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Data from: Assessing the impact of character evolution models on phylogenetic and macroevolutionary inferences from fossil data
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Activity-pressure-habitat linkage matrix: Identifying impact chains to assess impact risk to tropical marine ecosystems from human activities in four Southeast Asian case studies Version 1.0
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Use of geolocators for investigating breeding ecology of a rock crevice-nesting seabird: method validation and impact assessment
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Data from: An ecological vulnerability index to assess impacts of offshore wind facilities on migratory song-birds
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Environmental impact assessment for large carnivores: a methodological review of the wolf (Canis lupus) monitoring in Portugal
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Constructing visualization tools and training resources to assess climate impacts on the channel islands national marine sanctuary NetCDF files
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Assessing the impacts of livestock grazing on upland bird breeding territories using drone surveys
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Assessing impacts of social-ecological diversity on resilience in a wetland coupled human and natural system: Data release
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Impact assessment of coastal marine range shifts to support proactive management
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Supplementary material 1 from: Dickey JWE, Cuthbert RN, South J, Britton JR, Caffrey J, Chang X, Crane K, Coughlan NE, Fadaei E, Farnsworth KD, Ismar-Rebitz SMH, Joyce PWS, Julius M, Laverty C, Lucy FE, MacIsaac HJ, McCard M, McGlade CLO, Reid N, Ricciardi A, Wasserman RJ, Weyl OLF, Dick JTA (2020) On the RIP: using Relative Impact Potential to assess the ecological impacts of invasive alien species. NeoBiota 55: 27-60. https://doi.org/10.3897/neobiota.55.49547
Table S1. Outline of different numerical response proxies available, guidance for their use and the advantages and disadvantages of each
Data from: Mortality limits used in wind energy impact assessment underestimate impacts of wind farms on bird populations
<p>In this archive we share the data and R code used for the construction of population models for seven bird species (Common Starling, Black-tailed Godwit<strong>,</strong> Marsh Harrier, Eurasian Spoonbill, White Stork, Common Tern and White-tailed Eagle) for our assessment of the effects of wind farms (Schippers et al. 2020). In most cases we parameterized our population models based on species-specific survival and reproduction rates from scientific articles and reports, but in the case of the Western Marsh Harrier we analyzed previously unpublished nest success and capture-mark-resighting data. Below we first describe per species which data we used for model parameterization, and then describe per data file what each variable represents.</p> <p>We selected populations of seven species based on the availability of data, considerable likelihood to collide with wind turbines and contrasting ages of first reproduction. For species for which long time series of demographic data were available with population trends clearly changing over time, we separately assessed periods with contrasting population trends, as detailed in the species descriptions below. Mean survival and reproduction rates, standard deviations and additional information like the age of first reproduction can be found in the accompanying paper by Schippers et al. (2020). </p> <p> </p> <p><strong>Common Starling</strong></p> <p>On the fast-slow continuum of reproductive capacity, the common starling is the fastest of the seven species we selected: it starts reproducing at an age of one year. We used the mean survival and reproductive rates for the whole Dutch breeding population (Versluijs et al. 2016), distinguishing three separate periods: 1960-1978, 1978-1990 and 1990-2012. In the first period (1960-1978) the population grew at 10% per year. This was followed by a period where the population was relatively stable (1978-1990). During the last period (1990-2012) the population declined strongly.