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opencc-by-4.0Nov 2022View details →

Fig. 2 in The detection of an alien pest, the cotton leaf roller Haritalodes decorata (Lepidoptera: Crambidae), on the Black Sea Coast of Russia

Fig. 2. Maximum likelihood COI tree illustrating intraspecific divergence of Haritalodes

opencc-by-4.0Nov 2022View details →

Fig. 3 in The detection of an alien pest, the cotton leaf roller Haritalodes decorata (Lepidoptera: Crambidae), on the Black Sea Coast of Russia

Fig. 3. Biology of Haritalodes derogata in Sochi, Yana Fabritsiusa str., 22–27.VIII

Enhanced isohydric behavior decoupled the whole-tree sap flux response to leaf transpiration under nitrogen addition in a subtropical forest

Anthropogenic nitrogen deposition has the potential to change the leaf water-use strategy in the subtropical region of China. Nevertheless, the whole-tree level response crucial for ecosystem functions has not been well addressed over the past decades. In this study, the stem sap flux density (JS) was monitored for the whole-tree water transport capacity in two dominant species (Schima superba and Castanopsis chinensis) in a subtropical forest. To simulate the increased nitrogen deposition, the NH4NO3 solutions were sprayed onto the forest canopy at 25 kg ha-1 year-1 (CAN25) and 50 kg ha-1 year-1 (CAN50), respectively, since April 2013. The JS and microclimate (monitored since January 2014) derived from the whole-tree level stomatal conductance (GS) were used to quantify the stomatal behavior (GS sensitive to vapor pressure deficit, GS-VPD) in response to the added nitrogen. The maximum shoot hydraulic conductance (Kshoot-max) was also measured for both species. After one year of monitoring in January 2015, the mid-day (JS-mid) and daily mean (JS-mean) sap flux rates did not change under all the nitrogen addition treatments (p > 0.05). A consistent decline in the GS-VPD indicated an enhanced isohydric behavior for both species. In addition, the GS-VPD in the wet season was much lower than that in the dry season. S. superba had a lower GS-VPD and decreased JS-mid/JS-mean, implying a stronger stomatal control under the fertilization, which might be attributed to the low efficient diffuse-porous conduits and a higher JS. In addition, the GS for S. superba decreased and the GS-VPD increased more under CAN50 than that under CAN25, indicating that the high nitrogen dose restrains the extra nitrogen benefits. Our results indicated that the JS for both species was decoupled from the leaf transpiration for both species due to an enhanced isohydric behavior, and a xylem anatomy difference and fertilization dose would affect the extent of this decoupling relation.

Divergent response and adaptation of specific leaf area to environmental change at different spatio-temporal scales jointly improve plant survival

Specific leaf area (SLA) is one of the most important plant functional traits. It integrates multiple functions and reflects strategies of plants to obtain resources. How plants employ different strategies (e.g., through SLA) to respond to dynamic environmental conditions remains poorly understood.This study aimed to explore the spatial variation in SLA and divergent adaptation of plants by changing SLA through the lens of biogeographic patterns, evolutionary history, and short-term responses. SLA data for 5424 plant species from 76 natural communities in China were systematically measured and integrated with meta-analysis of field experiments (i.e., global warming, drought, and nitrogen addition). The mean value of SLA across all species was 21.8 m2 kg–1, ranging from 0.9 to 110.2 m2 kg–1. SLA differed among different ecosystems, temperature zones, vegetation types, and functional groups. Phylogeny had a weak effect on SLA, but plant species evolved toward higher SLA. Furthermore, plants responded nonlinearly to environmental change by changing SLA. Unexpectedly, radiation was one of the main factors determining the spatial variation in SLA on a large scale. Conversely, short-term manipulative experiments showed that SLA increased with increased resource availability and tended to stabilize with treatment duration. However, different species exhibited varying response patterns. Overall, long-term adaptation of plants to environmental gradients and its short-term response to resource pulses through variation in SLA jointly improve plant adaptability to a changing environment. Overall SLA-environment relationships should be emphasized as a multidimensional strategy for elucidating environmental change in future research.

