Remotely sensed data can help to identify both suitable habitat for individual species, and environmental conditions that foster species richness, which is important when predicting how biodiversity ...will respond to global change. The question is how to summarize remotely sensed data so that they are most relevant for biodiversity analyses, and the Dynamic Habitat Indices are three metrics designed for this. Our goals here were to a) derive, for the first time, the Dynamic Habitat Indices (DHIs) globally, and b) use these to evaluate three hypotheses (available energy, environmental stress, and environmental stability) that attempt to explain global variation in species richness of amphibians, birds, and mammals. The three DHIs summarize three key measures of vegetative productivity: a) annual cumulative productivity, which we used to evaluate the available energy hypothesis that more energy is associate with higher species richness; b) minimum productivity throughout the year, which we used to evaluate the environmental stress hypothesis that higher minima cause higher species richness, and c) seasonality, expressed as the annual coefficient of variation in productivity, which we used to evaluate the environmental stability hypothesis that less intra-annual variability causes higher species richness. We calculated the DHIs globally at 1-km resolution from MODIS vegetation products (NDVI, EVI, LAI, fPAR, and GPP), based on the median of the good observations of all years from the entire MODIS record for each of the 23 or 46 possible dates (8- vs. 16-day composites) during the year, and calculated species richness for three taxa (amphibians, birds, and mammals) at 110-km resolution from species range maps from the IUCN Red List. We found marked global patterns of the DHIs, and strong support for all three hypotheses. The three DHIs for a given vegetation product were well correlated (Spearman rank correlations ranging from −0.6 (cumulative vs. variation DHIs) to −0.93 (variation vs. minimum DHI)). Similarly, DHI components derived from different MODIS vegetation products were well correlated (0.8–0.9), and correlations of the DHIs with temperature and precipitation were moderate and strong respectively. All three DHIs were well correlated with species richness, showing in ranked order positive correlations for cumulative DHI based on GPP (Spearman rank correlations of 0.75, 0.63, and 0.67 for amphibians, resident birds, and mammals respectively) and minimum DHI (0.73, 0.83, and 0.62), and negative for variation DHI (−0.69, −0.83, and −0.59). Multiple linear models of all three DHIs explained 67%, 65%, and 61% of the variability in species richness of amphibians, resident birds, and mammals, respectively. The DHIs, which are closely related to well-established ecological hypotheses of biodiversity, can predict species richness well, and are promising for application in biodiversity science and conservation.
•The Dynamic Habitat Indices (DHIs) capture three aspects of annual productivity.•We derived DHIs from all MODIS vegetation products globally at 1-km resolution.•Cumulative, minimum, and variation DHIs all correlate well with species richness.•Relationships between species richness and the DHIs support ecological theory.•Amphibians and birds are best explained by DHIs, mammals least.
Animals select habitat at multiple spatial scales, suggesting that biodiversity modeling, for example of species richness, should be based on environmental data gathered at multiple spatial scales, ...and especially multiple grain sizes. Different satellite sensors collect data at different spatial resolutions and therefore provide opportunities for multi-grain habitat measures. The dynamic habitat indices (DHIs), which are derived from satellite data, capture patterns of vegetative productivity and predict bird species richness well. However, the DHIs have only been analyzed at single resolutions (e.g., 1-km), and have not yet been derived from high-resolution satellite data (< 10 -m). Our goal was to predict bird species richness based on measures of vegetation productivity (DHIs, NDVI median and NDVI percentile 90th) across a range of spatial resolutions both from different sensors, and from resampled high-resolution imagery. We analyzed bird species richness within 215 forest, grassland and shrubland plots (56.25 ha) located at 26 terrestrial field sites of the National Ecology Observatory Network (NEON), in the continental US. To obtain our multi-resolution measures of vegetation productivity, we acquired data from Planetscope (3-m), RapidEye (5-m), Sentinel-2 (10-m), Landsat-8 (30-m) and MODIS (250-m) from 2017 to 2020, generated time series of NDVI, calculated the three DHIs (cumulative, minimum and variation), NDVI median and the 90th percentile NDVI and calculated 1st and 2nd order texture measures. We evaluated the performance of the derived measures to predict bird species richness of habitat specialist guilds based on (i) univariate models (ii) multivariate models with single-resolution measures and (iii) multivariate models with multi-resolution measures. Single-spatial resolution measures predicted bird species richness moderately well (R2 up to 0.51) and the best performing spatial resolution and measure differed among bird species guilds. High-spatial resolution (3–5 m) measures outperformed medium-resolution measures (10–250 m). Models for all guilds performed best when incorporating multiple resolutions, including for all species richness (R2 = 0.63) and for forest (R2 = 0.72), grassland (R2 = 0.53) and shrubland specialists (R2 = 0.46). In addition, models based on multi-resolution data from different sensors performed better than models based on resampled high-resolution data for any of the guilds. Our results highlight, first, the value of the DHIs derived from high-resolution satellite data to predict bird species richness and, second, that remotely-sensed vegetation productivity measures from multiple spatial resolutions offer great promise for quantifying biodiversity.
