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31 results for “land use intensity”
Supporting data for "Mammalian species abundance across a gradient of tropical land-use intensity: A hierarchical multi-species modelling approach"
<p>Combined camera trap and live trap dataset underlying the analyses in a Biological Conservation paper (https://doi.org/10.1016/j.biocon.2017.05.007), provided in .csv format. This spatially- and temporally-replicated dataset is suitable for occupancy modelling.</p> <p>The first 3 columns in the dataset are:</p> <p>1) Trap location name – old-growth forest, logged forest and oil palm plantation locations have the prefixes "Old", "Log" and "Palm", respectively</p> <p>2) Sampling occasion number – camera trap and live trap occasions have the prefixes “Lvtrap” and “Ctrap”, respectively, and are defined in the paper</p> <p>3) Calendar year in which sampling took place (most locations were sampled in > 1 calendar years)</p> <p>Following these 3 columns, there are 66 columns for each of the mammal species detected during the study (species common names are used). The values for each species represent the number of independent captures, as defined in the paper. This can be reduced to detection/non-detection data (zeroes and ones), if needed, for occupancy modelling.</p>
Biodiversity impact assessment considering land use intensities and fragmentation
<p>The data provide supplementary information for the paper entitled "Biodiversity impact assessment considering land use intensities and fragmentation".</p><p> </p><p><strong>Coverage of the characterization factors</strong></p><ul><li>5 species groups: plants, amphibians, birds, mammals, and reptiles</li><li>5 broad land use types: cropland, pasture, plantations, managed forests, and urban areas</li><li>3 land use intensities: minimal, light, intense (sometimes, intensity levels had to be merged because the data did not allow to differentiate between them)</li><li>825 terrestrial ecoregions of the world (according to WWF / Olson et al. 2001)</li></ul><p> </p><p><strong>Files</strong></p><p>Main data</p><ul><li>CF.csv: characterization factors (CFs) for ecoregions and 5 species groups</li></ul><p>Taxonomically aggregated data</p><ul><li>CF_kingdom.csv: CFs aggregated from 5 species groups to plant and animal kingdoms</li><li>CF_domain.csv: CFs aggregated from plant and animal kingdoms to the domain of Eukaryota</li></ul><p>Spatially aggregated data</p><ul><li>CF_country.csv: CFs aggregated from ecoregions to countries</li><li>CF_global.csv: CFs aggregated from ecoregions to the globe</li></ul><p>Taxonomically and spatially aggregated data</p><ul><li>CF_kingdom_country.csv: CFs aggregated to countries and plant and animal kingdoms</li><li>CF_domain_country.csv: CFs aggregated to countries and the domain of Eukaryota</li><li>CF_kingdom_global.csv: CFs aggregated to the globe and plant and animal kingdoms</li><li>CF_domain_global.csv: CFs aggregated to the globe and the domain of Eukaryota</li></ul><p> </p><p><strong>Units</strong></p><p>CFs for land occupation: PDF/m2</p><p>CFs for land transformation: PDF⋅yr/m2</p><p> </p><p><strong>Columns</strong></p><ul><li>realm: 2-letter code to identify one of 8 biogeographical realms</li><li>biome: ID to identify one of 14 biomes</li><li>eco_id: ID to identify the ecoregion, combining numbers for the realm, biome, and ecoregion within each biome nested within each realm</li><li>eco_name: ecoregion name</li><li>species_group: species group</li><li>kingdom: kingdom as a taxonomic rank</li><li>habitat_id: ID to link to the land use type and intensity as used in land_use.tif. An ID with .5 represents a merged land use class considering the two habitats with the IDs when rounding the value both up and down.</li><li>habitat: land use type and intensity</li><li>CF_*: characterization factor</li><li>*_occ*: land occupation</li><li>*_tra*: land transformation</li><li>*_avg*: average approach</li><li>*_mar*: marginal approach</li><li>*_reg: regional relative species loss</li><li>*_glo: global relative species loss</li><li>*_rsd: relative standard deviation as a measure of spatial uncertainty due to aggregation (only concerns country and globally aggregated CFs)</li><li>quality_*: data quality, distinguishing between original estimates and the use of proxies</li><li>objectid: object id of the country</li><li>iso3cd: iso3 code of the country</li><li>romnam: romanized name of the country</li><li>m49code: M49 code of the country, a standard code used by the United Nations</li><li>weighting: aspect based on which the CFs were weighted (only concerns globally aggregated CFs)</li></ul><p> </p><p><strong>Data quality</strong></p><ul><li>original: original estimate (for globally aggregated CFs: mostly original estimates, proxies only considered in areas with current land use)</li><li>proxy_intensity: