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32 results for “contrasting landscape”
Contrasting effects of landscape composition on crop yield mediated by specialist herbivores
<p>Landscape composition not only affects a variety of arthropod-mediated ecosystem services, but also disservices, such as herbivory by insect pests that may have negative effects on crop yield. Yet, little is known about how different habitats influence the dynamics of multiple herbivore species, and ultimately their collective impact on crop production. Using cabbage as a model system, we examined how landscape composition influenced the incidence of three specialist cruciferous pests (aphids, flea beetles, and leaf-feeding Lepidoptera), lepidopteran parasitoids, and crop yield across a gradient of landscape composition in New York, USA. We expected that landscapes with a higher proportion of cropland and lower habitat diversity would lead to an increase in pest pressure of the specialist herbivores and a reduction in crop yield. However, results indicated that neither greater cropland area nor lower landscape diversity influenced pest pressure or yield. Rather, pest pressure and yield were best explained by the presence of non-crop habitats (i.e. meadows) in the landscape. Specifically, cabbage was infested with fewer Lepidoptera in landscapes with a higher proportion of meadows likely resulting from increased parasitism. Conversely, cabbage was infested with more flea beetles and aphids as the proportion of meadows in the landscape increased, suggesting that these pests benefit from non-crop habitats. Furthermore, path analysis confirmed that these landscape-mediated effects on pest populations can have either positive or negative cascading effects on crop yield. Our findings illustrate how different pest species within the same cropping system show contrasting responses to landscape composition with respect to both the direction and spatial scale of the relationship. Such tradeoffs resulting from the complex interaction between multiple-pests, natural enemies, and landscape composition must be considered, if we are to manage landscapes for pest suppression benefits.</p>
Contrasting effects of landscape composition on crop yield mediated by specialist herbivores
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Spatial scaling of pollen-plant diversity relationship in landscapes with contrasting diversity patterns
<p>Data for paper "Spatial scaling of pollen-plant diversity relationship in landscapes with contrasting diversity patterns" in Scientific Reports.</p> <p>Code for analysis and plots <a href="https://github.com/vojtechabraham/SpatialScalingPollenDiversity">https://github.com/vojtechabraham/SpatialScalingPollenDiversity</a>. Download original pollen and resample them to the same pollen sum by function spectra_to_target_sum in <a href="https://github.com/vojtechabraham/pollen">https://github.com/vojtechabraham/pollen</a> or work with resampled datasets below.</p> <p>Original pollen data stored in <a href="https://www.neotomadb.org/">https://www.neotomadb.org/</a>:</p> <table> <tbody> <tr> <td><strong>species-poor region Bohemian-Moravian Highland (Vrchovina)</strong></td> </tr> <tr> <td><strong>forested</strong></td> <td> </td> <td> </td> <td><strong>open</strong></td> </tr> <tr> <td><strong>SiteName</strong></td> <td><strong>Handle</strong></td> <td><strong>Dataset ID</strong></td> <td><strong>SiteName</strong></td> <td><strong>Handle</strong></td> <td><strong>Dataset ID</strong></td> </tr> <tr> <td>Račín</td> <td>V06</td> <td><a href="https://data.neotomadb.org/54872">54872</a></td> <td>Plíčky</td> <td>V03</td> <td><a href="https://data.neotomadb.org/54870">54870</a></td> </tr> <tr> <td>Vepřová-Žlábek</td> <td>V07</td> <td><a href="https://data.neotomadb.org/54873">54873</a></td> <td>Louky u Černého lesa</td> <td>V04</td> <td><a href="https://data.neotomadb.org/54871">54871</a></td> </tr> <tr> <td>Stropnická cesta</td> <td>V18</td> <td><a href="https://data.neotomadb.org/54882">54882</a></td> <td>Suché Kopce</td> <td>V10</td> <td><a href="https://data.neotomadb.org/54874">54874</a></td> </tr> <tr> <td>Žižkov</td> <td>V19</td> <td><a href="https://data.neotomadb.org/54883">54883</a></td> <td>Pihoviny</td> <td>V11</td> <td><a href="https://data.neotomadb.org/54875">54875</a></td> </tr> <tr> <td>Chlum</td> <td>V21</td> <td><a href="https://data.neotomadb.org/54885">54885</a></td> <td>Kocanda</td> <td>V12</td> <td><a href="https://data.neotomadb.org/54876">54876</a></td> </tr> <tr> <td>Míšek</td> <td>V22</td> <td><a href="https://data.neotomadb.org/54886">54886</a></td> <td>Porostliny</td> <td>V13</td> <td><a href="https://data.neotomadb.org/54877">54877</a></td> </tr> <tr> <td>Knížecí studánka</td> <td>V23</td> <td><a