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212 results for “landscape changes”
Fig. 3 in Historical biogeography highlights the role of Miocene landscape changes on the diversification of a clade of Amazonian tree frogs
Fig. 3 Dorsolateral views of Osteocephalus. a O. alboguttatus sensu stricto, QCAZ15972, Puyo-Canelos Road, Pastaza, Ecuador. b O. mimeticus sensu stricto, AF4442 (sequenced), San José, San Martín, Peru. c O. buckleyi sensu stricto, Canelos, Pastaza, Ecuador. d O. vilmae, QCAZ51205, Pompeya-Iro Road km 80, Orellana, Ecuador. e O. mutabor, QCAZ56066, Lorocachi, Pastaza, Ecuador. f O. mutabor, QCAZ39588, Río Pucayacu, Pastaza, Ecuador. g O. taurinus sensu stricto, Reserva Ducke, Amazonas, Brazil. h O. oophagus sensu stricto, Reserva Ducke, Amazonas, Brazil. Photographs: a, d–f by Santiago Ron (BIOWEB, Licence: CC BY-NC-ND 4.0); b by Antoine Fouquet; c, g–h by Diego Ortiz
Dataset from "Combined Landsat and L-Band SAR Data Improves Land Cover Classification and Change Detection in Dynamic Tropical Landscapes"
<p>These are the output land cover and land cover change raster maps from the paper, "<a href="https://doi.org/10.3390/rs10020306">Combined Landsat and L-Band SAR Data Improves Land Cover Classification and Change Detection in Dynamic Tropical Landscapes</a>," published in Remote Sensing journal.</p> <p>ABSTRACT. Robust quantitative estimates of land use and land cover change are necessary to develop policy solutions and interventions aimed towards sustainable land management. Here, we evaluated the combination of Landsat and L-band Synthetic Aperture Radar (SAR) data to estimate land use/cover change in the dynamic tropical landscape of Tanintharyi, southern Myanmar. We classified Landsat and L-band SAR data, specifically Japan Earth Resources Satellite (JERS-1) and Advanced Land Observing Satellite-2 Phased Array L-band Synthetic Aperture Radar-2 (ALOS-2/PALSAR-2), using Random Forests classifier to map and quantify land use/cover change transitions between 1995 and 2015 in the Tanintharyi Region. We compared the classification accuracies of single versus combined sensor data, and assessed contributions of optical and radar layers to classification accuracy. Combined Landsat and L-band SAR data produced the best overall classification accuracies (92.96% to 93.83%), outperforming individual sensor data (91.20% to 91.93% for Landsat-only; 56.01% to 71.43% for SAR-only). Radar layers, particularly SAR-derived textures, were influential predictors for land cover classification, together with optical layers. Landscape change was extensive (16,490 km<sup>2</sup>; 39% of total area), as well as total forest conversion into agricultural plantations (3,214 km<sup>2</sup>). Gross forest loss (5,133 km<sup>2</sup>) in 1995 was largely from conversion to shrubs/orchards and tree (oil palm, rubber) plantations, and gross gains in oil palm (5,471 km<sup>2</sup>) and rubber (4,025 km<sup>2</sup>) plantations by 2015 were mainly from conversion of shrubs/orchards and forests. Analysis of combined Landsat and L-band SAR data provides an improved understanding of the associated drivers of agricultural plantation expansion and the dynamics of land use/cover change in tropical forest landscapes.</p>
Data from: Using Landsat time-series to investigate nearly 50 years of tree canopy cover change across an urban-rural landscape in southern Ontario
<p><strong>Paper Abstract:</strong></p> <p>Canadian urban and adjacent landscapes have been dynamic over the last 50 years due to land management, land cover alternations, climate change, and disturbances. Remote sensing, particularly the Landsat archive, provides the only means to spatially quantify these long-term dynamics locally. Here, we explore the utility of Landsat, including the often-forgotten MSS sensor, for investigating percent tree canopy cover (TCC) change between 1972 and 2020 in a Canadian urban-rural context. We build a TCC time-series by training random forest models using visually interpreted TCC from high-resolution imagery. Predictors include topographic and yearly LandsatLinkr-harmonized and LandTrendr-fitted tasseled cap indices. Yearly binary TCC maps are built to mask consistently treeless areas and limit noise. To increase confidence in observed TCC change without historical reference imagery, we investigate multiple temporal validation options. Our TCC time-series (R2: 0.89, RMSE: 10.7%), quantifies TCC dynamics while limiting erroneous change and predictor space extrapolation. We explore TCC changes across landscapes, revealing periods of gain and loss associated with agricultural reforestation (1978-1996), housing development (on-going), drought (late 1990s), emerald ash borer (2010s), an ice storm (2013), and other drivers. Results demonstrate how long-term Landsat time-series can be used to better understand historical tree canopy change at local-regional scales. </p> <p> </p> <p><strong>Dataset details:</strong></p> <p>See paper. </p> <ul> <li>cc_72to20.tif: Yearly tree CC predictions (1972-2020)</li> <li>always_nonforest10_nowater.tif: continuous-non-canopy mask</li> <li>water.tif: water mask</li> <li>Yearly.zip: Annual predictors (including CC10) and asc outputs</li> </ul> <p> </p> <p>See code on GitHub: <a href="https://github.com/ZZMitch/PredictTreeCC_Landsat_1972to2020">ZZMitch/PredictTreeCC_Landsat_1972to2020: Code from the portion of my PhD about using Landsat time-series to predict tree canopy cover from 1972 - 2020. Code will be released as papers are published. (github.com)</a></p>