•Single-spatial resolution measures predicted bird richness moderately well.•Best-performing spatial resolution and measure differed among bird species guilds.•High-resolution measures performed better than medium-resolution measures.•Multi-grain habitat models performed best for all bird species guilds.•Models based on original data performed better than resampled data models.
The subfornical organ (SFO) is one of the circumventricular organs (CVOs) of the mammalian nervous system responsible for maintaining the energy and water and sodium balance. Despite notable interest ...in the SFO and its physiological functions, the organization of individual populations of SFO cells, as well as their interactions, remain unclearly established. In this study, GABA and nitroxidergic systems of SFO using immunohistochemical (IHC) methods were examined. The brain of male Wistar rats at different stages of postnatal development—postnatal day 7 (P7), 14 (P14), and adult (4–6 months)—was examined. The obtained data allowed the authors to characterize changes in the activity of the GABA- and nitroxidergic systems of the SFO during development. In adult rats, three subpopulations of nitroxidergic cells, differing in the intensity of the reaction and tissue localization, can be distinguished. The revealed morphological heterogeneity of nitroxidergic cells in SFO may reflect their diverse functional status.
Cropland abandonment is a widespread land-use change, but it is difficult to monitor with remote sensing because it is often spatially dispersed, easily confused with spectrally similar land-use ...classes such as grasslands and fallow fields, and because post-agricultural succession can take different forms in different biomes. Due to these difficulties, prior assessments of cropland abandonment have largely been limited in resolution, extent, or both. However, cropland abandonment has wide-reaching consequences for the environment, food production, and rural livelihoods, which is why new approaches to monitor long-term cropland abandonment in different biomes accurately are needed. Our goals were to 1) develop a new approach to map the extent and the timing of abandoned cropland using the entire Landsat time series, and 2) test this approach in 14 study regions across the globe that capture a wide range of environmental conditions as well as the three major causes of abandonment, i.e., social, economic, and environmental factors. Our approach was based on annual maps of active cropland and non-cropland areas using Landsat summary metrics for each year from 1987 to 2017. We streamlined per-pixel classifications by generating multi-year training data that can be used for annual classification. Based on the annual classifications, we analyzed land-use trajectories of each pixel in order to distinguish abandoned cropland, stable cropland, non-cropland, and fallow fields. In most study regions, our new approach separated abandoned cropland accurately from stable cropland and other classes. The classification accuracy for abandonment was highest in regions with industrialized agriculture (area-adjusted F1 score for Mato Grosso in Brazil: 0.8; Volgograd in Russia: 0.6), and drylands (e.g., Shaanxi in China, Nebraska in the U.S.: 0.5) where fields were large or spectrally distinct from non-cropland. Abandonment of subsistence agriculture with small field sizes (e.g., Nepal: 0.1) or highly variable climate (e.g., Sardinia in Italy: 0.2) was not accurately mapped. Cropland abandonment occurred in all study regions but was especially prominent in developing countries and formerly socialist states. In summary, we present here an approach for monitoring cropland abandonment with Landsat imagery, which can be applied across diverse biomes and may thereby improve the understanding of the drivers and consequences of this important land-use change process.
•We propose an approach to map cropland abandonment using all available Landsat images.•A novel method is developed to generate training dataset semi-automatically.•Annual cropland maps are generated using Landsat spectral-temporal metrics.•Our approach is successful in most of 14 study regions across the globe.•Strong spatial and temporal variations exist in cropland abandonment.
Epiplexus (Kolmer) cells are a special population of phagocytic cells of the choroid plexus involved in maintaining the blood–cerebrospinal fluid barrier in the brain. In the present work, the ...structural organization of these cells was studied in Wistar, Wistar Kyoto, and spontaneously hypertensive (SHR) rats. A comparative immunohistochemical investigation using antibodies against macrophage markers Iba-1 and CD68 and intermediate filament protein vimentin showed that Kolmer cells in three studied groups of animals differ in their functional activity. In Wistar Kyoto and SHR rats, not only signs of activation of Kolmer cells were noted, visible in the disappearance of processes and cells acquiring a rounded shape, but also vimentin presence in activated cells. The obtained result indicates a relationship between vimentin expression and activation of brain phagocytic cells.