href="http://data.neotomadb.org/54887">54887</a></td> <td>Bahna</td> <td>V14</td> <td><a href="https://data.neotomadb.org/54878">54878</a></td> </tr> <tr> <td>Pod Šindelným vrchem</td> <td>V24</td> <td><a href="https://data.neotomadb.org/54888">54888</a></td> <td>Ratajské rybníky</td> <td>V15</td> <td><a href="https://data.neotomadb.org/54879">54879</a></td> </tr> <tr> <td>Rampoltův mlýn</td> <td>V25</td> <td><a href="https://data.neotomadb.org/54889">54889</a></td> <td>Zubří</td> <td>V16</td> <td><a href="https://data.neotomadb.org/54880">54880</a></td> </tr> <tr> <td>Brožova skála</td> <td>V26</td> <td><a href="https://data.neotomadb.org/54890">54890</a></td> <td>Nový Rybník</td> <td>V17</td> <td><a href="https://data.neotomadb.org/54881">54881</a></td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td>Samotín</td> <td>V20</td> <td><a href="https://data.neotomadb.org/54884">54884</a></td> </tr> </tbody> </table> <p> </p> <table> <tbody> <tr> <td><strong>species-rich region White-Carpathians Mountains (Bílé Karpaty)</strong></td> </tr> <tr> <td><strong>forested</strong></td> <td> </td> <td><strong>open</strong></td> </tr> <tr> <td><strong>Handle</strong></td> <td><strong>Dataset ID</strong></td> <td><strong>Handle</strong></td> <td><strong>Dataset ID</strong></td> </tr> <tr> <td>BK1</td> <td><a href="https://data.neotomadb.org/54770">54770</a></td> <td>BK2</td> <td><a href="https://data.neotomadb.org/54771">54771</a></td> </tr> <tr> <td>BK3</td> <td><a href="https://data.neotomadb.org/54772">54772</a></td> <td>BK4</td> <td><a href="https://data.neotomadb.org/54773">54773</a></td> </tr> <tr> <td>BK5</td> <td><a href="https://data.neotomadb.org/54774">54774</a></td> <td>BK6</td> <td><a href="https://data.neotomadb.org/54775">54775</a></td> </tr> <tr> <td>BK9</td> <td><a href="https://data.neotomadb.org/54777">54777</a></td> <td>BK8</td> <td><a href="https://data.neotomadb.org/54776">54776</a></td> </tr> <tr> <td>BK11</td> <td><a href="https://data.neotomadb.org/54779">54779</a></td> <td>BK10</td> <td><a href="https://data.neotomadb.org/54778">54778</a></td> </tr> <tr> <td>BK13</td> <td><a href="https://data.neotomadb.org/54781">54781</a></td> <td>BK12</td> <td><a href="https://data.neotomadb.org/54780">54780</a></td> </tr> <tr> <td>BK15</td> <td><a href="https://data.neotomadb.org/54783">54783</a></td> <td>BK14</td> <td><a href="https://data.neotomadb.org/54782">54782</a></td> </tr> <tr> <td>BK16</td> <td><a href="https://data.neotomadb.org/54784">54784</a></td> <td>BK20</td> <td><a href="https://data.neotomadb.org/54788">54788</a></td> </tr> <tr> <td>BK17</td> <td><a href="https://data.neotomadb.org/54785">54785</a></td> <td>BK23</td> <td><a href="https://data.neotomadb.org/54791">54791</a></td> </tr> <tr> <td>BK18</td> <td><a href="https://data.neotomadb.org/54786">54786</a></td> <td>BK25</td> <td><a href="https://data.neotomadb.org/54793">54793</a></td> </tr> <tr> <td>BK19</td> <td><a href="https://data.neotomadb.org/54787">54787</a></td> <td>BK27</td> <td><a href="https://data.neotomadb.org/54795">54795</a></td> </tr> <tr> <td>BK21</td> <td><a href="https://data.neotomadb.org/54789">54789</a></td> <td>BK29</td> <td><a href="https://data.neotomadb.org/54797">54797</a></td> </tr> <tr> <td>BK22</td> <td><a href="https://data.neotomadb.org/54790">54790</a></td> <td>BK31</td> <td><a href="https://data.neotomadb.org/54799">54799</a></td> </tr> <tr> <td>BK24</td> <td><a href="https://data.neotomadb.org/54792">54792</a></td> <td>BK33</td> <td><a href="https://data.neotomadb.org/54801">54801</a></td> </tr> <tr> <td>BK26</td> <td><a href="https://data.neotomadb.org/54794">54794</a></td> <td>BK35</td> <td><a href="https://data.neotomadb.org/54803">54803</a></td> </tr> <tr> <td>BK28</td> <td><a href="https://data.neotomadb.org/54796">54796</a></td> <td>BK36</td> <td><a href="https://data.neotomadb.org/54804">54804</a></td> </tr> <tr> <td>BK30</td> <td><a href="https://data.neotomadb.org/54798">54798</a></td> <td>BK38</td> <td><a href="https://data.neotomadb.org/54805">54805</a></td> </tr> <tr> <td>BK32</td> <td><a href="https://data.neotomadb.org/54800">54800</a></td> <td>BK39</td> <td><a href="https://data.neotomadb.org/54806">54806</a></td> </tr> <tr> <td>BK34</td> <td><a href="https://data.neotomadb.org/54802">54802</a></td> <td>BK40</td> <td><a href="https://data.neotomadb.org/54807">54807</a></td> </tr> <tr> <td> </td> <td> </td> <td>BK41</td> <td><a href="https://data.neotomadb.org/54808">54808</a></td> </tr> </tbody> </table> <p> </p>
Data for: Resource landscapes explain contrasting patterns of aggregation and site fidelity by red knots at two wintering sites
<p>This repository contains data for the paper: Oudman et al. 2018. Resource landscapes explain contrasting patterns of aggregation and site fidelity by red knots at two wintering sites. <em>Movement Ecology</em> 6(14) 1-12. https://doi.org/10.1186/s40462-018-0142-4.</p> <p>Please cite the original publication when using this data.</p>
