225.</li> <li>Xiao Zhiqiang, et al. (2017). Evaluation of four long time-series global leaf area index products. Agricultural and Forest Meteorology, 246, 218-230.</li> </ol> If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).

opencc-zeroFeb 2023View details →

Genomic evidence for contrasting patterns of host‐associated genetic differentiation across shared host‐plant species in leaf‐ and bud‐galling sawflies

Resource specialization and host-associated genetic differentiation (HAD) are frequently invoked as an explanation for the high diversity of plant-feeding insects and other organisms with a parasitic lifestyle. While genetic studies have demonstrated numerous examples of HAD in insect herbivores, the general rarity of comparative studies means that we still lack an understanding of how deterministic HAD is, and whether patterns of host shifts can be predicted over evolutionary time scales. We applied genome-wide SNP data obtained through low-coverage genome resequencing to define species limits and to compare host-plant use in population samples of leaf- and bud-galling sawflies collected from seven shared willow (Salix) host species. To infer the repeatability of long-term cophylogenetic patterns, we also contrasted the phylogenies of the two galler groups with each other as well as with the phylogeny of their Salix hosts estimated based on RADseq data. We found clear evidence for host specialization and HAD in both of the focal galler groups, but also that leaf gallers are more specialized to single host species than are most bud gallers. In contrast to bud gallers, leaf gallers also exhibit statistically significant cophylogenetic signal with their Salix hosts. The observed discordant patterns of resource specialization and long-term host use in two related galler groups that have radiated in parallel across a shared resource base indicate a general lack of evolutionary repeatability and suggest that short- and long-term host use and ecological diversification in plant-feeding insects are dominated by stochasticity and/or lineage-specific effects.

MUSES Leaf Area Index (LAI) 8-Day Global 500m SIN Grid in 2013

The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (<a href="https://muses.bnu.edu.cn/">https://muses.bnu.edu.cn/</a>). This dataset is the MUSES global LAI product at 500m spatial resolution and 8-day temporal resolution. The MUSES LAI product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance product using general regression neural networks (GRNNs)  (Xiao et al., 2014; Xiao et al., 2016). The MUSES LAI product is spatially complete and temporally continuous. This dataset is the MUSES LAI product in 2013. Please <a href="https://zenodo.org/record/7615278#.Y-MBsnZByUk">click here</a> to download the MUSES LAI product in 2012, and <a href="https://zenodo.org/record/7620773#.Y-RxGHZByUk">click here</a> to download the MUSES LAI product in 2014. Dataset Characteristics: <ul> <li>Spatial Coverage: Global</li> <li>Temporal Coverage: 2013</li> <li>Spatial Resolution: 500m</li> <li>Temporal Resolution: 8 days</li> <li>Projection: Sinusoidal</li> <li>Data Format: HDF</li> <li>Scale: 0.01</li> <li>Valid Range: 0 – 1000</li> </ul> Citation (Please cite this paper whenever these data are used): <ol> <li>Xiao Zhiqiang, et al. (2014). Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product From Time-Series MODIS Surface Reflectance. IEEE Transactions on Geoscience and Remote Sensing, 52, 209-223.</li> <li>Xiao Zhiqiang, et al. (2016). Long-time-series global land surface satellite leaf area index product derived from MODIS and AVHRR surface reflectance. IEEE Transactions on Geoscience and Remote Sensing, 54, 5301-5318.</li> <li>Xiao Zhiqiang, Jinling Song, Hua Yang, Rui Sun and Juan Li. (2022). A 250 m resolution global leaf area index product derived from MODIS surface reflectance data. International Journal of Remote Sensing, 43(4), 1199-1225.</li> <li>Xiao Zhiqiang, et al. (2017). Evaluation of four long time-series global leaf area index products. Agricultural and Forest Meteorology, 246, 218-230.</li> </ol> If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).

