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2,603 results for “Ecological data”
Data and code from: Spatial ecology of the Turks & Caicos boa, Chilabothrus c. chrysogaster Cope, 1871 (Serpentes: Boidae)
<p><span>Obtaining ecological and natural history data from cryptic squamates can be challenging, but is crucial to understanding species' biology, particularly in the context of conservation. In the Greater Antilles, this challenge is especially apparent, particularly among the West Indian boas (genus <em>Chilabothrus</em>). Most species have had only minimal natural history study, with a few exceptions. The Turks & Caicos boa (<em>C. chrysogaster</em>) has been studied intensively for over 16 years on the small privately owned island of Big Ambergris Cay, Turks and Caicos Islands. We conducted a multi-year radio-tracking study on the species to generate information relevant to spatial habitat use and movement that will inform conservation decision-making in the face of increasing development pressure. We tracked a total of 19 female snakes using surgically implanted transmitters, enabling us to obtain between 16 and 40 location observations per boa over the lifetime of each transmitter. We estimated home ranges, the core space used by an animal, using range distributions, finding that females have a home range of 0.70 ha to 1.2 ha. We also estimated occurrence distributions, the use of space between specific time intervals, finding an average occurrence area of 1.62 ha. Several females overlapped in their spatial habitat use, and we observed female boas using two novel habitats for the species (iron shore wrack and red mangrove). This study provides valuable information on the spatial ecology of an endangered boa and will serve to inform conservation work that is currently underway. </span></p>
Data from: Fluctuation of ecological niches and geographic range shifts along chile pepper's domestication gradient
<p>Domestication is an ongoing well-described process. However, while many have stud- ied the changes domestication causes in plant genetics, few have explored its impact on the portion of the geographic landscape in which the plants exist. Therefore, the goal of this study was to understand how the process of domestication changed the geographic space suitable for chile pepper (<em>Capsicum annuum</em>) in its center of origin (domestication). <em>C. annuum</em> is a major crop species globally whose center of domes- tication, Mexico, has been well-studied. It provides a unique opportunity to explore the degree to which ranges of different domestication classes diverged and how these ranges might be altered by climate change. To this end, we created ecological niche models for four domestication classes (wild, semiwild, landrace, modern cultivar) based on present climate and future climate scenarios for 2050, 2070, and 2090. Considering present environment, we found substantial overlap in the geographic niches of all the domestication classes. Yet, environmental and geographic aspects of the current ranges did vary among classes. Wild and commercial varieties could grow in desert conditions, while landraces could not. With projections into the future, habitat was lost asymmetrically, with wild, semiwild, and landraces at greater risk of territorial declines than modern cultivars. Further, we identified areas where future suitability overlap between landraces and wilds is expected to be lost. While range expansion is widely associated with domestication, we found little support of a con- stant niche expansion (either in environmental or geographical space) throughout the domestication gradient in chile peppers in Mexico. Instead, particular domestication transitions resulted in loss, followed by capturing or recapturing environmental or geographic space. The differences in environmental characterization among domes- tication gradient classes and their future potential range shifts increase the need for conservation efforts to preserve landraces and semiwild genotypes</p>
Data from: Mating environments mediate the evolution of behavioral isolation during ecological speciation
<p>The evolution of behavioral isolation is often the first step towards speciation. While past studies show that behavioral isolation will sometimes evolve as a by-product of divergent ecological selection, we lack a more nuanced understanding of factors that may promote or hamper its evolution. The environment in which mating occurs may be important in mediating whether behavioral isolation evolves for two reasons. Ecological speciation could occur as a direct outcome of different sexual interactions being favored in different mating environments. Alternatively, mating environments may vary in the constraint they impose on traits underlying mating interactions, such that populations evolving in a 'constraining' mating environment would be less likely to evolve behavioral isolation than populations evolving in a less constraining mating environment. In the latter, mating environment is not the direct cause of behavioral isolation but rather permits its evolution only if other drivers are present. We test these ideas with a set of 28 experimental fly populations, each of which evolved under one of two mating environments and