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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Data from: Contrasting population structure and demographic history of cereal aphids in different environmental and agricultural landscapes
<p>Single Nucleotide Polymorphisms files and phylogonetic trees of S. miscanthi samples collected in China and S. avenae from the UK used to study the population genetics analyses of these species. These are:</p> <p>China_samples_vcf.zip: dataset of SNPs from S. miscanthi sampled in 10 populations of China obtained using FreeBayes (in vcf format).</p> <p>China_samples_vcf_filtered.zip: SNPs from S. miscanthi after filtering the file China_samples_vcf.zip using vcftools (max-missing 0.75, minDP 3, mac 3, minQ 30, remove-indels, thin 2000, max-missing 0.9, thin 5000). This file was used in all population genetic analyses of the Chinese populations in the paper, transforming to the appropriate formats.</p> <p>China_samples_SNPs.fas: fasta file of phased SNPs used to estimate the phylogeny of S. miscanthi haplotypes using RAxML.</p> <p>China_RAxML_phylogeny_newick.tre: RAxML phylogenetic tree in newick format obtained with China_samples_SNPs.fas.</p> <p>England_samples_vcf.zip: dataset of SNPs from S. avenae sampled in 12 populations of England obtained using FreeBayes (in vcf format).</p> <p>England_samples_vcf_filtered.zip: SNPs from S. avenae after filtering the file England_samples_vcf.zip using vcftools (max-missing 0.5, mac 3, minQ 30, minDP 3, max-missing 0.5, exclude individuals with 50% missing data, max-missing 0.75, remove-indels, thin 2000). This file was used in all population genetic analyses of the English populations in the paper, transforming the vcf to the corresponding formats.</p> <p>England_samples_SNPs.fas: fasta file of phased SNPs.</p> <p>England_samples_SNPs_polymorphic.fas: fasta file of phased SNPs used in the phylogenetic reconstruction of S. avenae haplotypes using RAxML. This file is the same as England_samples_SNPs.fas after removing sites which were not polymorphic (e.g. a site that contains N and T in different samples is not considered polymorphic for RAxML and has to be removed)</p> <p>England_RAxML_phylogeny_newick.tre: RAxML phylogenetic tree in newick format obtained with England_samples_SNPs_polymorphic.fas.</p>
Data from: Born in heterogenous landscapes: birth timing, body mass and growth of roe deer (Capreolus capreolus) fawns in contrasting habitats
<p>Although the widespread effects of global change impact almost all ecosystems, we lack a detailed understanding of how wildlife that thrive in human-dominated environments are able to adjust their life history to modifications in land use of their natural habitat. In particular, spatial variation in environmental conditions is predicted to influence development during the crucial early life phase, with marked impacts on individual performance and population dynamics for long-lived species. Large herbivores such as roe deer (<em>Capreolus capreolus</em>), a synanthropic species, have increased substantially in number and distribution over the last half century across Europe. Roe deer have been particularly successful, gradually colonizing agricultural landscapes to cope with a global warming-driven phenological mismatch in their natural forest habitat. However, to date, little is known about how habitat heterogeneity impacts their demographic performance in this heavily human-impacted environment. Specifically, we predicted that fawns born in predominantly cultivated local habitats would achieve faster early development due to the food subsidies obtained by their mothers from agricultural crops. Contrary to our expectations, fawns in semi-natural forest habitats were around 10% heavier at birth than those born in more mixed (by 0.163 ± 0.058 kg) and open (by 0.169 ± 0.006 kg) agricultural habitats. However, all fawns subsequently grew at a similar average rate (0.148 ± 0.058 kg/day), irrespective of their habitat. This habitat-dependent variation in birth mass appeared to be driven by reproductive phenology, as i) early-born fawns were heavier than late-born fawns, and ii) mothers living in the forest gave birth around 10 days earlier than those living in the mixed and open habitats. As natural habitats become increasingly scarce and fragmented due to the activities of humans, the prospects for many wild populations will depend on their ability to subsist in the heavily modified habitats of anthropogenic landscapes.</p>
Contrasting effects of vineyard type, soil and landscape factors on ground- versus above-ground nesting bees