MUSES Leaf Area Index (LAI) 8-Day Global 500m SIN Grid in 2014

The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (<a href="https://muses.bnu.edu.cn/">https://muses.bnu.edu.cn/</a>). This dataset is the MUSES global LAI product at 500m spatial resolution and 8-day temporal resolution. The MUSES LAI product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance product using general regression neural networks (GRNNs)  (Xiao et al., 2014; Xiao et al., 2016). The MUSES LAI product is spatially complete and temporally continuous. This dataset is the MUSES LAI product in 2014. Please <a href="https://zenodo.org/record/7619109#.Y-OjTnZByUk">click here</a> to download the MUSES LAI product in 2013, and <a href="https://zenodo.org/record/7620771#.Y-WShnZByUk">click here</a> to download the MUSES LAI product in 2015. Dataset Characteristics: <ul> <li>Spatial Coverage: Global</li> <li>Temporal Coverage: 2014</li> <li>Spatial Resolution: 500m</li> <li>Temporal Resolution: 8 days</li> <li>Projection: Sinusoidal</li> <li>Data Format: HDF</li> <li>Scale: 0.01</li> <li>Valid Range: 0 – 1000</li> </ul> Citation (Please cite this paper whenever these data are used): <ol> <li>Xiao Zhiqiang, et al. (2014). Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product From Time-Series MODIS Surface Reflectance. IEEE Transactions on Geoscience and Remote Sensing, 52, 209-223.</li> <li>Xiao Zhiqiang, et al. (2016). Long-time-series global land surface satellite leaf area index product derived from MODIS and AVHRR surface reflectance. IEEE Transactions on Geoscience and Remote Sensing, 54, 5301-5318.</li> <li>Xiao Zhiqiang, Jinling Song, Hua Yang, Rui Sun and Juan Li. (2022). A 250 m resolution global leaf area index product derived from MODIS surface reflectance data. International Journal of Remote Sensing, 43(4), 1199-1225.</li> <li>Xiao Zhiqiang, et al. (2017). Evaluation of four long time-series global leaf area index products. Agricultural and Forest Meteorology, 246, 218-230.</li> </ol> If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).

MUSES Leaf Area Index (LAI) 8-Day Global 500m SIN Grid in 2016

The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (<a href="https://muses.bnu.edu.cn/">https://muses.bnu.edu.cn/</a>). This dataset is the MUSES global LAI product at 500m spatial resolution and 8-day temporal resolution. The MUSES LAI product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance product using general regression neural networks (GRNNs)  (Xiao et al., 2014; Xiao et al., 2016). The MUSES LAI product is spatially complete and temporally continuous. This dataset is the MUSES LAI product in 2016. Please <a href="https://zenodo.org/record/7620771#.Y-WShnZByUk">click here</a> to download the MUSES LAI product in 2015, and <a href="https://zenodo.org/record/7628028#.Y-bbI3ZByUk">click here</a> to download the MUSES LAI product in 2017. Dataset Characteristics: <ul> <li>Spatial Coverage: Global</li> <li>Temporal Coverage: 2016</li> <li>Spatial Resolution: 500m</li> <li>Temporal Resolution: 8 days</li> <li>Projection: Sinusoidal</li> <li>Data Format: HDF</li> <li>Scale: 0.01</li> <li>Valid Range: 0 – 1000</li> </ul> Citation (Please cite this paper whenever these data are used): <ol> <li>Xiao Zhiqiang, et al. (2014). Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product From Time-Series MODIS Surface Reflectance. IEEE Transactions on Geoscience and Remote Sensing, 52, 209-223.</li> <li>Xiao Zhiqiang, et al. (2016). Long-time-series global land surface satellite leaf area index product derived from MODIS and AVHRR surface reflectance. IEEE Transactions on Geoscience and Remote Sensing, 54, 5301-5318.</li> <li>Xiao Zhiqiang, Jinling Song, Hua Yang, Rui Sun and Juan Li. (2022). A 250 m resolution global leaf area index product derived from MODIS surface reflectance data. International Journal of Remote Sensing, 43(4), 1199-1225.</li> <li>Xiao Zhiqiang, et al. (2017). Evaluation of four long time-series global leaf area index products. Agricultural and Forest Meteorology, 246, 218-230.</li> </ol> If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).

opencc-zeroFeb 2023View details →

Different assembly mechanisms of leaf epiphytic and endophytic bacterial communities underlie their higher diversity in more diverse forests