one of two larval environments. Counter to the prediction of ecological speciation by mating environment, behavioral isolation was not maximal between populations evolved in different mating environments. Nonetheless, mating environment was an important factor as behavioral isolation evolved among populations from one mating environment but not among populations from the other. Though one mating environment was conducive to the evolution of behavioral isolation, it was not sufficient: assortative mating only evolved between populations adapting to different larval environments within that mating environment, indicating a role for ecological speciation. Intriguingly, the mating environment that promoted behavioral isolation is characterized by less sexual conflict compared to the other mating environment. Our results suggest that mating environments plays a key role in mediating ecological speciation via other axes of divergent selection.</p>
Data used for "Assessing spatiotemporal change in coral reef social-ecological systems"
<p>Coral reef data used for Eason, T. and Garmestani, A. S., Assessing spatiotemporal change in coral reef ecosystems. Under Review (Ecology and Society)</p> <p>The raw data was gathered from a coral bleaching study performed by Sully et al (2019). In our current study, we used data spanning from 2003-2016. We amalgamated station names and associated data when the station locations (latitude and longitude) remained essentially the same, but were named slightly different from year to year. </p> <p>Reference: Sully S, Burkepile DE, Donovan MK, Hodgson G, van Woesik R. A global analysis of coral bleaching over the past two decades. Nat Commun. 2019;10(1):1264</p>
A lack of open data standards for large infrastructure projects hampers social-ecological research in the Brazilian Amazon
<p>List of papers used in literature review for "A lack of open data standards for large infrastructure projects hampers social-ecological research in the Brazilian Amazon"</p>
Data from: The spotted parrotfish genome provides evolutionary insight into the ecological adaptation of a keystone dietary specialist
<p>With over 600 valid species, the wrasses (family Labridae) are among the largest and most successful of the marine teleosts. They feature prominently on coral reefs where they are known not only for their impressive diversity in colouration and form, but also in their functional specialization and ability to occupy a wide variety of trophic guilds. Among the wrasses, the parrotfishes (tribe Scarini) display some one of the most dramatic examples of trophic specialization. Using abrasion-resistant biomineralized teeth, parrotfishes are able to mechanically extract protein-rich micro-photoautotrophs growing in and amongst reef carbonate material, a dietary niche that is inaccessible to most other teleost fishes. This ability to exploit an otherwise untapped trophic resource is thought to have played a role in the diversification and evolutionary success of the parrotfishes. In order to better understand the key evolutionary innovations leading to the success of these dietary specialists, we sequenced and analysed the genome of a representative species, the spotted parrotfish (<em>Cetoscarus ocellatus</em>). We find significant expansion, selection, and duplication within several detoxification gene families and a novel poly-glutamine expansion in the enamel protein ameloblastin, and we consider their evolutionary implications. Our genome provides a useful resource for comparative genomic studies investigating the evolutionary history of this highly specialized teleostean radiation.</p>
Data from: Interactions between sexual signaling, thermoregulation and body size drive ecology and evolution of wing colors in Odonata
<p>This dataset consists of images of the fore and hind wings (and associated metadata) of 4091 individual odonate specimens, and thus over 8000 wings, imaged on a commercially-available Epson desktop flatbed scanner and color-calibrated using a color-checker, comprising the Targeted Odonata Wing Digitization dataset (TOWD; <a href="https://digitizingdragonflies.org/">https://digitizingdragonflies.org/</a>) The odonates imaged are all from the Nearctic, and represent 343 species. </p> <p>In this dataset, 47% of images come from the Alabama Museum of Natural History (ALMNH), 19% from the PhD thesis collection of William Kuhn (now housed at the American Museum of Natural History, AMNH), 19% from the collection of the late Michael L. May, and 13% from Jessica Ware’s Rutgers-University Newark collection (now housed at the AMNH). </p> <p>Files are individual PNGs where transparency is the background. </p> <p>Metadata includes species, sex, and county. </p>
Data from: Leveraging satellite observations to reveal ecological drivers of pest densities across landscapes