<p><span>1. Agricultural intensification and abandonment of traditional agricultural practices are main drivers of current insect declines. The resulting loss of feeding and nesting opportunities has led to a decrease in pollinator populations like wild bees. While the restoration of floral resources has been widely implemented in wild bee conservation, nesting resources, particularly for ground-nesting species, are barely considered.</span></p> <p><span>2. We assessed wild bee diversity in a wine-growing area in Germany in 15 study sites along a soil gradient and evaluated whether wild bees were distinctly affected by different vineyard types (vertically oriented, terraced, abandoned), local conditions (e.g. shrub and flower cover), and landscape factors in response to divergent nesting needs (above-ground vs. ground-nesting). </span></p> <p><span>3. We found that wild bees responded more strongly to the availability of nesting sites than to flower resources. While ground-nesting bees were determined by the suitability of soil aspects for nesting irrespective of vineyard management types, above-ground nesting bees profited from vineyard abandonment and shrub encroachment in vineyard fallows and were enhanced by the availability of seminatural habitats (SNH) in the surrounding landscape. In contrast, floral resource availability in managed vineyards had only marginal effects on above-ground-nesting bees.</span></p> <p><span>4. Synthesis and applications: Life history traits like nesting strategies have long been neglected in wild bee conservation approaches, but proved to be highly relevant, especially for ground-nesting bees. For this, agri-environmental schemes can no longer solely focus on the restoration of floral resources, but should equally address nesting resources. Therefore, management efforts for enhancing wild bees in vineyard landscapes should aim at complementing nesting resources for ground-nesting bees (e.g. exposed bare ground patches) and above-ground-nesting bees (e.g. woody elements, hedges) in addition to floral resources. At the landscape level, conserving heterogeneous landscapes at a mixture of actively managed vineyards and semi-natural and woody elements is significant to maintain diverse bee communities. </span></p>
Flower strip effectiveness for pollinating insects in agricultural landscapes depends on established contrast in habitat quality: A meta-analysis
<p>Flower strips have become a prevalent measure in agricultural landscapes to counteract biodiversity loss and especially promote pollinators. Although their benefits for pollinating insects have been frequently evaluated and reported, generalized conclusions about optimal settings for effective flower strips are still difficult. From the perspective of pollinators, flower strips vary distinctly in habitat quality, and the same applies for the control sites selected for scientific studies.</p> <p>In this study, we used a meta-analytic approach based on a systematic review of recent studies (2009-2020) to analyze the relationship between flower strip effectiveness for pollinators and the contrast in habitat quality between flower strips and control sites. We extracted 350 data entries from 29 out of 172 studies based on available data for richness or abundance of the pollinator taxa groups Apiformes, Lepidoptera and Syrphidae as response variables, for both flower strips and control treatments. All flower strips and control treatments were assigned a habitat quality score including information on spatial dimension, floral resources and management. Moreover, we included information on landscape complexity as measured by percent cover of semi-natural habitats in the studied landscape.</p> <p>In general, our results of meta-analytical models showed an increasing effect size of flower strips on pollinators for higher contrasts in habitat quality between flower strips and control treatments. This relationship was consistent across pollinator taxa and different levels of landscape complexity. Altogether, in terms of pollinator habitat quality, high-quality flower strips were more attractive than low-quality flower strips, and the reported effectiveness of flower strips decreased from low-quality to high-quality control treatments.</p> <p>We recommend that results of future studies evaluating flower strips for pollinators are always linked with the contrast in habitat quality between selected flower strips and control treatments.</p>
Flower strip effectiveness for pollinating insects in agricultural landscapes depends on established contrast in habitat quality: A meta-analysis
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Data from: Born in heterogenous landscapes: birth timing, body mass and growth of roe deer (Capreolus capreolus) fawns in contrasting habitats
