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178 results for “Landscape heterogeneity”
Data belonging to: Teurlincx, S., Verhofstad, M. J., Bakker, E. S., & Declerck, S. A. (2018). Managing successional stage heterogeneity to maximize landscape-wide biodiversity of aquatic vegetation in ditch networks. Frontiers in plant science, 9, 1013.
<p>Data belonging to the paper Teurlincx, S., Verhofstad, M. J., Bakker, E. S., & Declerck, S. A. (2018). Managing successional stage heterogeneity to maximize landscape-wide biodiversity of aquatic vegetation in ditch networks. Frontiers in plant science, 9, 1013.</p> <p>Data includes analysis scripts (R Language) and all used data files. Data is composed of location information of the different sites, environmental conditions on site and vegetation composition.</p>
CBM01 Plains bison movement patterns in an experimental heterogeneous landscape at Konza Prairie
This GPS-collar data set was used to evaluate the factors that influence where bison choose to graze and how grazing and space use patterns affect ecosystem function and structure. Our objectives were to quantify space use and movement patterns of adult female Plains bison in the context of selection for specific prescribed burn frequencies and topographical features in the bison-grazed watersheds at Konza Prairie. We hypothesized bison would track post-prescribed burn forage productivity and we predicted watersheds burned for the first time in several years would be used to a greater extent than watersheds burned more frequently.
Code: A model of wild bee populations accounting for spatial heterogeneity and climate induced temporal variability of food resources at the landscape level
<p><span>The viability of wild bee populations and the pollination services that they provide are driven by the availability of food resources during their activity period and within the surroundings of their nesting sites. Changes in climate and land use influence the availability of these resources and are major threats to declining bee populations. Because wild bees may be vulnerable to interactions between these threats, spatially explicit models of population dynamics that capture how bee populations jointly respond to land use at a landscape scale and weather are needed. Here, we developed a spatially and temporally explicit theoretical model of wild bee populations aiming for a middle ground between the existing mapping of visitation rates using foraging equations and more refined agent-based modelling. The model is developed for <em>Bombus</em> sp. and captures within-season colony dynamics. The model describes mechanistically foraging at the colony level and temporal population dynamics for an average colony at the landscape level. Stages in population dynamics are temperature-dependent triggered with a theoretical generalized seasonal progression, which can be informed by growing degree days (GDD). The purpose of the LandscapePhenoBee model is to evaluate the impact of systematic changes and within-season variability in resources on bee population sizes and crop visitation rates. In a simulation study, we used the model to evaluate the impact of the shortage of food resources in the landscape arising from extreme drought events in different types of landscapes (ranging from different proportions of semi-natural habitats and early and late flowering crops) on bumblebee populations.</span></p>
Fig. 1 in Landscape-B Asin Approach To The Study Of Floristic Diversity (Heterogeneous Catchments Of Steppe And Forest-Steppe Zones Of Altai Krai, Russia, As A Case Study)
Fig. 1. Landscape and floristic regionalization of neighboring the Kasmalinsky basin and the Barnaulka river basin.
Figure 4 in Dung beetle conservation in a heterogeneous landscape of the Maputaland Centre of Endemism
Figure 4. Contribution of each spatial component (habitat and patch size) to total species numbers sampled in the entire reserve (gamma diversity, n = 61 species); alpha (α) = diversity contributed by study sites; beta (β1) = diversity between study sites; beta (β2) = diversity between habitats. Proportions contributed by alpha and beta diversity are shown by the divisions within the bars whereas numbers of species are shown inside the bars (see methods). DF = dune forest; SF-L, SF-M, SF-S = large, medium and small sand forest patches; G = grassland.
Figure S1 in Dung beetle conservation in a heterogeneous landscape of the Maputaland Centre of Endemism
Figure S1. Rarity index values for each species ranked from highest to lowest. Dotted lines show rare species found at up to two sites and indicate the high proportion of the total species (~60%) found at eight sites or less (key to blocks of species numbers in Table S1; species in bold biased to grassland occurrence).
Figure 3 in Dung beetle conservation in a heterogeneous landscape of the Maputaland Centre of Endemism
Figure 3. Box plots comparing number of species (A) and abundance (B) in each habitat type (DF = dune forest, SF-L, SF-M, SF-S = large, medium and small sand forest patches, G = grassland). Within each box, the mid-line represents the median whereas the lower and upper extents of the box represent the interquartile range Q1 = 25th percentile and Q3 = 75th percentile, respectively. Whiskers are minimum and maximum values. Black points represent outliers. Different letters indicate statistical differences at P <0.05.
Figure 2 in Dung beetle conservation in a heterogeneous landscape of the Maputaland Centre of Endemism
Figure 2. Frequency distribution for proportions of species recorded at 32 study sites in MSR (n = 4 in dune forest; n = 7 in grassland and each of three patch sizes of sand forest). Dotted lines delineate the relative incidence of rar- er species at study sites. Note that a high percentage of the total species are found only in 25% of the sampled sites. See Figure S1 for values of the species rarity index.