<p>Landscape ecologists have long suggested that pest abundances increase in simplified, monoculture landscapes. However, tests of this theory often fail to predict pest population sizes in real-world agricultural fields. These failures may arise not only from variations in pest ecology but also from the widespread use of categorical land-use maps that do not adequately characterize habitat availability for pests. We used 1163 field-year observations of <em>Lygus hesperus</em> (Western Tarnished Plant Bug) densities in California cotton fields to determine whether integrating remotely sensed metrics of vegetation productivity and phenology into pest models could improve pest abundance analysis and prediction. Because <em>L. hesperus</em> often overwinters in non-crop vegetation, we predicted that pest abundances would peak on farms surrounded by more non-crop vegetation, especially when the non-crop vegetation is initially productive but then dries down early in the year, causing the pest to disperse into cotton fields. We found that the effect of non-crop habitat on pest densities varied across latitudes, with a positive relationship in the north and a negative one in the south. Aligning with our hypotheses, models predicted that <em>L. hesperus</em> densities were 35 times higher on farms surrounded by high versus low productivity non-crop vegetation (EVI area 350 vs. 50) and 2.8 times higher when dormancy occurred earlier versus later in the year (May 15 vs. June 30). Despite these strong and significant effects, we found that integrating these remote-sensing variables into land-use models only marginally improved pest density predictions in cotton compared to models with categorical land cover metrics alone. Together, our work suggests that the remote sensing variables analyzed here can advance our understanding of pest ecology, but not yet substantively increase the accuracy of pest abundance predictions.</p>
Data from: Distribution, ecology, and natural history of the recently rediscovered and critically endangered Santa Marta Sabrewing
<div><strong>Description for "RawData&media_Cphainopeplus" dataset.</strong></div> <div> </div> <div>Data from: Distribution, ecology, and natural history of the recently rediscovered and critically endangered Santa Marta Sabrewing</div> <div>MS bioRxiv ID: </div> <div>Article DOI:</div> <div> </div> <div>Please address questions to:</div> <div> </div> <div>Esteban Botero D., Dr.Sc.</div> <div>Director of Conservation Science</div> <div>SELVA: Research for Conservation in the Neotropics</div> <div>http://www.selva.org.co</div> <div>https://www.selva.org.co/integrantes/esteban-botero-delgadillo/</div> <div>e-mail: eboterod@gmail.com; esteban.botero@selva.org.co</div> <div> </div> <div>=====================================================================================</div> <div>=====================================================================================</div> <div> </div> <div> </div> <div><strong>General information:</strong></div> <div> </div> <div>The whole dataset contains information on Santa Marta Sabrewing, a critically endangered (CR) hummingbird species endemic to the Sierra Nevada de Santa Marta, northern Colombia. Part of the data are of public access and consist of historical geographic records, while another part contains data collected from a focal study population of Santa Marta Sabrewing inhabiting along the La Macana stream, in the Guatapurí River Basin, Cesar Department. No exact coordinates from the focal population were included in this dataset or related documents/materials to protect these locations.</div> <div> </div> <div>The data set is comprised by an Excel file (four spreadsheets) and two short video files that are explained below.</div> <div> </div> <div>*************************************************************************************</div> <div> </div> <div><strong>Excel file "RawData_Cphainopeplus.xlsx" (created 12-02-2024)</strong></div> <div> </div> <div> </div> <div>********** Spreadsheet "1. ConfirmedLoc" **********</div> <div>This spreadsheet contains metadata for the only five records of Santa Marta Sabrewing with confirmatory evidence.</div> <div> </div> <div>The matrix contains the following variables:</div> <div> </div> <div>VARIABLE DESCRIPTION</div> <div> </div> <div>Locality: Name of the locality as described in museums or public databases.</div> <div>Department: Name of department where the locality is found.</div> <div>Latitude: Latitude in decimal degrees.</div> <div>Longitude: Longitude in decimal degrees.</div> <div>Record status: Confirmed (with confirmatory evidence) or unconfirmed (without or needing further examination).</div> <div>Evidence: Evidence available to confirm the record.</div> <div>Source: Source of the record.</div> <div>Observations: Additional information for each row.</div> <div> </div> <div> </div> <div>********** Spreadsheet "2. PointCountDat" **********</div> <div>This spreadsheet contains information from bird counts aimed at detecting Santa Marta Sabrewing.</div> <div> </div> <div>The matrix contains the following variables:</div> <div> </div> <div>VARIABLE DESCRIPTION</div> <div> </div> <div>ID: Point-count station ID.</div> <div>Elevation: Elevation in m.</div> <div>Native: Proportion (0–1) of native vegetation 100 m around the point coordinates.</div> <div>Transformed: Proportion (0–1) of transformed vegetation 100 m around the point coordinates.</div> <div>R1: First point-count replicate, with 0 = absence and 1 = presence.</div> <div>R2: Second point-count replicate, with 0 = absence and 1 = presence.</div> <div>R3: Third point-count replicate, with 0 = absence and 1 = presence.