Data for the article: Coupling of leaf elemental traits with root fungal community composition reveals a plant resource acquisition strategy in a desert ecosystem

Purpose: Plant-associated microbes enhance nutrient access and stress tolerance of the host species, and therefore, are crucial for plant traits and resource strategies. However, the links between aboveground plant traits and belowground microbes related to plant resource strategies under stressful conditions remain poorly understood. Methods: We tested the relationships between leaf traits linked to water (carbon isotopic composition, δ13C) and nutrient use (elemental concentrations and stoichiometry) with microbial compositions in roots and rhizospheres of two dominant species (Artemisia ordosica and Leymus secalinus) in the Mu Us Desert, northern China. Results: L. secalinus exhibited higher Mg and Mn concentrations, N:P ratios, stoichiometric flexibility, and root fungi:bacteria ratios, but lower foliar K and Ca concentrations and δ13C values than A. ordosica. The leaf N:P of L. secalinus increased with the root fungi:bacteria ratios, whereas the leaf N:P of A. ordosica decreased with the root fungi:bacteria ratios. The plant elemental levels (P, N, K, Ca, Mn, and δ13C) of L. secalinus but not A. ordosica were significantly related to their root fungal composition. Additionally, the random forest model identified four key fungal families in predicting leaf elemental traits for both plant species. Conclusion: The results suggested tight coupling and coordination between leaf elemental traits and root microbial compositions (especially fungal communities) related to plant resource acquisition strategies. By regulating aboveground and belowground feedback loops through trait flexibility and root microbial compositions, the studied plant species can sustain their resource strategies under stressful environmental conditions.

Lianas rapidly colonize early stages of tropical forests, presumably through leaf trait diversification

Questions: Ecological succession is the process during which ecosystems recover after disturbances. Studies investigating community re-assembly during tropical forest succession have rarely compared lianas to trees. We addressed two questions: (1) How do changes in stem density, total basal area, and species richness of lianas and trees compare throughout a secondary succession, and to what extent does the relative basal area of lianas change along a secondary succession? (2) How do the successional trajectories of functional community trait values of lianas and trees compare? Location: Yoko forest reserve, central Congo basin. Methods: Using univariate Bayesian modeling techniques, we analyzed differences in successional pathways between lianas and trees in terms of community structure, and functional assembly in a replicated chronosequence spanning from young to old-growth forests. Results: We found divergent structural trajectories between lianas and trees along the forest chronosequence. The stem density of lianas peaked at the intermediate stage, while that of trees almost linearly decreased from the early to late stages of succession. The basal area of lianas increased at a higher rate than that of trees, which translated into a marginal increase of liana relative basal area over succession. On the contrary, we observed a lower rate of increase in species richness for lianas than trees over succession. We found a progressive convergence in the responses of lianas and trees to changes with succession in terms of specific leaf area and leaf nitrogen content, but a diverging response in terms of leaf phosphorus content. These functional composition patterns most probably resulted from environmental filtering, induced by a change from nitrogen to phosphorus limitation as the succession progressed to mature forest. Conclusions: These findings underscore the rapid colonization of tropical forests by lianas after agricultural abandonment, presumably by deploying a more diverse leaf economic spectrum early in succession.

Green synthesis of ZnO nanoparticles using Cocos nucifera leaf extract: Characterization, antimicrobial, antioxidant, and photocatalytic activity

Zinc oxide nanoparticles (ZnO NPs) have been successfully prepared using Cocos nucifera leaf extract and investigated with their antimicrobial, antioxidant, and photocatalytic activity. The structural, compositional, and morphological properties of the NPs were recorded and studied systematically to confirm the synthesis. The aqueous suspension of NPs showed a UV-Vis. absorption maxima of 370 nm indicating primarily its formation. The XRD analysis identified the NPs with hexagonal wurtzite structure with an average particle size of 16.6 nm. The FTIR analysis identified some biomolecules and functional groups in the leaf extract as responsible for the encapsulation and stabilization of ZnO NPs. The EDX analysis showed the desired elemental compositions in the material. A flower-shaped morphology of ZnO NPs was observed by SEM with a grain size of around 15 nm. The optical properties of the NPs were studied by UV-Vis spectroscopy and the bandgap was calculated as 3.37 eV. The Prepared ZnO NPs have demonstrated antimicrobial activity against T. harzianum, and S. aureus with a ZOI (zone of inhibition) of 14 and 10 mm, respectively. The photocatalytic behavior of ZnO NPs showed absorbance degradation at around 640 nm and discolored methylene blue dye after one hour with a degradation maximum of 84.29 %. Thus, the prepared ZnO NPs could be used potentially in antibiotic development, pharmaceutical industries, and as photocatalysts.

opencc-by-4.0Dec 2021View details →

Measurements of natural radioactivity levels and associated radiation hazard indices in Moringa plant leaf samples from selected areas in southern Ethiopia.