Influence of land use changes on landscape connectivity for North China leopard (Panthera pardus japonensis)
<p><span>North China leopard (<em>Panthera pardus japonensis</em></span><span>), is the most wid</span><span>espread subspecies of leopard and one of the rare and endangered species in China. It is currently confined to several isolated natural reserves, and little is known about its habitat network connectivity with land use changes. This study was conducted to assess the impacts of land use changes on landscape connectivity for North China leopards in the Great Taihang Region. Circuit theory-based connectivity models and least-cost path analyses were used to delineate pathways suitable for species movement, and evaluate the impacts of land use changes on landscape connectivity. The results revealed that there were 37 least-cost paths in 1990 and 38 in 2020. The area of forest land increased from 57142.74 km<sup>2</sup> to 74836.64 km<sup>2</sup>, with the percentage increasing from 26.61% to 34.85%. In general, the increase of forest land area promoted landscape connectivity for North China leopards at broad spatial scales. The improvement of landscape connectivity was not always consistent with the land use changes, and there was a slightly decreasing trend in connectivity in some key movement barrier areas </span><span>with high-intensity of human activities</span><span>. Improving landscape connectivity at broad spatial scales is as important as protecting the habitats (natural reserves) where the species lives. Our study can serve as an example of exploring the relationships between land use changes and landscape connectivity for species conservation at broad spatial scales with limited movement pattern data. This information is proved to be critical for enhancing landscape connectivity for the conservation concerns of North China leopard and planning of natural reserves network.</span></p>
Data for: Changes in evapotranspiration, transpiration and evaporation across natural and managed landscapes in the Amazon, Cerrado and Pantanal biomes
<p>This dataset contains measurements of evapotranspiration and other meteorological variables (net radiation, air temperature, vapor pressure deficit, etc) from nine eddy covariance towers located in different ecosystems in the Amazon (natural Amazon forest, cropland and pastureland), Cerrado (natural savannah, irrigated and rainfed croplands) and Pantanal (natural forest, pastureland) biomes. It also contains estimates of transpiration that were calculated using two different approaches, the transpiration estimation algorithm (TEA) and the underlying water use efficiency method (uWUE).</p>
Concordant and opposing effects of climate and land-use change on avian assemblages in California's most transformed landscapes
<p>Climate and land-use change could exhibit concordant effects that favor or disfavor the same species, which would amplify their impacts, or species may respond to each threat in a divergent manner, causing opposing effects that moderate their impacts in isolation. We used early 20th-century surveys of birds conducted by Joseph Grinnell paired with modern resurveys and land-use change reconstructed from historic maps to examine avian change in Los Angeles and California's Central Valley (and their surrounding foothills). Occupancy and species richness declined greatly in Los Angeles from urbanization, strong warming (+1.8°C) and drying (-77.2 mm), but remained stable in the Central Valley, despite large-scale agricultural development, average warming (+0.9°C), and increased precipitation (+11.2 mm). While climate was the main driver of species distributions a century ago, the combined impacts of land-use and climate change drove temporal changes in occupancy, with similar numbers of species experiencing concordant and opposing effects.</p>
Interspecific differences in thermal tolerance landscape explain aphid community abundance under climate change