</div> <div>R4: Fourth point-count replicate, with 0 = absence and 1 = presence.</div> <div> </div> <div> </div> <div>********** Spreadsheet "3. Behav" **********</div> <div>This spreadsheet contains information from ad libitum observations to characterize habitat associations and general behaviour of Santa Marta Sabrewing.</div> <div> </div> <div>The matrix contains the following variables:</div> <div> </div> <div>VARIABLE DESCRIPTION</div> <div> </div> <div>Date: Date (day/month/year).</div> <div>Hour: Hour in 24h format.</div> <div>Elevation: Elevation in m.</div> <div>No. Indiv.: No. of individuals of Santa Marta Sabrewing recorded.</div> <div>Behaviour: Simple categories describing sabrewing behaviour.</div> <div>Habitat: Simple categories describing habitat type where sabrewing was recorded.</div> <div> </div> <div> </div> <div>********** Spreadsheet "4. HabitatNeu" **********</div> <div>This spreadsheet contains estimated quantities for estimating Neu´s habitat proportions for Santa Marta Sabrewing. </div> <div> </div> <div>The matrix contains the following variables:</div> <div> </div> <div>VARIABLE DESCRIPTION</div> <div> </div> <div>Habitat: Simple categories describing habitat type where sabrewing was recorded.</div> <div>Obs. Count: No. of Santa Marta Sabrewing records in each habitat type.</div> <div>Hab. Prop.: Proportional area (0–1) of each habitat type in study area.</div> <div>Expected Use: Expected No. of records based on each habitat's proportional area.</div> <div>Sel. Ratio: Ratio between observed count and expected use.</div> <div>Standard. Ratio: Standardized selection ratio (0–1).</div> <div> </div> <div>*************************************************************************************</div> <div> </div> <div><strong>Video file "Campylopterus_threat.mp4" (modified 09-02-2024)</strong></div> <div> </div> <div>A slow-motion (~0.1 x) video showing a perching male Santa Marta Sabrewing involved in a persecution flight with a conspecific male aggressor. <span>Footage was taken along the course of La Macana stream, near Chemesquemena village (Cesar Department, northern Colombia). </span>Video: Elquin Toro © (reproduced with permission).</div> <div> </div> <div>*************************************************************************************</div> <div> </div> <div><strong>Video file "Campylopterus_fight.mp4" (modified 09-02-2024)</strong></div> <div> </div> <div>A lek-attending male Santa Marta Sabrewing vocalizing in a perch and subsequently adopting a threatening body posture with wing and tail feathers extended towards a conspecific male intruder. <span>Footage was taken along the course of La Macana stream, near Chemesquemena village (Cesar Department, northern Colombia). </span>Video: Elquin Toro © (reproduced with permission).</div> <div> </div> <div> </div> <div>=====================================================================================</div> <div> </div> <div> </div> <div><strong>Methodological information (for more details, please see the related manuscript):</strong></div> <div> </div> <div>We conducted a thorough revision of scientific literature, international public databases, digital collections, and museum international/national bird collections in search of geographical records of Santa Marta Sabrewing.</div> <div> </div> <div>Aside from the locality where the focal population was found (along the La Macana stream, near the Chemesquemena village, Cesar department), we also conducted three 4–7-day field expeditions to three other localities where Santa Marta Sabrewing could stably occur: San Lorenzo ridge (Magdalena Department); the upper Rioancho River basin, in Dibulla (La Guajira Department); Aracataca (Magdalena Department).</div> <div> </div> <div>We projected Santa Marta Sabrewing's extent of occurrence (EOO) and area of occupancy (AOO). Our EOO and AOO projections were compared with the threshold values given under the IUCN’s criteria B1 and B2 (IUCN 2022). To this end, we delimited suitable habitat in ArcGIS Desktop 10 (ESRI 2011) using a vegetation layer from the CORINE Land Cover Methodology from Colombia, 2018, at 1:100,000 scale (IDEAM 2021), and a layer of biomes compiled in the public-access portal of the Land-use Planning Geographic Information System (SIG-OT) of the “Agustín Codazzi” Geographic Institute of Colombia (https://geoportal.igac.gov.co).</div> <div> </div> <div>We conducted bird counts from September to December 2022 to estimate local abundance along the course of La Macana stream, near Chemesquemena village. Bird surveys were carried out using 20 georeferenced point-count stations (30 m radius), in which one observer searched for Santa Marta Sabrewing for 15 min. One count per station was conducted every month between 07:00 to 11:00s. Detection histories were analysed using the <em>occu</em> function in the <em>unmarked</em> package (Fiske and Chandler 2011) in R 4.0.2 (R Core Team 2020).</div> <div> </div> <div>In the same locality, we also conducted ad libitum observations from July 2022 to October 2023 to describe different aspects of the species’ natural history. We conducted 2–3-day monthly visits to monitor individuals and territories year-round. We described feeding habits and aspects of social or breeding behaviour, including territorial and lekking displays, vocal activity, and lek conformation.