Plant microbiomes are known to influence host fitness and ecosystem functioning, but mechanisms regulating their structure are poorly understood. Here, we explored the assembly mechanisms of leaf epiphytic and endophytic bacterial communities using a subtropical forest biodiversity experiment. Both epiphytic and endophytic bacterial diversity increased as host tree diversity increased. However, the increased epiphytic diversity in more diverse forests was driven by greater epiphytic diversity (i.e., greater α diversity) on individual trees, whereas the increased endophytic diversity in more diverse forests was driven by greater dissimilarity in endophytic composition (i.e., greater β diversity) among trees. Mechanistically, responses of epiphytes to changes in host diversity were consistent with mass effects, whereas responses of endophytes were consistent with species sorting. Synthesis: These results provided novel experimental evidence that biodiversity declines of plant species will lead to biodiversity declines of plant-associated microbiomes, but the underlying mechanism may differ between habitats on the plant host.

MUSES Leaf Area Index (LAI) 8-Day Global 500m SIN Grid in 2017

The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (<a href="https://muses.bnu.edu.cn/">https://muses.bnu.edu.cn/</a>). This dataset is the MUSES global LAI product at 500m spatial resolution and 8-day temporal resolution. The MUSES LAI product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance product using general regression neural networks (GRNNs)  (Xiao et al., 2014; Xiao et al., 2016). The MUSES LAI product is spatially complete and temporally continuous. This dataset is the MUSES LAI product in 2017. Please <a href="https://zenodo.org/record/7627209#.Y-YLynZByUk">click here</a> to download the MUSES LAI product in 2016, and <a href="https://zenodo.org/record/7488190#.Y-YM7C9Bw2y">click here</a> to download the MUSES LAI product in 2018. Dataset Characteristics: <ul> <li>Spatial Coverage: Global</li> <li>Temporal Coverage: 2017</li> <li>Spatial Resolution: 500m</li> <li>Temporal Resolution: 8 days</li> <li>Projection: Sinusoidal</li> <li>Data Format: HDF</li> <li>Scale: 0.01</li> <li>Valid Range: 0 – 1000</li> </ul> Citation (Please cite this paper whenever these data are used): <ol> <li>Xiao Zhiqiang, et al. (2014). Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product From Time-Series MODIS Surface Reflectance. IEEE Transactions on Geoscience and Remote Sensing, 52, 209-223.</li> <li>Xiao Zhiqiang, et al. (2016). Long-time-series global land surface satellite leaf area index product derived from MODIS and AVHRR surface reflectance. IEEE Transactions on Geoscience and Remote Sensing, 54, 5301-5318.</li> <li>Xiao Zhiqiang, Jinling Song, Hua Yang, Rui Sun and Juan Li. (2022). A 250 m resolution global leaf area index product derived from MODIS surface reflectance data. International Journal of Remote Sensing, 43(4), 1199-1225.</li> <li>Xiao Zhiqiang, et al. (2017). Evaluation of four long time-series global leaf area index products. Agricultural and Forest Meteorology, 246, 218-230.</li> </ol> If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).

opencc-zeroFeb 2023View details →

Data for: Steal the rain: Interception loses and rainfall partitioning by a broad-leaf and a fine-leaf woody encroaching species in a southern African semi-arid savanna