intensity level was missing; CF was derived from another CF of the same ecoregion and land use type but different intensity level and scaled to the right intensity level</li><li>proxy_type: land use type was missing; CF was derived from the average regional CFs for light use in the same biome and the ecoregion-specific GEP and scaled to the right intensity level if needed</li><li>proxy_gep: global extinction probability (GEP) was missing (only concerns CFs for global relative species loss); GEP estimated based on average GEP per area unit in the same biome and the ecoregion area</li><li>proxy_partial: some species groups were missing but not all (only concerns taxonomically aggregated CFs); aggregation done based on partly original estimates and partly proxies</li><li>proxy_neighbours: country was missing (only concerns country-aggregated CFs); values were estimated based on the average of the three nearest neighbouring countries</li><li>proxy: proxies were considered even in areas without current land use (only concerns globally aggregated CFs)</li></ul><p>Note: proxies in country-aggregated CFs apply to at least one of the ecoregions overlapping with the country and not necessarily all ecoregions</p><p> </p><p><strong>Land use type and intensity data</strong></p><p>Raster file: land_use.tif</p><p>Spatial resolution: 0.08333333, 0.08333333 (x, y)</p><p>Spatial extent: -180, 180, -90, 90 (xmin, xmax, ymin, ymax)</p><p>Coordinate reference system: WGS 84 (EPSG:4326)</p><p> </p><p>Codes</p><ol><li>Primary_vegetation_Minimal (incl. sparse/no vegetation)</li><li>Cropland_Intense</li><li>Cropland_Light</li><li>Cropland_Minimal</li><li>Managed_forest_Intense</li><li>Managed_forest_Light</li><li>Managed_forest_Minimal</li><li>Pasture_Intense</li><li>Pasture_Light</li><li>Pasture_Minimal</li><li>Plantation_Intense</li><li>Plantation_Light</li><li>Plantation_Minimal</li><li>Urban_Intense</li><li>Urban_Light</li><li>Urban_Minimal</li></ol>
Dataset on Article: River ecological status is shaped by agricultural land use intensity across Europe: Establishing a typology of farming-driven freshwater impacts
<p>This repository contains raw data from the article "River ecological status is shaped by agricultural land use intensity across Europe: Establishing a typology of farming-driven freshwater impacts" which is currently under review.</p> <p>It contains data to allocate the pressures (<strong>Data_pressure_allocation.csv</strong>) and calculate the Pressure Index (<strong>Data_pressure_index.csv</strong>) for Table 1, and for the Spearman correlations for Figure 2 (<strong>Data_Spearman_correlations.csv</strong>).</p> <p> </p> <p>Also available is the Shapefile used for the different agricultural maps (Figure 1 and Figure S1-S4):</p> <p><strong>Shapefile Schürings_et_al._2023</strong> (Coordinate system: ETRS 1989 UTM Zone 32N)</p> <p><strong>Attribute description</strong></p> <p>Id - Identifier of polygons</p> <p>gridcode - Code of agricultural archetypes of Levers et al., (2018)</p> <p>M_ZHYD: Unique identifier of corresponding FEC</p> <p>mars_bt12: River types</p> <p>eco_stat_2: Ecological status</p> <p>Biogeoregi: Biogeographical Regions - AN = Northern and Highland, Temp = Temperate, Mediterranean = Mediterranean</p> <p>Cum_pressu: Agricultural pressure index</p> <p>Nitrogen: Agricultural nitrogen pressure</p> <p>Pesticides: Agricultural pesticide pressure</p> <p>Hydromorph: Agricultural hydromorphological pressure</p> <p>Water_abst: Agricultural water abstraction</p>
Historic land use intensity (tau) development
<p>tau_xref_history_country.mz contains historic tau values on iso country level for total tau factor. This numbers were calculated by taking FAO yields and norming it to the 1995 tau values of the paper (faoyields*tau95/mean(faoyields[1995:2005]))</p>
Data from: The relative importance of green infrastructure as refuge habitat for pollinators increases with local land-use intensity
<p>1. Agricultural expansion and intensification have resulted in strong declines in farmland biodiversity across Europe. In many intensively farmed landscapes, linear landscape elements such as field boundaries, road verges and ditch banks are the main remaining green infrastructures providing refuge for biodiversity, and as such play a pivotal role in agri-environmental policies aiming at mitigating biodiversity loss. Yet, while we have a fairly good understanding of how agricultural intensification influences biodiversity on farmland, little is known about whether and how local land-use intensity affects biodiversity in nearby linear landscape elements and how this affects their role as biodiversity refuge.