</div> <div> </div> <div>Lastly, we performed acoustic analysis focused on a description of Santa Marta Sabrewing territorial calls based on three males. We selected and analyzed a single high-quality recording per male of 20 seconds each. We used a Zoom H6 recorder coupled with a Rode NTG4 phantom-powered shotgun microphone. We visualized and analyzed the recordings using the Seewave package (Sueur et al. 2008) in R.</div> <div> </div> <div> </div> <div><strong>References:</strong></div> <div> </div> <div>ESRI (2011). ArcGIS Desktop: Release 10. Redlands, CA, USA: Environmental Systems Research Institute.</div> <div> </div> <div>Fiske, I., and Chandler, R. (2011). “unmarked”: an R package for Fitting Hierarchical Models of Wildlife Occurrence and Abundance. J. Stat. Soft. 43: 1–23.</div> <div> </div> <div>IDEAM (2021). Leyenda nacional de coberturas de la tierra. Metodología CORINE Land Cover adaptada para Colombia escala 1:100.000 (período 2018). Bogotá, Colombia: Instituto de Hidrología, Meteorología y Estudios Ambientales (IDEAM).</div> <div> </div> <div>IUCN Standards and Petitions Committee (2022). Guidelines for using the IUCN Red List Categories and Criteria. Version 15. Prepared by the Standards and Petitions Committee. https://www.iucnredlist.org/documents/RedListGuidelines.pdf</div> <div> </div> <div>R Core Team. (2020). R: a language and environment for statistical computing, version 4.0.2. R Foundation for Statistical Computing, Vienna, Austria, http://www.R.project.org</div> <div> </div> <div>Sueur, J., Aubin, T., and Simonis, C. (2008). Seewave: a free modular tool for sound analysis and synthesis. Bioacoustics 18: 213–226.</div> <div> </div> <div>=====================================================================================</div> <div>=====================================================================================</div> <p> </p>
Data from: Ecology of fear alters behaviour of grizzly bears exposed to bear-viewing ecotourism
<p>Humans are perceived as predators by many species and may generate landscapes of fear, influencing the spatiotemporal activity of wildlife. Additionally, wildlife might seek out human activity when faced with predation risks (human shield hypothesis). We used the Anthropause, a decrease in human activity resulting from the COVID-19 pandemic, to test the ecology of fear and human shield hypotheses and quantify the effects of bear-viewing ecotourism on grizzly bear (<em>Ursus arctos</em>) activity. We deployed camera traps in the Khutze watershed in Kitasoo Xai'xais Territory in the absence of humans in 2020 and with experimental treatments of variable human activity when ecotourism resumed in 2021. Daily bear detection rates decreased with more people present and increased with days since people were present. Human activity was also associated with more bear detections at forested sheltered sites, and less at exposed sites, likely due to the influence of habitat on bear perception of safety. The number of people negatively influenced adult male detection rates, but we found no influence on females with young detections, providing no evidence that females responded behaviourally to a human shield effect from reduced male activity. We also observed apparent trade-offs of risk avoidance and foraging. When salmon levels were moderate to high, detected bears were more likely to be females with young than adult males on days with more people present. Should managers want to minimize human impacts on bear activity and maintain baseline age-sex class composition at ecotourism sites, multi-day closures and daily occupancy limits may be effective. More broadly, this work revealed that antipredator responses can vary with the intensity of risk cues, habitat structure, and forage trade-offs, as well as manifest as the altered age-sex class composition of individuals using human-influenced areas, highlighting that wildlife avoids people across multiple spatiotemporal scales.</p>
Data from: Developing spatially explicit and stochastic measures of ecological departure
<p>Background: Ecological departure is a metric applied to mapped ecological systems measuring dissimilarity between the distributions of observed and expected proportions of non-stochastic reference vegetation classes within an area.</p> <p>Aims: We created spatially explicit measures of ecological departure incorporating stochasticity for each ecological system and all ecological systems from a central Nevada USA landscape.</p> <p>Methods: Spatially explicit ecological departures were estimated from a radius from each pixel governed by a distance-decay function within a moving window. Variability was introduced by simulating replicate climate time series for each spatial reference condition and calculating departure per replicate.</p> <p>Key results: Single system spatial ecological departure was highly and extensively departed, except for one area of low-elevation groundwater-dependent systems. Variance of spatial ecological departure was extensively low, except in areas of lower ecological departure, despite vegetation differences among replicates. The multiple-system ecological departure exhibited lower ecological departure.</p> <p>Conclusions: Spatial ecological departure was warranted for efficient land management as results were concordant between non-spatial and spatial metrics; however, rapid coding languages will be required.</p>