Woody plant encroachment (WPE) has been found to alter ecosystem functioning and services in savannas. In rain-limited savannas, increasing woody cover can reduce streamflow and groundwater by altering evapotranspiration rates and rainfall partitioning, but the ecological relevance of this impact is not well known. This study quantified the altered partitioning of rainfall by two woody plant structural types (fine- and broad-leaved trees) across a gradient of encroachment in a semi-arid savanna in South Africa. Averaged across both plant functional types, loss of rainfall through canopy interception and subsequent evaporation roughly doubled (from 20.5 to 43.6% of total rainfall) with a roughly 13-fold increase in woody cover (from 2.4 to 31.4 m2/ha tree basal cover). Spatial partitioning changes comprised fourfold increases in stemflow (from 0.8 to 3.9% of total rainfall) and a decline in throughfall proportion of about two-fifths (from 80.2% to 47.3% of total rainfall). Changes in partitioning were dependent on plant functional type; rainfall interception by the fine-leaved multi-stemmed shrub Dichrostachys cinerea was almost double that of the broad-leaved tree Terminalia sericea at the highest levels of woody encroachment (i.e., 49.7% vs 29.1% of total rainfall intercepted at tree basal area of 31.4 m2/ha). Partitioning was also dependent on rainfall characteristics, with the proportion of rainfall intercepted inversely related to rainfall event size and intensity. Therefore, increasing tree cover in African grassy ecosystems reduces the amount of canopy throughfall, especially beneath canopies of fine-leaved species in smaller rainfall events. Rainfall interception traits may thus confer a selective advantage, especially for fine-leaved woody plant species in semi-arid savannas.

Canopy height, rather than neighborhood effects, shapes leaf herbivory in a tropical rainforest

These files contain the datasets and R codes used in the data analyses in the paper " Canopy height, rather than neighborhood effects, shapes leaf herbivory in a tropical rainforest " that will be published in Ecology.

opencc-zeroDec 2022View details →

Data from: Seasonality in the equatorial tropics: Flower, fruit and leaf phenology of montane trees in the highlands of southwest Uganda

Phenology influences many forest functions and can inform forest conservation and management, yet representative phenological data for most common tropical forest tree species remain sparse or absent. Between June 2011 and December 2013, we investigated flowering, fruiting and leafing patterns in the Bwindi Impenetrable National Park, a montane forest located near the equator in Uganda, drawing on 16,410 observations of 530 trees of 54 species located between 2,066 and 2,527 m in elevation. The park's climate is equatorial with two wet and dry seasons each year. Flowering and fruiting were strongly seasonal while patterns in leafing were less pronounced. Flower occurrence peaked at the beginning of the short dry season followed by a pronounced trough during the beginning and the middle of the short wet season. Fruit occurrence had a pronounced peak during high rainfall months in March through April with most fruits ripening during drier months in May through July. Fruit scarcity was observed for a 4-month period spanning September to December and most flushing of leaves noted at the end of the wet season in November and December. Our binomial generalised linear mixed models (GLMM) indicated that flowering and fruiting were negatively associated with temperature and that leafing activity was positively associated with rainfall and temperature. These findings are consistent with the insolation- and water-limitation hypotheses suggesting that the seasonally varying availability of resources such as light, water and nutrients determines these phenological patterns. Ideally, prolonged, multi-year community-level studies would be supported so as to better characterise the influence of climate and of climate variability.

opencc-zeroMar 2023View details →

Widespread controls of leaf nutrient resorption by nutrient limitation and stoichiometry

Leaf nutrient resorption is a key process in nutrient cycles, but fundamental knowledge regarding its control mechanisms remains limited. Among the three proposed basic control mechanisms on leaf nutrient resorption, namely nutrient concentration control, nutrient limitation control, and stoichiometry control, only the first has been demonstrated to exist globally, while the latter two have not been systematically evaluated. Here, we conducted a global data synthesis to explore nutrient limitation and stoichiometry control on leaf resorption of carbon, nitrogen, phosphorus, sulfur, potassium, calcium, and magnesium, based on 3,395 data points from 109 peer-reviewed studies. Results showed that the nutrient limitation control existed globally, but was only applicable for the resorption of nitrogen, phosphorus, and potassium. The stoichiometry control existed globally for carbon and all studied nutrients, and coexisted with nutrient limitation control. Conifers' resorption relied primarily on stoichiometry control rather than nutrient limitation control. Nutrient limitation control was stronger in evergreen angiosperms than in deciduous angiosperms. Our findings support the ubiquity of both nutrient limitation and stoichiometry in controlling leaf resorption for multiple nutrients, such that these controls should be considered in Earth system models to better predict biogeochemical cycles in terrestrial ecosystems.