Figure 1. A in Dung beetle conservation in a heterogeneous landscape of the Maputaland Centre of Endemism
Figure 1. A, map outlining the study area and showing the position of Maputo Special Reserve (MSR) in relation to land usage in the surrounding area of southeast Mozambique and northeast KwaZulu-Natal, South Africa (nature and game reserves are marked by green and yellow, darker grey outlines the Lubombo Conservancy, unprotected areas are pale grey, Indian Ocean, inlets and lagoons are blue) (redrawn from Peace Parks Foundation 2020); B, position of sampling localities in MSR plotted onto Google Earth; L1–L7 = study sites at each locality in large, medium and small sand forest patches plus grassland; L8, DF1–DF4 = dune forest study sites. Position of most forest sites was measured at the point of entry as the GPS could not detect satellites under the canopy.
Figure. Constrained ordination plot as produced from canonical correspondence analysis (CCA). The variability of environmental variables is summarized on Axis 1 and Axis 2 of the constrained biplot, explaining the variability of the trophic groups included in the red fox's diet. Trophic groups are shown with black line (unfilled) pyramids, whereas environmental variables are shown with black filled pyramids. Proximity and distance of response centroids to predictor centroids indicate positive and negative correlations between them, respectively. in Factors affecting the diet of the red fox (Vulpes vulpes) in a heterogeneous Mediterranean landscape
Figure. Constrained ordination plot as produced from canonical correspondence analysis (CCA). The variability of environmental variables is summarized on Axis 1 and Axis 2 of the constrained biplot, explaining the variability of the trophic groups included in the red fox's diet. Trophic groups are shown with black line (unfilled) pyramids, whereas environmental variables are shown with black filled pyramids. Proximity and distance of response centroids to predictor centroids indicate positive and negative correlations between them, respectively.
Landscape heterogeneity provides co-benefits to predator and prey
<p>Predator populations are imperiled globally, due in part to changing habitat and trophic interactions. Theoretical and laboratory studies suggest that heterogeneous landscapes containing prey refuges acting as source habitats can benefit both predator and prey populations, although the importance of heterogeneity in natural systems is uncertain. Here, we tested the hypothesis that landscape heterogeneity mediates predator-prey interactions between the California spotted owl (<em>Strix occidentalis occidentalis</em>) – a mature forest species – and one of its principal prey, the dusky-footed woodrat (<em>Neotoma</em> <em>fuscipes</em>) – a younger forest species – to the benefit of both. We did so by combining estimates of woodrat density and survival from live-trapping and VHF tracking with direct observations of prey deliveries to dependent young by owls in both heterogeneous and homogeneous home ranges. Woodrat abundance was approximately 2.5x higher in owl home ranges (1,412 hectares) featuring greater heterogeneity in vegetation types (1,805.0 ± 50.2 SE) compared to those dominated by mature forest (727.3 ± 51.9 SE), in large part because of high densities in young forests appearing to act as sources promoting woodrat densities in nearby mature forests. Woodrat mortality rates were low across vegetation types and did not differ between heterogeneous and homogeneous home ranges, yet all observed predation by owls occurred within mature forests, suggesting young forests may act as woodrat refuges. Owls exhibited a type 1 functional response, consuming approximately 2.5x more woodrats in heterogeneous (31.1/month ± 5.2 SE) versus homogeneous (12.7/month ± 3.7 SE) home ranges. While consumption of smaller-bodied alternative prey partially compensated for lower woodrat consumption in homogeneous home ranges, owls nevertheless consumed 30% more biomass in heterogeneous home ranges – approximately equivalent to the energetic needs of producing one additional offspring. Thus, a mosaic of vegetation types including young forest patches increased woodrat abundance and availability that, in turn, provided energetic and potentially reproductive benefits to mature forest-associated spotted owls. More broadly, our findings provide strong empirical evidence that heterogeneous landscapes containing prey refuges can benefit both predator and prey populations. As anthropogenic activities continue to homogenize landscapes globally, promoting heterogeneous systems with prey refuges may benefit imperiled predators.</p>
Landscape heterogeneity provides co-benefits to predator and prey
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Data from: Crop and landscape heterogeneity increase biodiversity in agricultural landscapes: A global review and meta-analysis
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Effects of compositional heterogeneity and spatial autocorrelation on richness and diversity in simulated landscapes
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Code: A model of wild bee populations accounting for spatial heterogeneity and climate induced temporal variability of food resources at the landscape level
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Eight Mile Lake Research Watershed, Thaw Gradient, Ecosystem carbon balance: Eddy covariance CO2 flux data of a heterogenous landscape undergoing permafrost thaw.
In this larger study, we are asking the question: Is old carbon that comprises the bulk of the soil organic matter pool released in response to thawing of permafrost? We are answering this question by using a combination of field and laboratory experiments to measure radiocarbon isotope ratios in soil organic matter, soil respiration, and dissolved organic carbon, in tundra ecosystems. The objective of these proposed measurements is to develop a mechanistic understanding of the SOM sources contributing to C losses following permafrost thawing. We are making these measurements at an established tundra field site near Healy, Alaska in the foothills of the Alaska Range. Field measurements center on a natural experiment where permafrost has been observed to warm and thaw over the past several decades. This area represents a gradient of sites each with a different degree of change due to permafrost thawing. As such, this area is unique for addressing questions at the time and spatial scales relevant for change in arctic ecosystems. Understanding how landscape level physical and biological changes effect carbon cycling is important for estimating the carbon balance of an ecosystem undergoing permafrost thaw.