Data from: Ecological and anthropogenic drivers of waterfowl productivity are synchronous across species, space, and time
<p>We used hierarchical random-effects models to examine interspecific and spatial variation in annual productivity in six migratory ducks (i.e., American wigeon [<em>Mareca americana</em>], blue-winged teal [<em>Spatula discors</em>], gadwall [<em>Mareca strepera</em>], green-winged teal [<em>Anas crecca</em>], mallard [<em>Anas platyrhynchos</em>] and northern pintail [<em>Anas acuta</em>]) across six distinct ecostrata in the Prairie Pothole Region of North America (Alberta parkland, Alberta prairie, Saskatchewan parkland, Saskatchewan prairie, Manitoba parkland, US prairie). We tested whether breeding habitat conditions (seasonal pond counts, agricultural intensification, and grassland acreage) or cross-seasonal effects (indexed by flooded rice acreage in primary wintering areas) better explained variation in the proportion of juveniles captured during late summer banding. This submission comprises model code and data of banded birds by species, breeding population survey by species, proportion of ecostratum in conservation tillage (a proxy for agriculutral intensification), proportion of ecostratum in grassland, mean winter precipitation for Pacific Coast and Gulf Coast, total hectares of rice planted in the US, as well as hectares of flooded rice in the Pacific Coast and Gulf Coast. </p>
Data from: Accounting for missing ticks: Use (or lack thereof) of hierarchical models in tick ecology studies
<p>Ixodid (hard) ticks play important ecosystem roles and have significant impacts on animal and human health via tick-borne diseases and physiological stress from parasitism. Tick occurrence, abundance, behavior, and key life-history traits are highly influenced by host availability, weather, microclimate, and landscape features. As such, changes in the environment can have profound impacts on ticks, their hosts, and the spread of diseases. Researchers interested in enumerating questing ticks attempt to integrate this heterogeneity by conducting replicate sampling bouts spread over the tick questing period as common field methods notoriously underestimate ticks. However, it is unclear how (or if) tick studies account for this heterogeneity in the modeling process. This step is critical as unaccounted variance in detection can lead to biased estimates of occurrence and abundance. We performed a descriptive review to evaluate the extent to which studies account for the detection process while modeling tick data. We also categorized the types of analyses that are commonly used to model tick data. We used hierarchical models (HMs) that account for imperfect detection to analyze simulated and empirical tick data, demonstrating that inference is muddled when detection probability is not accounted for in the modeling process. Our review indicates that only 5 of 412 (1%) papers explicitly accounted for imperfect detection while modeling ticks. By comparing HMs with the most common approaches used for modeling tick data (e.g., ANOVA), we show that population estimates are biased low for simulated and empirical data when using non-HMs, and that confounding occurs due to not explicitly modeling factors that influenced both detection and abundance. Our review and analysis of simulated and empirical data shows that it is important to account for our ability to detect ticks using field methods with imperfect detection. Not doing so leads to biased estimates of occurrence and abundance which could complicate our understanding of parasite-host relationships and the spread of tick-borne diseases. We highlight the resources available for learning HM approaches and applying them to analyzing tick data.</p>
Industrial Ecology Data Commons (iedc) December 2024 update
<p>The Industrial Ecology Data Commons (iedc) is a database that contains more than 200 IE-related datasets from the literature, including stocks, flows, process descriptions, IO tables, material composition of products, and many more. Launched in 2018, the iedc is continuously improved and expanded. </p> <p>The homepage of the project is https://www.database.industrialecology.uni-freiburg.de/</p> <p>This Zenodo backup contains a .zip file with 156 parameter templates (xlsx), which where all uploaded to the iedc (SQL database) and are available online.</p> <p>This backup is for archiving the intermediate step between raw data and uploaded data.</p> <p>It contains all data that were gathered up to and including November 2024 except for those data that were uploaded directly via Pyhton scripts from other sources (like .csv) and not via the xlsx templates.</p>
Data from: Pitfalls and pointers: an accessible guide to marker gene amplicon sequencing in ecological applications