The aim of the research was to study morphological changes that occur in cortical catecholaminergic forebrain structures of Wistar rats during postnatal development. Rat’s forebrain sections at ...different stages of postnatal development (postnatal day 7, postnatal day 30, 4–6 months, and 23 months) were studied using immunohistochemistry methods. It has been shown that distinct cortical areas perform a unique distribution of catecholaminergic fibers due to their functional features. Age-related changes in density of the distribution of catecholaminergic fibers were analyzed, and it has been stated that the density of catecholaminergic fibers in the sensorimotor cortex increases with aging. It has been demonstrated that confocal laser scanning microscopy offers a wide variety of opportunities for qualitative and quantitative analysis of immunohistochemical results and can be a useful tool for tyrosine hydroxylase distribution studies.
Due to the high importance of investigating the subfornical organ and its tissue components and mediator systems, the aim of this work was to study the morphological features of the catecholaminergic ...innervation of this area. Using the methods of immunohistochemistry and confocal microscopy, preparations of the rat subfornical organ were studied on postnatal days 14 and 30 and at the age of 4–6 months. The main direction of the ingrowth of catecholaminergic fibers into the subfornical organ at the early stages was determined. It was found that the processes of catecholaminergic cells can contact the cells covering the subfornical organ and can penetrate through the ependymal layer. This allows them to contact the cerebrospinal fluid directly and, presumably, affect its composition. It is shown that some of the fibers run in parallel to the basal processes of specialized ependymal cells, tanycytes, which suggests their possible function as a scaffold for growing catecholaminergic fibers in postnatal development. This is the first study to demonstrate the presence of intrinsic catecholaminergic neurons in the subfornical organ.
—The aim of the study was to investigate brain GABAergic neurons and synaptic terminals in early postnatal development and aging. Wistar rat brain slices at different stages of postnatal development ...(from postnatal day 7 to 24 months) were used. Immunohistochemical staining of GABA synthesizing enzyme (glutamate decarboxylase isoform 67 or GAD67) was performed to reveal GABAergic structures. We described morphological changes that occur in the GABAergic system during postnatal development. In particular, it has been shown that synaptic terminals are predominantly localized in cortical layer 1 in 7-day-old animals. Our findings suggest that Cajal–Retzius cells are not GABAergic. GABAergic neurons were found in young and old animals in the subventricular zone. We found that the epithelial layer of choroid plexus contains GAD67 by the end of the first month of postnatal development. In this regard, these epithelial cells may be a source of extrasynaptic GABA, which enters the cerebrospinal fluid and then nervous tissue of the brain.
Addressing global declines in biodiversity requires accurate assessments of key environmental attributes determining patterns of species diversity. Spatial heterogeneity of vegetation strongly ...affects species diversity patterns, and measures of vegetation structure derived from lidar and satellite image texture analysis correlate well with species richness. Our goal here was to gain a better understanding of why image texture explains bird richness, by linking field-based measures of vegetation structure directly with both image texture and bird richness. In addition, we asked how image texture compares with lidar-based canopy height variability, and how sensor resolution affects the explanatory power of image texture. We generated texture metrics from 30 m (Landsat 8) and 10 m (Sentinel-2) resolution Enhanced Vegetation Index (EVI) imagery from 2017 to 2019. We compared textures with vegetation metrics and bird richness data from 27 National Ecological Observatory Network (NEON) terrestrial field sites across the continental US. Both 30 and 10 m resolution texture metrics were strongly correlated with lidar-based canopy height variability (|r| = 0.64 and 0.80, respectively). Texture was moderately correlated with field-based metrics, including variability of vegetation height and tree stem diameter, and foliage height diversity (range |r| = 0.31–0.52). Generally, 10 m resolution texture had stronger correlations with lidar and field-based metrics than 30 m resolution texture. In univariate linear models of total bird richness, 10 m resolution texture metrics also had higher explanatory power (up to R2adj = 0.45), than 30 m texture metrics (up to R2adj = 0.31). Among all metrics evaluated, the 10 m homogeneity texture was the best univariate predictor of total bird richness. In multivariate bird richness models that combined texture with lidar-based canopy height variability and field-based metrics, both 30 m and 10 m resolution texture metrics were selected in top-ranked models and independently contributed explanatory power (up to R2adj = 46%). Lidar-based canopy height variability was also selected in a top-ranked model of total bird richness, but independently contributed only 15% of the variance explained. Our results show satellite image texture characterized multiple features of structural and compositional vegetation heterogeneity, complemented more commonly used metrics in models of bird richness and for some guilds outperformed both lidar-based canopy height variability and field-based vegetation measurements. Ours is the first study to directly link image texture both to specific components of vegetation heterogeneity and to bird richness across multiple ecoregions and spatial resolutions, thereby shedding light on habitat features underlying the strong correlation between image texture and biodiversity.
•Image texture captures heterogeneity in both vegetation structure and composition.•10 m resolution texture outperforms 30 m texture in bird richness models.•Texture metrics outperform lidar canopy height variability in bird richness models.•Image texture has exciting potential for biodiversity research and conservation.
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