MUSES Leaf Area Index (LAI) Monthly Global 500m SIN Grid in 2018

The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (<a href="https://muses.bnu.edu.cn/">https://muses.bnu.edu.cn/</a>). This dataset is the MUSES global LAI product at 500m spatial resolution and monthly temporal resolution. The MUSES LAI product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance product using general regression neural networks (GRNNs)  (Xiao et al., 2014; Xiao et al., 2016). The MUSES LAI product is spatially complete and temporally continuous. This dataset is the MUSES LAI product in 2018. Please <a href="https://zenodo.org/record/7739941#.ZBMmtHZByUk">click here</a> to download the MUSES LAI product in 2017, and <a href="https://zenodo.org/record/7739123#.ZBKW4BRBw2x">click here</a> to download the MUSES LAI product in 2019. Dataset Characteristics: <ul> <li>Spatial Coverage: Global</li> <li>Temporal Coverage: 2018</li> <li>Spatial Resolution: 500m</li> <li>Temporal Resolution: 1 month</li> <li>Projection: Sinusoidal</li> <li>Data Format: HDF</li> <li>Scale: 0.01</li> <li>Valid Range: 0 – 1000</li> </ul> Citation (Please cite this paper whenever these data are used): <ol> <li>Xiao Zhiqiang, et al. (2014). Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product From Time-Series MODIS Surface Reflectance. IEEE Transactions on Geoscience and Remote Sensing, 52, 209-223.</li> <li>Xiao Zhiqiang, et al. (2016). Long-time-series global land surface satellite leaf area index product derived from MODIS and AVHRR surface reflectance. IEEE Transactions on Geoscience and Remote Sensing, 54, 5301-5318.</li> <li>Xiao Zhiqiang, Jinling Song, Hua Yang, Rui Sun and Juan Li. (2022). A 250 m resolution global leaf area index product derived from MODIS surface reflectance data. International Journal of Remote Sensing, 43(4), 1199-1225.</li> <li>Xiao Zhiqiang, et al. (2017). Evaluation of four long time-series global leaf area index products. Agricultural and Forest Meteorology, 246, 218-230.</li> </ol> If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).

MUSES Leaf Area Index (LAI) Monthly Global 500m SIN Grid in 2016

The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (<a href="https://muses.bnu.edu.cn/">https://muses.bnu.edu.cn/</a>). This dataset is the MUSES global LAI product at 500m spatial resolution and monthly temporal resolution. The MUSES LAI product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance product using general regression neural networks (GRNNs)  (Xiao et al., 2014; Xiao et al., 2016). The MUSES LAI product is spatially complete and temporally continuous. This dataset is the MUSES LAI product in 2016. Please <a href="https://zenodo.org/record/7742815#.ZBPPs3ZByUk">click here</a> to download the MUSES LAI product in 2015, and <a href="https://zenodo.org/record/7739941#.ZBMobhRBw2x">click here</a> to download the MUSES LAI product in 2017. Dataset Characteristics: <ul> <li>Spatial Coverage: Global</li> <li>Temporal Coverage: 2016</li> <li>Spatial Resolution: 500m</li> <li>Temporal Resolution: 1 month</li> <li>Projection: Sinusoidal</li> <li>Data Format: HDF</li> <li>Scale: 0.01</li> <li>Valid Range: 0 – 1000</li> </ul> Citation (Please cite this paper whenever these data are used): <ol> <li>Xiao Zhiqiang, et al. (2014). Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product From Time-Series MODIS Surface Reflectance. IEEE Transactions on Geoscience and Remote Sensing, 52, 209-223.</li> <li>Xiao Zhiqiang, et al. (2016). Long-time-series global land surface satellite leaf area index product derived from MODIS and AVHRR surface reflectance. IEEE Transactions on Geoscience and Remote Sensing, 54, 5301-5318.</li> <li>Xiao Zhiqiang, Jinling Song, Hua Yang, Rui Sun and Juan Li. (2022). A 250 m resolution global leaf area index product derived from MODIS surface reflectance data. International Journal of Remote Sensing, 43(4), 1199-1225.</li> <li>Xiao Zhiqiang, et al. (2017). Evaluation of four long time-series global leaf area index products. Agricultural and Forest Meteorology, 246, 218-230.</li> </ol> If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).