</p> <p>2. Focussing on pollinating insects, we examined the effects of local land-use intensity on biodiversity in agricultural fields and adjacent green infrastructures. In an intensively farmed area in South-Western France, we selected 23 agricultural grasslands and nearby field boundaries along a gradient in grassland cutting frequency which acted as a proxy for land-use intensity. We analysed how grassland cutting frequency affects species richness, abundance and community composition of wild bees and hoverflies in the grasslands and neighbouring field boundaries, and whether these effects differ across habitat types and species groups.</p> <p>3. Grassland cutting frequency negatively affected pollinator species richness and abundance in the grasslands, whereas pollinators in the neighbouring field boundaries were unaffected. These responses reflected the effects of cutting frequency on floral resources, with flower cover and richness decreasing in grasslands but not in field boundaries. As a result, the proportion of the local pollinator community supported by field boundaries increased with the increasing cutting frequency of the adjacent grassland.</p> <p>4. Common and rare pollinator species generally showed similar responses. Furthermore, communities of plants and pollinators in field boundaries next to intensively farmed grasslands were fairly similar to those next to extensively farmed ones.</p> <p>5. Synthesis and applications. Our results suggest that, as nearby land-use intensifies, flower-rich field boundaries become increasingly important as pollinator refuge habitats. Conserving field boundaries and other green infrastructures, and maintaining or enhancing their quality, therefore constitute important tools to conserve and promote pollinators in intensively farmed landscapes.</p>
Data from: Land-use intensity and relatedness to native plants promote exotic plant invasion in a tropical biodiversity hotspot
<p>Exotic plant invasions threaten biodiversity and are costly to farmers. Land use is a major pathway promoting the spread of exotic plant species; however, little is known about the processes underlying the success of exotic plants in tropical agricultural landscapes. Focussing on the heterogeneous smallholder landscapes of north-eastern Madagascar, we studied exotic plants of understorey communities across a land-use intensity gradient from unburned lands (old-growth forests, forest fragments, and forest-derived vanilla agroforests) to burned ones (fallow-derived vanilla agroforests, woody fallows, and herbaceous fallows). </p> <p>We quantified the absolute species richness, abundance, and cover of exotic plants across land-use types and their proportional contribution to community richness, abundance, and cover as indicators of exotic plant invasion. We tested for the effects of land-use parameters, namely land-use history, canopy closure, and landscape-level forest cover, on exotic plants. Additionally, we tested whether the phylogenetic relatedness between exotic and native species in the same plot affected invasion success, testing Darwin's naturalization and pre-adaptation hypotheses. </p> <p>All indicators of exotic plant invasion were lowest in old-growth forests and forest fragments and highest in fallow-derived vanilla agroforests, woody fallows, and herbaceous fallows. Absolute and proportional exotic richness was negatively affected by canopy closure, and landscapes with high forest cover had lower proportions of exotic plant richness. High phylogenetic relatedness between exotics and natives was associated with lower proportional richness but higher proportions of exotics in abundance and cover. However, individual exotic species showed contrasting responses to land-use parameters and relatedness to natives.</p> <p>Synthesis and applications: Our results indicate that maintaining unburned lands, land-use types with dense canopies, and landscapes with high forest cover prevents the spread of exotic plants within agricultural landscapes of north-eastern Madagascar. Supporting Darwin's pre-adaptation hypothesis, exotic plants phylogenetically closely related to native plants are more likely to become successful invaders in terms of abundance and cover. Nevertheless, individual species show different responses to land-use changes and phylogenetic relatedness. Therefore, land-use decisions and management choices can be tailored to limit the spread of exotic species and to preserve native plants in this global biodiversity hotspot.</p>
Deepened snow cover mitigates soil carbon loss from intensive land use in a semi-arid temperate grassland