<p>Next Generation Sequencing (NGS) is a powerful tool that has been rapidly adopted by many ecologists studying microbial communities. Despite the exciting demonstration of NGS technology as a tool for ecological research, cryptic pitfalls inherent to its use can obscure correct interpretation of NGS data. Here, we provide an accessible overview of a NGS process that uses marker gene amplicon sequences (MGAS) that will allow scientists, particularly community ecologists, to make appropriate methodological choices and understand limits on inference about community composition and diversity that can be drawn from MGAS data.</p> <p>We describe the MGAS pipeline, focusing specifically on cryptic sources of variation that have received less emphasis in the ecological literature, but which may substantially impact inference about microbial community diversity and composition. By simulating communities from published microbiome data, we demonstrate how these sources of variation can generate inaccurate or misleading patterns.</p> <p>We specifically highlight sample dilution without researcher awareness and lane-to-lane variability, two cryptic sources of variation arising during the MGAS pipeline. These sources of variation affect estimates of species presence and relative abundance, particularly for species with moderate to low abundances. Each of these sources of bias can lead to errors in the estimation of both absolute and relative abundance within, and turnover among, microbial communities.</p> <p>Awareness and understanding of what happens and, specifically, why it happens during MGAS generation is key to generating a strong data set and building a robust community matrix. Requesting sample dilution information from the sequencing center, including technical replicates across sequencing lanes, and understanding how sampling intensity and community taxa distribution patterns shape the measurement of community richness, evenness, and diversity are critical for drawing correct ecological inferences using MGAS data.</p>
EcoDes-DK15: High-resolution ecological descriptors of vegetation and terrain derived from Denmark's national airborne laser scanning data set
<p><strong>Eighteen high-resolution ecological descriptors of vegetation and terrain for Denmark "EcoDes-DK15"</strong></p> <p>The data are derived from the nationwide airborne laser scanning / LiDAR campaign of Denmark from 2014-2015 provided by the Danish Agency for Data Supply and Efficiency.</p> <p><strong>Update: EcoDes-DK15 v1.1.0 (4 Dec. 2021)</strong></p> <p>Following the recommendations and feedback during the first round of peer-review, we updated the EcoDes-DK processing pipeline and EcoDes-DK15 data set. The key changes are:</p> <ul> <li>New version of the source data optimised to contain only point data collected before the end of 2015. The source data for EcoDes-DK15 v1.0.0 unintentionally contained data from 2018. The new source data is documented <a href="https://github.com/jakobjassmann/ecodes-dk-lidar/blob/master/documentation/source_data/readme.md">here</a>.</li> <li>New "date_stamp_*" auxiliary variables that illustrate the survey dates for the vegetation points in each cell. See updated descriptor documentation <a href="https://github.com/jakobjassmann/ecodes-dk-lidar/blob/master/documentation/descriptors.md">here</a>.</li> <li>Re-scaling of "solar_radiation" variable to MJ per 100 m<sup>2</sup> per year.</li> </ul> <p><strong>Detailed documentation for the data set can be found in the accompanying manuscript and GitHub repository:</strong></p> <p>Assmann, J. J., Moeslund, J. E., Treier, U. A., and Normand, S.: EcoDes-DK15: High-resolution ecological descriptors of vegetation and terrain derived from Denmark's national airborne laser scanning data set, Earth Syst. Sci. Data Discuss. [preprint], <a href="https://doi.org/10.5194/essd-2021-222">https://doi.org/10.5194/essd-2021-222</a>, in review, 2021<strong><em>.</em></strong></p> <p><a href="https://github.com/jakobjassmann/ecodes-dk-lidar">https://github.com/jakobjassmann/ecodes-dk-lidar</a></p> <p>Files are compressed using bzip2 and tar archiving. The compressed archives can be extracted using commonly available archiving tools (for example <a href="https://www.7-zip.org/">7z </a>on Windows, the archiving tool on macOS and bz2 on Linux). </p> <p>A small example "teaser" subset (5 MB) of the data set, covering the Husby Klit area from Figure 7 in the manuscript, can be found <a href="https://github.com/jakobjassmann/ecodes-dk-lidar/blob/master/manuscript/figure_7/EcoDes-DK15_teaser.zip">here</a>.</p> <p><strong>Abstract (from manuscript)</strong></p> <p>Biodiversity studies could strongly benefit from three-dimensional data on ecosystem structure derived from contemporary remote sensing technologies, such as Light Detection and Ranging (LiDAR). Despite the increasing availability of such data at regional and national scales, the average ecologist has been limited in accessing them due to high requirements on computing power and remote-sensing knowledge. We processed Denmark’s publicly available national Airborne Laser Scanning (ALS) data set acquired in 2014/15 together with the accompanying elevation model to compute 70 rasterized descriptors of interest for ecological studies. With a grain size of 10 m, these data products provide a snapshot of high-resolution measures including vegetation height, structure and density, as well as topographic descriptors including elevation, aspect, slope and wetness across more than forty thousand square kilometres covering almost all of Denmark’s terrestrial