<p>A single critical thermal limit is often used to explain and infer the impact of climate change on geographic range and population abundance. However, it has limited application in describing the temporal dynamic and cumulative impacts of extreme temperatures. Here, we used a thermal tolerance landscape approach to address the impacts of extreme thermal events on the survival of co-existing aphid species (<em>Metopolophium dirhodum, Sitobion avenae </em>and<em> Rhopalosiphum padi</em>). Specifically, we built the thermal death time (TDT) models based on detailed survival datasets of three aphid species with three ages across a broad range of stressful high (34–40 ÅãC) and low (−3∼-11 ÅãC) temperatures to compare the interspecific and developmental stage variations in thermal tolerance. Using these TDT parameters, we performed a thermal risk assessment by calculating the potential daily thermal injury accumulation associated with the regional temperature variations in three wheat-growing sites along a latitude gradient. Results showed that <em>M</em>. <em>dirhodum</em> was the most vulnerable to heat but more tolerant to low temperatures than <em>R. padi </em>and<em> S. avenae. R. padi</em> survived better at high temperatures than <em>Sitobion avenae </em>and<em> M. dirhodum</em> but was sensitive to cold. <em>R. padi</em> was estimated to accumulate higher cold injury than the other two species during winter, while <em>M. dirhodum</em> accrued more heat injury during summer. The warmer site had higher risks of heat injury and the cooler site had higher risks of cold injury along a latitude gradient. These results support recent field observations that the proportion of <em>R. padi</em> increases with the increased frequency of heat waves. We also found that young nymphs generally had a lower thermal tolerance than old nymphs or adults. Our results provide a useful dataset and method for modelling and predicting the consequence of climate change on the population dynamics and community structure of small insects.</p>
Disentangling compound effects of changing disturbance and regeneration across temperate forest landscapes
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Data from: Species-specific responses to paleoclimatic changes and landscape barriers drive contrasting phylogeography of co-distributed lemur species in northeastern Madagascar
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Land use change converts temperate dryland landscape into a net methane source
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Agricultural landscape simplification affects wild plant fitness indirectly through herbivore-mediated changes in floral display
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Interspecific differences in thermal tolerance landscape explain aphid community abundance under climate change
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Influence of land use changes on landscape connectivity for North China leopard (Panthera pardus japonensis)
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Data from: Beyond resilience: Responses to changing climate and disturbance regimes in temperate forest landscapes across the Northern Hemisphere
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Concordant and opposing effects of climate and land-use change on avian assemblages in California’s most transformed landscapes
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Reptile diversity patterns under climate and land use change scenarios in a subtropical montane landscape in Mexico
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Movement behavior in a dominant ungulate underlies successful adjustment to a rapidly changing landscape following megafire
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Baltimore Ecosystem Study: Loss of Phylogenetic Diversity under Landscape Change
Habitat alteration and destruction are a primary driver of biodiversity loss. There is a plethora of research documenting similarly strong patterns of decline across ecosystem types and spatial scales. However, evolutionary dimensions remain largely unexplored in many systems. For example, little is known about how habitat alteration/loss can lead to phylogenetic deconstruction of ecological assemblages at the local level. That is, while species loss is evident, are some lineages favored over others? Using a long-term dataset of a globally, ecologically important guild of invertebrate consumers, stream leaf “shredders,” we created a phylogenetic tree of the taxa in the regional species pool, calculated mean phylogenetic distinctiveness for > 1000 communities spanning > 10 y period, and related species richness, phylogenetic diversity and distinctiveness to watershed-scale impervious cover. Using a combination of changepoint and compositional analyses, we learned that increasing impervious cover produced marked reductions in all three measures of diversity, and in particular, aid in understanding both phylogenetic diversity and average assemblage phylogenetic distinctiveness. Our findings suggest that, not only are species lost when there is an increase in watershed urbanization, as other studies have demonstrated, but that those lost are members of more distinct lineages relative to the community as a whole.