<p>Carbon (C) loss due to soil erosion is a major issue in semi-arid grasslands. The extent of soil erosion is determined by soil properties and vegetation structure, especially during the non-growing season. In many Inner Mongolian grasslands, intensive land use, such as overgrazing and mowing, has severely reduced plant cover and damaged soil structure, which has exacerbated soil C loss by erosion. At the same time, increasing winter snowfall due to climate change is stimulating plant growth and altering plant composition. However, we do not know how changes in winter snow cover interact with land-use practices to regulate soil C loss due to erosion.</p> <p>Here, we conducted a six-year snow manipulation experiment under different land-use practices (control; moderately mowed, MM; heavily mowed, HM) to measure net changes in soil depth, soil C, plant biomass, and vegetation structure.</p> <p>After six years, soil C loss under ambient snow was three times greater in the MM and four times greater in the HM treatment compared with controls during non-growing season. However, deepened winter snow alleviated erosion-induced soil C loss by 14%, 47%, 16% in the controls, MM and HM treatments, respectively.</p> <p>The severity of soil C loss declined with increasing aboveground biomass (AGB), surface root biomass and vegetation structure. Vegetation structure and AGB explained more of the variation in soil C loss than surface root biomass, possibly because a complex canopy and plant cover increases overall surface roughness, thereby reducing soil C loss. Intensified land use reduced AGB, surface root biomass and vegetation structure, but deepened snow increased overall surface roughness by promoting AGB. Hence, our study demonstrates that deepened snow can alleviate soil C loss due to land use practices by promoting AGB.</p>
Result data from "Relative effects of land conversion and land-use intensity on terrestrial vertebrate diversity"
<p>These tif files contain the raw results of the publication "Relative effects of land conversion and land-use intensity on terrestrial vertebrate diversity", i.e. the raw data used to create the species richness loss maps shown in the main text and Supplementary Information of the publication. Please cite the publication when using these data.</p>
Forest quality and land use intensity indicators
<b>Description: </b><p>Trends in Biophysical Vegetation Traits of Tropical Forests under Logging and Fragmentation</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/81"><b>Trends in Biophysical Vegetation Traits of Tropical Forests under Logging and Fragmentation</b></a></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=22">here</a></p><p><b>Data worksheets: </b>There are 4 data worksheets in this dataset:</p><ol><li><p><b>Canopy-based forest quality metrics</b> (Worksheet Canopy)</p><p>Dimensions: 213 rows by 6 columns</p><p>Description: Fractional canopy cover and leaf area index</p><p>Fields: </p><ul><li><b>Plot</b>: SAFE project plot number (Field type: Location)</li><li><b>Date</b>: Date images were collected (Field type: Date)</li><li><b>LAI</b>: Leaf area index, corrected for clumping of leaves at plot level (25 m x 25 m) (Field type: Numeric)</li><li><b>fcover</b>: Fractional canopy cover (mean) (Field type: Numeric)</li><li><b>sdfcov</b>: Fractional canopy cover (standard deviation) (Field type: Numeric)</li></ul><br></li><li><p><b>Above-ground biomass</b> (Worksheet AGB)</p><p>Dimensions: 203 rows by 11 columns</p><p>Description: Was derived from DBH and Height of trees for individuals >= 10 cm DBH using five different algorithms on the raw data. We additionally binned heights of trees to account for uncertainties in tree height measurements and used multiple published equations combined with wood density estimates drawn from a distribution of wood density values that differs for unogged, logged and severely logged forest stands. We used oil palm specific equations for biomass estimations in oil palm plots. They will be identical estimates across the five algorithms used. Oil palms have a fundamentally different physical structure to forest trees, so we estimated AGB in oil palm plantations separately using the equation 〖AGB〗_palm= (0.3747*height*100+3.6334)/1000 (Thenkabail et al. 2004). See Pfeifer M, Lefebvre V, Turner E, Cusack J, Khoo M, Chey VK, Peni M, Ewers RMet al. 2015, Deadwood biomass: an underestimated carbon stock in degraded tropical forests?, ENVIRONMENTAL RESEARCH LETTERS, Vol: 10:044019. </p><p>Fields: </p><ul><li><b>Plot</b>: SAFE project plot number (Field type: Location)</li><li><b>Date</b>: Date field plot data were collected (Field type: Date)</li><li><b>AGB_Saner</b>: AGB estimates developed for mixed-species forest stands in East Kalimantan, Indonesia (Field type: Numeric)</li><li><b>AGB_Chave_wet</b>: AGB estimates developed for wet forest (Field type: Numeric)</li><li><b>AGB_Chave_moist</b>: AGB estimates developed for moist forest (Field type: Numeric)</li><li><b>AGB_K09</b>: AGB estimates developed for logged over old growth forest in Malaysian Sabah (Field type: Numeric)</li><li><b>AGB_N10</b>: AGB estimate developed for old growth forest in Malaysia for forests 110 km south-east of Kuala Lumpur (Field type: Numeric)</li><li><b>AGB_Chave14</b>: AGB estimates developed for pantropical forest assuming a wood density of 0.64 (Field type: Numeric)</li><li><b>AGB_Chave14_simulWD</b>: AGB estimates developed for pantropical forest and reflecting disturbance-induced changes to wood density (Field type: Numeric)</li><li><b>AGB_Chave14_Bin5_simulWD</b>: AGB estimates developed for pantropical forest and reflecting disturbance-induced changes to wood density (Field type: Numeric)</li></ul><br></li><li><p><b>SAFE project forest quality scores</b> (Worksheet Quality)</p><p>Dimensions: 203 rows by 4 columns</p><p>Description: Visual assessment of forest disturbance</p><p>Fields: </p><ul><li><b>Plot</b>: SAFE project plot number (Field type: Location)</li><li><b>Date</b>: Date of assessment (Field type: Date)</li><li><b>ForestQuality</b>: SAFE Project forest quality scores (Field type: Ordered Categorical)</li></ul><br></li><li><p><b>Caneye software analyses carried out using Caneye v6.3.8 in August/September 2013</b> (Worksheet LAI_Caneye)</p><p>Dimensions: 237 rows by 16 columns</p><p>Description: LAI, fcover and fAPAR estimates derived from hemispherical images or using Sunscan Delta T device (Cambridge) if applicable</p><p>Fields: </p><ul><li><b>Plotname</b>: SAFE project plot number (Field type: Location)</li><li><b>Date</b>: Date on which photographs were taken (Field type: Date)</li><li><b>HemiUp</b>: Number of sample points = number of pictures taken - upward looking fisheye pictures (Field type: Numeric)</li><li><b>LAI_eff_v6</b>: LAI effective estimated following algorithm of Caneye version 6 (v6.3.8) (Field type: Numeric)</li><li><b>LAI_true_v6</b>: LAI true (accounting for vegetation clumping) estimated following algorithm of Caneye version 6 (v6.3.8) (Field type: Numeric)</li><li><b>LAI_eff_v5</b>: LAI effective estimated according to Caneye version 5 (Field type: Numeric)</li><li><b>LAI_true_v5</b>: LAI true (accounting for vegetation clumping) estimated according to Caneye version 5 (Field type: Numeric)</li><li><b>ALAeffv5</b>: Effective average leaf inclination angle following alogorith used in Caneye v5 (Field type: Numeric)</li><li><b>ALAtruev5</b>: True average leaf inclination angle following alogorith used in Caneye v5 (Field type: Numeric)</li><li><b>Fap_meas_Dir</b>: Black - sky direct fAPAR (fraction of absorbed photosynthetically active radiation) measured (Field type: Numeric)</li><li><b>Fap_mod_Dir</b>: Black - sky direct fAPAR (fraction of absorbed photosynthetically active radiation) modelled (Field type: Numeric)</li><li><b>Fap_meas_Dif</b>: White - sky diffuse fAPAR (fraction of absorbed photosynthetically active radiation) measured (Field type: Numeric)</li><li><b>Fap_mod_Dif</b>: White - sky diffuse fAPAR (fraction of absorbed photosynthetically active radiation) modelled (Field type: Numeric)</li><li><b>fcover</b>: Fractional canopy cover (mean). Cover fraction (fcover) is defined as the fraction of the soil covered by the vegetation viewed in the nadir direction. Using hemispherical images, the cover fraction must be integrated over a range of zenith angles (0-10 degrees) (Field type: Numeric)</li><li><b>sdfcov</b>: Fractional canopy cover (standard deviation). Cover fraction (fcover) is defined as the fraction of the soil covered by the vegetation viewed in the nadir direction. Using hemispherical images, the cover fraction must be integrated over a range of zenith angles (0-10 degrees) (Field type: Numeric)</li></ul><br></li></ol><p><b>Date range: </b>2010-07-01 to 2014-01-10</p><p><b>Latitudinal extent: </b>4.4245 to 4.7714</p><p><b>Longitudinal extent: </b>116.9477 to 117.7028</p>
Quantifying Predation Pressure Along a Gradient of Land Use Intensity
<b>Description: </b><p>Experimentally quantified mealworm predation rates</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/2"><b>Quantifying Predation Pressure Along a Gradient of Land Use Intensity</b></a></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=76">here</a></p><p><b>Data worksheets: </b>There are 1 data worksheets in this dataset:</p><ol><li><p><b>Experimental predation trials</b> (Worksheet Data)</p><p>Dimensions: 1030 rows by 16 columns</p><p>Description: Full experimental and environmental data accompanying each predation measurement</p><p>Fields: </p><ul><li><b>Date</b>: Date mealworm was placed in field (Field type: Date)</li><li><b>Location</b>: SAFE Project sample site (Field type: Location)</li><li><b>Point</b>: SAFE Project sample site (Field type: ID)</li><li><b>Group</b>: Group nested within Point within Date (Field type: ID)</li><li><b>Time</b>: 12 hour period during which mealworm was left in the field (Field type: Categorical)</li><li><b>Treat</b>: Experimental treatment (Field type: Categorical)</li><li><b>Level</b>: Height strata mealworm was placed at (Field type: Categorical)</li><li><b>Status</b>: Was the mealworm predated or not? (Field type: Categorical)</li><li><b>Zfloor</b>: Percentage vegetation cover at ground level (Field type: Numeric)</li><li><b>Z1m</b>: Percentage vegetation cover 1.5m above ground (Field type: Numeric)</li><li><b>Z2m</b>: Percentage vegetation cover 2m above ground (Field type: Numeric)</li><li><b>Litdep</b>: Leaf litter depth (Field type: Numeric)</li><li><b>Canh</b>: Canopy height (Field type: Numeric)</li><li><b>Cand</b>: Canopy cover (Field type: Numeric)</li><li><b>Rain</b>: How heavily did it rain last night? 