surface. The resulting data set is comparatively small (~94 GB, compressed 16.8 GB) and the raster data can be readily integrated into analytical workflows in software familiar to many ecologists (GIS software, R, Python). Source code and documentation for the processing workflow are openly available via a code repository, allowing for transfer to other ALS data sets, as well as modification or re-calculation of future instances of Denmark’s national ALS data set. We hope that our high-resolution ecological vegetation and terrain descriptors (EcoDes-DK15) will serve as an inspiration for the publication of further such data sets covering other countries and regions and that our rasterized data set will provide a baseline of the ecosystem structure for current and future studies of biodiversity, within Denmark and beyond.</p> <p><strong>Acknowledgements (from manuscript)</strong></p> <p>We would like to thank Andràs Zlinszky for his contributions to earlier versions of the data set, Charles Davison for feedback regarding data use and handling, as well as Matthew Barbee and Zsófia Koma for sharing their insights on the source data merger and Zsófia’s script to generate summary statistics for the different versions of the DHM point clouds. Funding for this work was provided by the Carlsberg Foundation (Distinguished Associate Professor Fellowships) and Aarhus University Research Foundation (AUFF-E-2015-FLS-8-73) to Signe Normand (SN). This work is a contribution to SustainScapes – Center for Sustainable Landscapes under Global Change (grant NNF20OC0059595 to SN).</p>
Data for Ecology Letters paper: Jack-of-all-trades paradigm meets long-term data: generalist herbivores are more widespread and locally less abundant.
<p>Data, R-code, and meta-data document for a 2022 paper in Ecology Letters that examines assumptions about associations between local abundance and dietary specialization using an 18-year dataset of caterpillar-plant interactions in Ecuador. </p>
FIGURE 4 in AVONET: morphological, ecological and geographical data for all birds
FIGURE 4 Geographical distribution of morphological data sampling. (a) Location of collections sampled (n = 78 museums or scientific collections in 31 countries), with the number of specimens per collection indicated by bubble size (excluding seven specimens from unknown museums). Sampling of live-caught and released individuals (n = 14,177) is not shown. (b) The number of individual birds sampled from each of 206 administrative units (181 countries), combining museum and field sampling (removing cases not assignable to administrative units). Darker colours indicate a larger number of specimens; specimens lacking precise information on the country of origin (n = 12,775) are not included. (c) The completeness of species sampling in each 100 km grid cell. Colours show the proportion of species present in that cell with specimens sampled from the same country in which the cell is located; warmer colours indicate higher proportions. Species presence was mapped as the portion of the species range occurring within the country, because the specimen is unlikely to have originated from outside the natural range
FIGURE 2 in AVONET: morphological, ecological and geographical data for all birds
FIGURE 2 Diagram of linear measurements of avian morphology presented in AVONET. (a) Resident frugivorous tropical passerine (fiery-capped manakin,Machaeropterus pyrocephalus) showing four beak measurements: (1) beak length measured from tip to skull along the culmen; (2) beak length measured from the tip to the anterior edge of the nares; (3) beak depth; (4) beak width. (b) Insectivorous migratory temperate-zone passerine (redwing, Turdus iliacus) showing five body measurements: (5) tarsus length; (6) wing length from carpal joint to wingtip measured on the unflattened wing; (7) secondary length from carpal joint to tip of the outermost secondary; (8) Kipp's distance, measured directly or calculated as wing length minus first-secondary length; (9) tail length. Protocols for measuring these traits are provided in Supplementary material. AVONET also includes body mass, and Hand-wing index (calculated from 6 to 8), making 11 traits in total. Illustration by Richard Johnson
FIGURE 1 in AVONET: morphological, ecological and geographical data for all birds
FIGURE 1 The sampling of avian morphological traits over time. The number of species (above x axis) and the number of specimens (below x axis) measured for landmark studies along with their year of publication is indicated by the vertical bars. Each bar indicates the maximum number of species and specimens measured for any trait. The number of traits in each study is represented by circle sizes (continuous from 1 to 15, with examples shown in the legend). Studies openly providing raw trait data are indicated in black. AVONET contains the raw specimen-level data for Pigot et al. (2020), along with substantial expansion in coverage of both species and specimens-per-species. To provide historical context, coloured time periods correspond roughly to interest in 'ecomorphology' (blue) and 'functional traits' (red). Citations for studies not used in the main text are provided in the Supplementary Material
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