Moringa stenopetala (MS) is a multipurpose tree whose leaf is consumed by people of all ages in southern Ethiopia. In this study, natural radioactivity levels of 226Ra, 232Th, and 40K, as well as the related dangerous radiological characteristics, were measured on different sites of moringa samples using high-purity germanium (HPGe) gamma-ray spectrometry. Average activity concentrations of 226Ra, 232Th, and 40K in moringa leaves are found to be 0.49 ± 0.12, 3.84 ± 1.12, and 573.29 ± 26.79 Bq.kg-1, respectively. Moreover, the average values of the corresponding radiological parameters, Raeq, Hint, and annual effective dose (Eave) (sum), were also found to be 50.13 ± 3.79 Bq.kg-1, 0.136 ± 0.010, and 0.238 ± 0.089 mSvy-1, respectively. When the obtained results for all of the samples were compared to globally accepted criteria, they were discovered to be lower than the allowable world average levels. Furthermore, the lifetime cancer risk was revealed to be far lower than the allowable limit. According to the study, the risk of absorbing natural radionuclides from moringa leaf ingestion is insignificant. The findings can be utilized to create radiation safety norms and regulations for the use of moringa leaves as food and medication.

opencc-zeroDec 2022View details →

Coordination of economics spectra in leaf, stem and root within the genus Artemisia along a large environmental gradient in China

Aim: The plant economics spectrum provides a fundamental framework for understanding functional trait variation along environmental gradients. However, it is unclear whether there is a general whole-plant economics spectrum across organs at the finer taxonomic scale (e.g. within genera), and if there is, which factors affect the trait coordination of the different organs. Here, we examined whether resource economics spectra of different organs (i.e. leaf, stem and root) can be integrated at the whole-plant level within a single genus, and how environment, intraspecific variation and taxonomic scale shape the whole-plant spectrum. Location: China. Time period: 2018. Major taxa studied: Artemisia. Results: Pairwise trait correlations and the trade-off patterns along the resource economic axis were consistent at both organ and whole-plant levels. Environmental gradients did not strongly affect the correlations among leaf, stem and root economics spectra, i.e. the intraspecific variation weakened but did not mask this coordination. Taxonomic scale did not affect the degree of trait coordination as the genus-wide whole-plant economics spectrum also emerged within each of the three subgenera. Main conclusions: Our results support the hypothesis that the coordination of economics spectra across organs forms a whole-plant economics spectrum representing "fast-slow" resource management strategy, which is robust to recent evolution (genotypic variation, even for species within a single genus) and present-day environmental variation. Further studies should elucidate in which circumstances or phylogenetic branches the coordinated pattern found for Artemisia is representative of other widely distributed genera.

opencc-zeroDec 2022View details →

Parameters of leaf phenology for North American forests

Leaf phenology regulates multiple aspects of plant vital activities and provides feedback to climate change. Despite its importance, an effective parameterization method to predict continental-scale leaf phenology has been elusive. Here, we developed a new parameterization method using local climatic conditions instead of species or plant function types to calibrate the phenology parameters of forests. We provided the parameters of phenology include: the effective temperature threshold (TA) to determine spring phenology (i.e. start of the season; SOS); the critical heat threshold (SA) to determine SOS; the effective temperature threshold (TB) to determine autumn phenology (i.e. end of the season; EOS); the effective photoperiod threshold (Pstart) to determine EOS, and the critical chilling threshold (SB) to determine EOS.

opencc-by-4.0Dec 2022View details →

MUSES Leaf Area Index (LAI) 8-Day Global 1km 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 1km 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 2019. Please <a href="https://zenodo.org/record/7485123#.Y6p12n1ByUl">click here</a> to download the MUSES LAI product in 2018, and click here to download the MUSES LAI product in 2020. Dataset Characteristics: <ul> <li>Spatial Coverage: Global</li> <li>Temporal Coverage: 2019</li> <li>Spatial Resolution: 1km</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-by-4.0Dec 2022View details →

MUSES Leaf Area Index (LAI) 8-Day Global 1km 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 1km 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 2018.  Please <a href="https://zenodo.org/record/7578514#.Y9UHGXZByUk">click here</a> to download the MUSES LAI product in 2017, and <a href="https://zenodo.org/record/7483992#.Y6pzZNVBw2x">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: 1km</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-zeroDec 2022View details →