0 being no rain and 5 being torrential rain (Field type: Numeric)</li></ul><br></li></ol><p><b>Date range: </b>2012-06-21 to 2012-12-13</p><p><b>Latitudinal extent: </b>4.6350 to 4.7510</p><p><b>Longitudinal extent: </b>116.9632 to 117.5861</p>
Data for: "Land-use intensity influences European tetrapod food-webs"
<p>These .Rdata files enable to reproduce results from our article " Signatures of land use intensity on european tetrapod food-web architectures" along with the R code provided at: https://github.com/ChrisBotella/foodwebs_vs_land_use</p> <p>- raw_data : Raw data including GBIF and iNaturalist occurrences and IUCN enveloppes used to select sites and generate species presence/absence. We provide this file for transparency and reproducibility of our methodology.</p> <p>- preprocessed_data: preprocessed data (obtained from raw_data) used to generate our article Figures along with the next file.</p> <p>- TrophicNetworksList : .Rdata containing a list of igraph objects, each igraph is a foodweb associated to a site identify by the list element name. </p> <p>- MultiRegMatrices: .Rdata containing especially the pre-computed matrix Y of cells (rows) by food web metrics (columns) and the covariate design matrix X (for the linear regressions) in order to facilitate and accelerate the reproduction of the analyses.</p>
Inconsistent responses of carabid beetles and spiders to land-use intensity and landscape complexity in Northwestern Europe
<p>Reconciling biodiversity conservation with agricultural production requires a better understanding of how key ecosystem service providing species respond to agricultural intensification. Carabid beetles and spiders represent two widespread guilds providing biocontrol services. Here we surveyed carabid beetles and spiders in 66 winter wheat fields in four Northwestern European countries and analyzed how the activity density and diversity of carabid beetles and spiders were related to crop yield (proxy for land-use intensity), percentage cropland (proxy for landscape complexity) and soil organic carbon content, and whether these patterns differed between dominant and non-dominant species. Less than 17% of carabid or spider species were classified as dominant, which accounted for more than 90% of individuals respectively. We found that carabids and spiders were generally related to different aspects of agricultural intensification. Carabid species richness was positively related with crop yield and evenness was negatively related to crop cover. The activity density of non-dominant carabids was positively related with soil organic carbon content. Meanwhile, spider species richness and non-dominant spider species richness and activity density were all negatively related to percentage cropland. Our results show that practices targeted to enhance one functionally important guild may not promote another key guild, which helps explain why conservation measures to enhance natural enemies generally do not ultimately enhance pest regulation. Dominant and non-dominant species of both guilds showed mostly similar responses suggesting that management practices to enhance service provisioning by a certain guild can also enhance the overall diversity of that particular guild.</p>
Inconsistent responses of carabid beetles and spiders to land-use intensity and landscape complexity in Northwestern Europe
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Deepened snow cover mitigates soil carbon loss from intensive land use in a semi-arid temperate grassland
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Data from: Assessing the effects of land‑use intensity on small mammal community composition and genetic variation in Myodesglareolus and Microtus arvalis across grassland and forest habitats
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Data from: The relative importance of green infrastructure as refuge habitat for pollinators increases with local land-use intensity
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Data from: Land-use intensity and relatedness to native plants promote exotic plant invasion in a tropical biodiversity hotspot
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Data from: Does mixed-species flocking influence how birds respond to a gradient of land-use intensity?