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Data from: Navigating a landscape of contrasting hunting regimes and habitats: Red deer responses to risk and resources
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Data from: Contrasting patterns of land use by resident and migratory bird assemblages in a tropical working landscape
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Contrasting effects of vineyard type, soil and landscape factors on ground- versus above-ground nesting bees
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Data from: Contrasting population structure and demographic history of cereal aphids in different environmental and agricultural landscapes
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Data from: Dispersal in a patchy landscape reveals contrasting determinants of infection in a wild avian malaria system
1. Understanding exactly when, where and how hosts become infected with parasites is critical to understanding host-parasite coevolution. However, for host-parasite systems in which hosts or parasites are mobile (for example vector-borne diseases), the spatial location of infection, and the relative importance of parasite exposure at successive host life-history stages, are often uncertain. 2. Here, using a six-year longitudinal dataset from a spatially referenced population of blue tits, we test the extent to which infection by avian malaria parasites is determined by conditions experienced at natal or breeding sites, as well as by postnatal dispersal between the two. 3. We show that the location and timing of infection differs markedly between two sympatric malaria parasite species. For one species (P. circumflexum), our analyses indicate that infection occurs after birds have settled on breeding territories, and because the distribution of this parasite is temporally stable, hosts could in principle alter their exposure and potentially avoid infection through postnatal dispersal. Conversely, the spatial distribution of another parasite species (P. relictum) is unpredictable, and infection probability is positively associated with postnatal dispersal distance, potentially indicating that infection occurs during this major dispersal event. 4. These findings suggest that hosts in this population may be subject to divergent selection pressures from these two parasites, potentially acting at different life-history stages. Because this implies parasite species-specific predictions for many coevolutionary processes, they also illustrate the complexity of predicting such processes in multi-parasite systems.
Data from: Contrasting habitat and landscape effects on the fitness of a long-lived grassland plant under forest encroachment: do they provide evidence for extinction debt?
1. Habitat loss, fragmentation and transformation threaten the persistence of many species worldwide. Population and individual fitness are often compromised in small, degraded and isolated habitats, but extinction can be a slow process and extinction debts are common. 2. Long-lived species are prone to persist as remnant populations in low quality habitats for a long time, but the population and individual-level mechanisms of extinction debt remain poorly explored so far. 3. We here investigate the mechanisms involved in the long-term persistence of the common grassland specialist, long-lived, clonal plant Aphyllanthes monspeliensis L. (Asparagaceae). after forest encroachment into semi-natural Mediterranean calcareous grasslands in Catalonia (NE Iberian Peninsula). For this purpose we assess vegetative (aboveground and belowground) and reproductive plant performance indicators and their habitat and landscape (current and historical) drivers. 4. We confirm the existence of an extinction debt for this species, since current plant frequency is related to historical but not current connectivity, and we also find a positive effect of historical connectivity on seed set. In addition, current tree cover negatively affects individual size and aboveground/belowground biomass ratio, and biotic soil acidification leads to a reduction in the flowering probability of individuals and stems. 5. However, we also find that current connectivity negatively affects flowering and that tree cover enhances seed set. The forestation process, thus, also exerts a positive effect on some fitness traits, probably by providing a moister environment. 6. Synthesis. Habitat loss and deterioration result in a decreased vegetative performance of Aphyllanthes monspeliensis, a grassland specialist, but show contrasting effects on its reproductive performance. However, further forest encroachment would increase light competition and soil acidification, threatening its persistence and promoting the payment of the extinction debt if no conservation measures are taken.