MUSES Leaf Area Index (LAI) Monthly Global 500m SIN Grid in 2019

The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (<a href="https://muses.bnu.edu.cn/">https://muses.bnu.edu.cn/</a>). This dataset is the MUSES global LAI product at 500m spatial resolution and monthly temporal resolution. The MUSES LAI product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance product using general regression neural networks (GRNNs)  (Xiao et al., 2014; Xiao et al., 2016). The MUSES LAI product is spatially complete and temporally continuous. This dataset is the MUSES LAI product in 2019. Please <a href="https://zenodo.org/record/7739325#.ZBLQNHZByUk">click here</a> to download the MUSES LAI product in 2018. Dataset Characteristics: <ul> <li>Spatial Coverage: Global</li> <li>Temporal Coverage: 2019</li> <li>Spatial Resolution: 500m</li> <li>Temporal Resolution: 1 month</li> <li>Projection: Sinusoidal</li> <li>Data Format: HDF</li> <li>Scale: 0.01</li> <li>Valid Range: 0 – 1000</li> </ul> Citation (Please cite this paper whenever these data are used): <ol> <li>Xiao Zhiqiang, et al. (2014). Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product From Time-Series MODIS Surface Reflectance. IEEE Transactions on Geoscience and Remote Sensing, 52, 209-223.</li> <li>Xiao Zhiqiang, et al. (2016). Long-time-series global land surface satellite leaf area index product derived from MODIS and AVHRR surface reflectance. IEEE Transactions on Geoscience and Remote Sensing, 54, 5301-5318.</li> <li>Xiao Zhiqiang, Jinling Song, Hua Yang, Rui Sun and Juan Li. (2022). A 250 m resolution global leaf area index product derived from MODIS surface reflectance data. International Journal of Remote Sensing, 43(4), 1199-1225.</li> <li>Xiao Zhiqiang, et al. (2017). Evaluation of four long time-series global leaf area index products. Agricultural and Forest Meteorology, 246, 218-230.</li> </ol> If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).

MUSES Leaf Area Index (LAI) Monthly Global 500m SIN Grid in 2017

The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (<a href="https://muses.bnu.edu.cn/">https://muses.bnu.edu.cn/</a>). This dataset is the MUSES global LAI product at 500m spatial resolution and monthly temporal resolution. The MUSES LAI product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance product using general regression neural networks (GRNNs)  (Xiao et al., 2014; Xiao et al., 2016). The MUSES LAI product is spatially complete and temporally continuous. This dataset is the MUSES LAI product in 2017. Please <a href="https://zenodo.org/record/7741430#.ZBOggnZByUk">click here</a> to download the MUSES LAI product in 2016, and <a href="https://zenodo.org/record/7739325#.ZBLTRRRBw2x">click here</a> to download the MUSES LAI product in 2018. Dataset Characteristics: <ul> <li>Spatial Coverage: Global</li> <li>Temporal Coverage: 2017</li> <li>Spatial Resolution: 500m</li> <li>Temporal Resolution: 1 month</li> <li>Projection: Sinusoidal</li> <li>Data Format: HDF</li> <li>Scale: 0.01</li> <li>Valid Range: 0 – 1000</li> </ul> Citation (Please cite this paper whenever these data are used): <ol> <li>Xiao Zhiqiang, et al. (2014). Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product From Time-Series MODIS Surface Reflectance. IEEE Transactions on Geoscience and Remote Sensing, 52, 209-223.</li> <li>Xiao Zhiqiang, et al. (2016). Long-time-series global land surface satellite leaf area index product derived from MODIS and AVHRR surface reflectance. IEEE Transactions on Geoscience and Remote Sensing, 54, 5301-5318.</li> <li>Xiao Zhiqiang, Jinling Song, Hua Yang, Rui Sun and Juan Li. (2022). A 250 m resolution global leaf area index product derived from MODIS surface reflectance data. International Journal of Remote Sensing, 43(4), 1199-1225.</li> <li>Xiao Zhiqiang, et al. (2017). Evaluation of four long time-series global leaf area index products. Agricultural and Forest Meteorology, 246, 218-230.</li> </ol> If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).