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.
Landscape heterogeneity shapes bird phylogenetic responses at forest-matrix interfaces in Atlantic Forest, Brazil
<p>Agricultural intensification is one of the major factors driving biodiversity loss. However, most studies in human-dominated landscapes have used taxonomic diversity in their analysis, ignoring evolutionary relationships. Consequently, the relationship between landscape structure and phylogenetic diversity is not well understood. Here, we tested the hypothesis that landscape heterogeneity is positively related to bird phylogenetic indexes of diversity and structure, leading to over-dispersed phylogenies in very heterogeneous landscapes. We analyzed phylogenetic responses in interfaces between forest edges and anthropogenic matrices (forest-pasture and forest-eucalyptus) using generalized linear mixed models. We also compared these indexes between land covers to assess which one best preserves the phylogenetic history of communities. We used both traditional phylogenetic indexes and those corrected for species richness. Our results showed that phylogenetic diversity varied significantly between land cover types, but this did not occur when we removed effects associated with species richness, suggesting that all land covers preserve similar levels of evolutionary history. Additionally, our best models showed a positive relationship between landscape heterogeneity and bird phylogenetic indexes of diversity and structure, but the strength of these relationships may be land-cover dependent. In summary, our work highlights the influence of landscape heterogeneity on the phylogenetic diversity and structure of bird communities, reinforcing the need for its incorporation into conservation-based studies.</p>
Data from: Landscape heterogeneity is key to forecasting outcomes of plant reintroduction
Conservation and restoration projects often involve starting new populations by introducing individuals into portions of their native or projected range. Such efforts can help meet many related goals, including habitat creation, ecosystem service provisioning, assisted migration, and the reintroduction of imperiled species following local extirpation. The outcomes of reintroduction efforts, however, are highly variable, with results ranging from local extinction to dramatic population growth; reasons for this variation remain unclear. Here, we ask whether population growth following plant reintroductions is governed by variation at two scales: the scale of individual habitat patches to which individuals are reintroduced, and larger among-landscape scales in which similar patches may be situated in landscapes that differ in matrix type, soil conditions, and other factors. Quantifying demographic variation at these two scales will help prioritize locations for introduction and, once introductions take place, forecast population growth. This work took place within a large-scale habitat fragmentation experiment, where individuals of two perennial forb species were reintroduced into eight replicate ~50 ha landscapes, each containing a set of five ~1 ha patches that varied in their degree of isolation (connected by habitat corridors or unconnected) and edge-to-area ratio. Using data on individual growth, survival, reproductive output, and recruitment collected one to two years after reintroduction, we developed models to forecast population growth, then compared forecasts to observed population sizes, three and six years later. Both the type of patch (connected and unconnected) and identity of the landscape to which individuals were reintroduced had effects on forecasted population growth rates, but only variation associated with landscape identity was an accurate predictor of subsequently observed population growth rates. Models that did not include landscape identity had minimal forecasting ability, revealing the key importance of variation at this scale for accurate prediction. Of the five demographic rates used to model population dynamics, seed production was the most important source of forecast error in population growth rates. Our results point to the importance of accounting for landscape-scale variation in demographic models and demonstrate how such models might assist with prioritizing particular landscapes for species reintroduction projects.
Data from: A trait-based framework for discerning drivers of species co-occurrence across heterogeneous landscapes
Null model analysis of species co-occurrence patterns has long been used to gain insight into community assembly but is often limited to identifying non-random patterns without providing clarity about underlying ecological mechanisms. This challenge is especially apparent when sampling units are spread across a heterogeneous landscape or along an environmental gradient because multiple mechanisms can produce similar co-occurrence patterns. We developed a trait-based approach for discriminating between environmental filtering and biotic interactions as the probable driver of co-occurrence patterns across environmentally heterogeneous sites. We demonstrate our framework by analyzing the co-occurrence of small mammals over elevation in three independent mountain ranges in the Great Basin of the western United States. Our sampling design accounts for landscape scale environmental variability and within-site habitat heterogeneity. We identified 52 non-random species pairs, of which 36 were aggregated and 16 were segregated. For each pair, we determined which mechanism was the likely ecological explanation using a hypothesis-testing framework based on functional trait similarity. Expectations of biotic interactions were based on similarity of diet and body size whereas habitat affinity and geographic range were used for environmental filtering. Only four pairs were consistent with expectations under biotic interactions, including pairs for which competitive exclusion has previously been documented. In addition to analyzing individual pairs, we used binomial tests of observed versus expected totals of intra- and inter-guild pairs to determine assemblage-wide deviations from random community structure. Signatures of environmental filtering were consistent across mountain ranges and scales. Despite differences in species composition and significant pairs among data sets, our approach revealed consistent mechanistic conclusions, emphasizing the value of trait-based methods to co-occurrence and community assembly.
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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)
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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.
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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.