Conservation biology is increasingly concerned with preserving interactions among species such as mutualisms in landscapes facing anthropogenic change. We investigated how one kind of mutualism, mixed-species bird flocks, influences the way in which birds respond to different habitat types of varying land-use intensity. We use data from a well-replicated, large-scale study in Sri Lanka and the Western Ghats of India, in which flocks were observed inside forest reserves, in 'buffer zones' of degraded forest or timber plantations, and in areas of intensive agriculture. We find flocks affected the responses of birds in three ways: (i) species with high propensity to flock were more sensitive to land use; (ii) different flock types, dominated by different flock leaders, varied in their sensitivity to land use and because following species have distinct preferences for leaders, this can have a cascading effect on followers' habitat selection; and (iii) those forest-interior species that remain outside of forests were found more inside flocks than would be expected by chance, as they may use flocks more in suboptimal habitat. We conclude that designing policies to protect flocks and their leading species may be an effective way to conserve multiple bird species in mixed forest and agricultural landscapes.
Effectiveness of agri-environmental management on pollinators is moderated more by ecological contrast than by landscape structure or land-use intensity
<p>Study dataset</p>
Data from: Land-use type and intensity differentially filter traits in above- and belowground arthropod communities
1. Along with the global decline of species richness goes a loss of ecological traits. Associated biotic homogenization of animal communities and narrowing of trait diversity threaten ecosystem functioning and human well-being. High management intensity is regarded as an important ecological filter, eliminating species that lack suitable adaptations. Belowground arthropods are assumed to be less sensitive to such effects than aboveground arthropods. 2. Here, we compared the impact of management intensity between (grassland vs. forest) and within land-use types (local management intensity) on the trait diversity and composition in below- and aboveground arthropod communities. 3. We used data on 722 arthropod species living above ground (Auchenorrhyncha and Heteroptera), primarily in soil (Chilopoda and Oribatida) or at the interface (Araneae and Carabidae). 4. Our results show that trait diversity of arthropod communities is not primarily reduced by intense local land use, but is rather affected by differences between land-use types. Communities of Auchenorrhyncha and Chilopoda had significantly lower trait diversity in grassland habitats as compared to forests. Carabidae showed the opposite pattern with higher trait diversity in grasslands. Grasslands had a lower proportion of large Auchenorrhyncha and Carabidae individuals, whereas Chilopoda and Heteroptera individuals were larger in grasslands. Body size decreased with land-use intensity across taxa, but only in grasslands. The proportion of individuals with low mobility declined with land-use intensity in Araneae and Auchenorrhyncha, but increased in Chilopoda and grassland Heteroptera. The proportion of carnivorous individuals increased with land-use intensity in Heteroptera in forests and in Oribatida and Carabidae in grasslands. 5. Our results suggest that gradients in management intensity across land-use types will not generally reduce trait diversity in multiple taxa, but will exert strong trait filtering within individual taxa. The observed patterns for trait filtering in individual taxa are not related to major classifications into above- and belowground species. Instead, ecologically different taxa resembled each other in their trait diversity and compositional responses to land-use differences. These previously undescribed patterns offer an opportunity to develop management strategies for the conservation of trait diversity across taxonomic groups in permanent grassland and forest habitats.
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