Data for: Earlier leaf senescence dates are constrained by soil moisture

The unprecedented warming that has occurred in recent decades has led to later autumn leaf senescence dates (LSD) throughout the Northern Hemisphere. Yet, great uncertainties still exist regarding the strength of these delaying trends, especially in terms of how soil moisture affects them. Here we show that changes in soil moisture in 1982–2015 had a substantial impact on autumn LSD in one fifth of the vegetated areas in the Northern Hemisphere (>30° N), and how it contributed more to LSD variability than either temperature, precipitation or radiation. We developed a new model based on soil-moisture-constrained cooling degree days (CDDSM) to characterize the effects of soil moisture on LSD and compared its performance with the cooling degree days (CDD), Delpierre (DM) and spring-influenced autumn (SIAM) models. We show that the CDDSM model with inputs of temperature and soil moisture outperformed the three other models for LSD modelling and had an overall higher correlation coefficient (R), a lower root mean square error (RMSE) and lower Akaike information criterion (AIC) between observations and model predictions. These improvements were particularly evident in arid and semi-arid regions. We studied future LSD using the CDDSM model under two scenarios (SSP126 and SSP585) and found that predicted LSD was 4.1±1.4 days and 5.8±2.8 days earlier under SSP126 and SSP585, respectively, than other models for the end of this century. Our study therefore reveals the importance of soil moisture in regulating autumn LSD and, in particular, highlights how coupling this effect with LSD models can improve simulations of the response of vegetation phenology to future climate change.

opencc-zeroJan 2023View details →

Chionochloa rubra leaf litter decomposition at Mt Tongariro, New Zealand

Concept: Decomposition rates are an important component of carbon sequestration rates in soils, potentially mitigating future climate change. Here we aim to better understand decomposition's relationship with temperature in natural conditions. Structure: In snow-tussock grassland dominated by Chionochloa rubra var. rubra on Mount Tongariro, Tongariro National Park, New Zealand, we measured decomposition of Chionochloa leaf litter along an ~ 700 m altitudinal gradient, as a space-for-temperature experiment, representing 4.2ºC of warming. For litter, we haphazardly collected attached, but achlorophyllous leaves, which were cut into 5 cm long segments and pooled per site. We examined decomposition rates in a full reciprocal translocation of litter bags between 8 plots as both the origin of 8 litter types and the 8 destinations of plating out of litter bags, over 4 years using 6 replicates. Litter decomposition bags, 20 cm x 25 cm in size, were of black nylon rectangular mesh with a pore size of 2 mm. We used 3.00 g of litter per bag, applied a wet/dry weight correction, and measured litter remaining after each time period. Bag recovery was 91 %. We went on to model decomposition's relationships to environmental variates. Results: Litter decomposed progressively over time, but at the same rate along the altitudinal gradient. There was no home-field advantage. In terms of litter quality, decomposition rates were related only to litter lignin, or fibre or litter N. Only decomposition at Year 4, and that only when organised by litter destination, showed a relationship to mean annual temperature jointly with soil C, and this was only weak and implausible. When studied across the full reciprocal transplant, there were no significant interactions between Origin and Destination data with or without Years. Therefore litter from each plot decomposed at the same rate as other plots' litter at all altitudes, allowing for small, often irregular differences in litter quality and micro-environment.

Leaf decomposition, flammability and functional trait data for tropical swamp forest tree species

Decomposition and fire are major carbon pathways in many ecosystems, yet the contribution of species identity to these processes can be difficult to predict. Plant decomposability and flammability have usually been studied separately but could be linked through shared predictive traits. We explored how decomposability and flammability were related to each other and to key plant functional traits in a tropical swamp forest in Singapore. Full methodological details in situ decomposition experiment in Nee Soon freshwater swamp forest, Singapore, laboratory flammability experiment, and leaf functional trait measurements can be found in the published article and supporting information stated below. Nur E. B. Rahman, Stuart W. Smith, Weng Ngai Lam, Kwek Yan Chong, Matthias S. E. Chua, Pei Yun Teo, Daniel W. J. Lee, Shi Yu Phua, Cheryl Y. Aw, Janice S. H. Lee, David A. Wardle. Leaf decomposition and flammability are largely decoupled across species in a tropical swamp forest despite sharing some predictive leaf functional traits. New Phytologist In this data repository, we have uploaded the following decomposition, flammability and trait data as well as secondary data used in our statistical analyses to generate the findings presented in the paper. Specific datasets include the following: <ul> <li>litter_mass_loss.csv : raw data of leaf litterbag dry masses before and after 1 year in situ decomposition experiment in Nee Soon Swamp Forest</li> <li>flammability_leaf_temperature.csv : raw data of temperature recorded during flammability experiments of leaf litter and fresh leaves</li> <li>flammability_timings.csv : raw data of timings of flammability events, namely smouldering and pyrolysis recorded from video footage of flammability experiments</li> <li>senesced_leaf_dryweights_area.csv : senesced leaf raw data for calculating physical traits</li> <li>senesced_leaf_dryweights.csv: senesced leaf dry weights raw data</li> <li>freshtraits_measurements.csv: fresh leaf raw data for calculating physical traits</li> <li>decomposition_constants.csv: derived decomposition constants (k) for each species from the analysis of decomposition experiments.</li> <li>functional_traits_z_standardized.csv : all traits required for the analysis, consolidated following z-standardized transformation</li> <li>functional_traits_untransformed_decomposition_flammability.csv : all traits required for the analysis, untransformed (for back transforming axis labels) and species decomposition and flammability variables</li> </ul> Raw leaf litter mass loss and leaf flammability data are associated meta-data file explaining the column headers and variables. For all other datasets please refer to the paper and supporting information.