Topographic heterogeneity mediates patterns of vegetation response to climate change across a mountain landscape, Niwot Ridge, Colorado, 1972-2008
The distributions of biomes worldwide are predicted to shift as vegetation tracks climate change. Ecologists often use coarse-scale climate models to predict these shifts along broad elevational and latitudinal gradients, but these assessments could fail to capture important dynamics by ignoring fine-scale heterogeneity. We ask how the elevational ranges of vegetation types have changed in a mountainous landscape, and investigate the influence of fine-scale topographic, snowpack, and soil properties on vegetation change. We manually classified vegetation from high-resolution repeat aerial photographs from 1972 and 2008 at Niwot Ridge, Colorado, USA, and generally found that trees and shrubs colonized tundra, while tundra colonized barren soils. Only shrubs expanded their elevational range. Several fine-scale topographic, soil and snow characteristics, including elevation, slope, solar radiation, soil bulk density, and interannual snowpack variability, modulated where plant establishment occurred. Each vegetation type had a unique suite of variables best predicting its establishment in new areas. We suggest that fine-scale heterogeneity may strongly control how plants in mountainous regions respond to climate change, and different vegetation types may be sensitive to different aspects of this heterogeneity. An improved understanding of the factors controlling vegetation change gives us a broader understanding of ecosystem response to climate change, nitrogen deposition, and release from grazing.
Pairing Remote Sensing and Clustering in Landscape Hydrology for Large-Scale Changes Identification. Applications to the Subarctic Watershed of the George River (Nunavik, Canada). Dataset and Code.
<p>For remote and vast northern watersheds, hydrological data are often sparse and incomplete. Landscape hydrology provides useful approaches for the indirect assessment of the hydrological characteristics of watersheds through analysis of landscape properties. In this study, we used unsupervised Geographic Object-Based Image Analysis (GeOBIA) paired with the Fuzzy C-Means (FCM) clustering algorithm to produce seven high-resolution territorial classifications of key remotely sensed hydro-geomorphic metrics for the 1985-2019 time-period, each spanning five years. Our study site is the George River watershed (GRW), a 42,000 km<sup>2</sup> watershed located in Nunavik, northern Quebec (Canada). The subwatersheds within the GRW, used as the objects of the GeOBIA, were classified as a function of their hydrological similarities. Classification results for the period 2015-2019 showed that the GRW is composed of two main types of subwatersheds distributed along a latitudinal gradient, which indicates broad-scale differences in hydrological regimes and water balances across the GRW. Six classifications were computed for the period 1985-2014 to investigate past changes in hydrological regime. The seven-classification time series showed a homogenization of subwatershed types associated to increases in vegetation productivity and in water content<br> in soil and vegetation, mostly concentrated in the northern half of the GRW, which were the major changes occurring in the land cover metrics of the GRW. An increase in vegetation productivity likely contributed to an augmentation in evapotranspiration and may be a primary driver of fundamental shifts in the GRW water balance, potentially explaining a measured decline of about 1 % (∼ 0.16 km<sup>3</sup>y<sup>−1</sup>) in the George River’s discharge since the mid-1970s. Permafrost degradation over the study period also likely affected the hydrological regime and water balance of the GRW. However, the shifts in permafrost extent and active layer thickness remain difficult to detect using remote sensing based approaches, particularly in areas of discontinuous and sporadic permafrost.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.