Data from: Contrasting effects of landscape features on genetic structure in different geographical regions in the ornate dragon lizard, Ctenophorus ornatus
Habitat fragmentation can have profound effects on the distribution of genetic variation within and between populations. Previously, we showed that in the ornate dragon lizard, Ctenophorus ornatus, lizards residing on outcrops that are separated by cleared agricultural land are significantly more isolated and hold less genetic variation than lizards residing on neighbouring outcrops connected by undisturbed native vegetation. Here, we extend that fine-scale study to examine the pattern of genetic variation and population structure across the species' range. Using a landscape genetics approach, we test whether land clearing for agricultural purposes has affected the population structure of the ornate dragon lizard. We found significant genetic differentiation between outcrop populations (FST = 0.12), as well as isolation-by-distance within each geographic region. In support of our previous study, land clearing was associated with higher genetic divergences between outcrops and lower genetic variation within outcrops, but only in the region that had been exposed to intense agriculture for the longest period of time. No other landscape features influenced population structure in any geographic region. These results show that the effects of landscape features can vary across species' ranges and suggest there may be a temporal-lag in the response to contemporary changes in land use. These findings therefore highlight the need for caution when assessing the impact of contemporary land use practices on genetic variation and population structure.
Data from: Landscape genetics reveals contrasting patterns of connectivity in two newt species (Lissotriton montandoni and L. vulgaris)
<p><span>Ecologically distinct species may respond to landscape changes in different ways. </span>In addition to basic ecological data, <span>the extent of the</span> geographic range has been successfully used as an indicator of species sensitivity to anthropogenic landscapes, with widespread species usually found to be less sensitive compared to range-restricted species. <span>In this study, we investigate connectivity patterns of two closely related but ecologically distinct newt species – the range-restricted, <em>Lissotriton montandoni</em> and the widespread,<em> L. vulgari</em>s – using genomic data, a highly replicated setting (six geographic regions per species), and tools from landscape genetics. Our results show the importance of forest for connectivity in both species, but at the same time suggest differential use of forested habitat, with <em>L. montandoni</em> and <em>L. vulgaris</em> showing the highest connectivity at forest-core and forest-edges, respectively. Anthropogenic landscapes (i.e., higher crop- or urban-cover) increased resistance in both species, but the effect was one to three orders of magnitude stronger in <em>L. montandoni</em> than in <em>L. vulgaris</em>. </span><span>This result is consistent with a view of <em>L. vulgaris</em> as an ecological generalist. </span><span>Even so, currently, the negative impact of anthropogenic landscapes is mainly seen in connectivity among L. vulgaris populations, which show significantly stronger isolation and lower effective sizes relative to <em>L. montandoni</em>. Overall, this study emphasizes how habitat destruction is compromising genetic connectivity not only in endemic, range-restricted species of conservation concern but also in widespread generalist species, despite their comparatively lower sensitivity to anthropogenic landscape changes.</span></p>
Home range sizes of red deer in contrasting landscapes and implications for management
<p><span>Knowledge about deer spatial use is essential for damage mitigation and management coordination. Here we assess annual and seasonal home range sizes for </span><span>red deer in Sweden, based on data from GPS-marked deer in two regions with different management systems and contrasting landscapes</span><span>. We compare our findings with reviewed data on European red deer (<em>Cervus elaphus</em>) home range sizes in Europe. We found that female annual</span><span> home ranges (95% kernels) were 2.7 times larger in a mixed agricultural-forest landscape compared to a forest-dominated landscape. Core areas (50% kernels) were approximately 1/5 of the full annual home ranges (90% kernels) regardless of region. Home range size in the forest landscape showed little inter-seasonal variation whereas in the agricultural-forest landscape, home ranges were significantly larger during calving, hunt, and winter-spring compared to summer and rut. In the forest landscape, females had home range sizes that enables single red deer management areas to manage their own females. Whereas, within the agricultural-forest landscape, female spatial use cover several license units. Here, the coordinated license system is needed to reach trade-offs between goals of conservation, game management, and damage mitigation. Males had in general larger home ranges than females. The majority of the males made a seasonal migration to and from the rutting areas. The license system in the agricultural-forest landscape is large enough to manage migrating males, but in the forest landscape a coordination of several deer management areas is needed in order to encompass male spatial use. </span></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.