opencc-zeroJan 2023View details →

Incorporating pressure-volume traits into the leaf economics spectrum

Dataset associated with the article 'Incorporating pressure-volume traits into the leaf economics spectrum' (Nadal et al. 2023 [Eco Lett, accepted]). We provide theoretical and empirical evidence for the coordination of the turgor loss point and associated pressure-volume traits (osmotic potential at full turgor, leaf capacitance, leaf elasticity, leaf saturated water content, modulus of elasticity) with photosynthesis (light-saturated net CO2 assimilation) and leaf mass per area (LMA) and its two components (leaf thickness and density). The complete dataset comprehends a total of 45 data entries from 37 species, including ferns and angiosperms (woody and herbaceous species), most of them measured on the campus of University of the Balearic Islands; this complements the data presented in Nadal et al. (2018 [Eco Lett 21 (9), 1372-1379]). For the 20 species from Nadal et al. (2018), we also provide data regarding their N and C content, leaf anatomy (from optical and TEM images) and cell wall composition.

opencc-zeroJan 2023View details →

Data from: Leaf-cutting ant nests support less dense and impoverished seed assemblages in a human-modified Caatinga dry forest

Regenerating forests make up an increasingly large portion of tropical landscapes worldwide and regeneration dynamics may be influenced by leaf-cutting ants (LCA), which proliferate in disturbed areas and collect seeds for fungus culturing. Here we investigate how LCA influence seed fate in human-modified areas of Caatinga dry forest. We evaluate the seed deposition and predation on Atta opaciceps nests, foraging habitat surrounding nest and control habitat away of nest influence of 15 colonies located along a forest cover gradient during the rainy and dry season. For each habitat, four 50-cm2 plots were established and all seeds on the soil surface were collected along one year. We recorded 13,628 seeds distributed among 47 species and 36.57% of the total seeds did not show any sign of predation. Nest mound habitats supported low-density and species-poor seed assemblages, which were taxonomically distinct from the control habitats. These effects only occurred in the rainy season. The proportion of undamaged seeds were similar across the habitats. While forest cover did not influence seed assemblage in terms of species richness or seed predation, it did interact with habitat type via increments in seed abundance as forest cover increased across the nests. Forest cover also affected seed composition, but only in the rainy season. These results indicate that LCA decrease seed deposition in areas under their influence, particularly on the nest mounds. As LCA profit from human disturbance in the Caatinga, their role as seed 'sinks' should be enhanced in disturbed Caatinga patches, particularly during the rainy season, when most of the plant recruitment occurs. Our findings reinforce the importance of LCA as drivers of forest dynamics and resilience in human-modified landscape.

MUSES Leaf Area Index (LAI) 8-Day Global 1km 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 1km 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/7578885#.Y9Ut8XZByUk">click here</a> to download the MUSES LAI product in 2016, and <a href="https://zenodo.org/record/7485123#.Y9TTcGBBw2z">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: 1km</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 1km 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 1km 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/7580620#.Y9ZA03BBw2x">click here</a> to download the MUSES LAI product in 2011, and <a href="https://zenodo.org/record/7580435#.Y9X8u3BBw2w">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: 1km</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 1km 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 1km 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/7580622#.Y9YcS3ZByUk">click here</a> to download the MUSES LAI product in 2012, and <a href="https://zenodo.org/record/7580098#.Y9XbGHBBw2x">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: 1km</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).