</p> <p> </p> <p><strong>Black tailed Godwit</strong></p> <p>Kentie et al. (2017) studied two Dutch populations of the Black-tailed Godwit in southwestern Fryslân (Skriezekrite and Kuststrook) over four to five annual transitions (Kentie et al. 2017). Godwits started reproducing at age two, but only had 0.5-0.6 fledglings per breeding pair per year. The adults are rather long-lived with an 86% annual survival rate. We construct separate matrix models for the two populations.</p> <p> </p> <p><strong>Marsh Harrier</strong></p> <p>Mean vital rates of the Dutch breeding population of Marsh Harriers were estimated for 1997-2015 using respectively ring recoveries available at the Dutch Centre for Avian Migration and Demography NIOO-KNAW and reproduction data from the Dutch Raptor Working Group. Annual survival of Marsh Harriers was analyzed using live re-sightings and dead recoveries of 12,059 birds ringed as nestling between 1991 and 2016 and 74 birds ringed as ‘adult’ in the same period (due to low sample sizes, birds ringed in their first and second calendar year were lumped with older birds in the ‘adult’ category; see ‘marshHarrierSurvival.csv’ below). Nest success was estimated using data of 1914 nests, which were followed from the beginning to the end of the nest cycle, in the Netherlands between 1997 and 2015 (see ‘marshHarrierReproduction.csv’ below; we thank Rob G. Bijlsma for making the data available). </p> <p> </p> <p><strong>Spoonbill</strong></p> <p>For each year in the 1994-2008 period, age-specific (first-year, second-year, third-year, older) annual survival rates were derived for the Dutch Spoonbill population from van der Jeugd et al. (2014). Participation in the breeding population was 0% in the first three years and went up from 63% at age four to 95% at age 6 and older.</p> <p> </p> <p><strong>White Stork</strong></p> <p>Schaub et al. (2004) analyzed demographic data on White Storks in Switzerland from 1977 till 2000. Here we extracted annual survival and reproduction rates from the COMADRE Animal Matrix Database (version 2.0.1; Salguero-Gómez et al., 2016). Storks start reproducing at age 3, with breeding participation increasing with age from 48% to 100%. </p> <p> </p> <p><strong>Common Tern</strong></p> <p>For the Common Tern we used mean vital rate estimates published by van der Jeugd et al. (2014) for the Dutch Waddenzee population, including the Northern part of the IJsselmeer, between 2000 and 2010 (van der Jeugd et al. 2014). The total Waddenzee and IJsselmeer population is estimated at 7,630 pairs (average population 2010-2014), constituting approximately 40% of the Dutch breeding population of about 20,000 pairs (Sovon 2016). </p> <p> </p> <p><strong>White-tailed Eagle</strong></p> <p>Krüger et al. (2010) published demographic data on White-tailed Eagles in Schleswig-Holstein, Germany, over the period 1947 till 2008. Following these authors, and based on the two matrices in COMADRE v.2.0.1 (Salguero-Gómez et al., 2016), we used separate matrix models for the early period (stable population dynamics) and from 1975 onwards (population growth). These eagles start reproducing at age five. </p> <p> </p> <p>Here we describe the archived files:</p> <p> </p> <p><strong>matrices.R</strong></p> <p>This annotated R file details how the vital rate estimates are used to construct age-structured, post-breeding-census, one-year-timestep population matrix models. In these so-called post-breeding census models the birds in the first class were 0 years old (Caswell 2001).</p> <p> </p> <p><strong>commonstarling19602012.csv</strong></p> <p>Mean survival and reproductive rates for the whole Dutch breeding population of Common Starlings for the time period 1960-2012. </p> <p>year = start year</p> <p>juvSurv = first-year survival of fledgelings</p> <p>adultSurv = annual survival of older birds</p> <p>fec = number of fledgelings per pair (which have a 1:1 sex ratio)</p> <p> </p> <p><strong>blacktailedgodwit20112016.csv</strong></p> <p>Mean survival and reproduction rates of the Black-tailed Godwit in southwestern Fryslân (populations Skriezekrite and Kuststrook) over four to five annual transitions in the period 2011-2016. </p> <p>pop = population</p> <p>startYear = start year</p> <p>adultSurv = annual survival of older birds</p> <p>chickSurv = first-year survival of chicks</p> <p>nestSuc = probability that a nest is successful</p> <p> </p> <p><strong>marshharrier19972015.csv</strong></p> <p>Mean vital rates of the Dutch breeding population of Western Marsh Harriers for 1997-2015.</p> <p>year = start year</p> <p>r = number of fledgelings per pair</p> <p>s1 = first-year survival of fledgelings</p> <p>s2 = annual survival of older birds</p> <p> </p> <p><strong>marshharrierreproduction.csv</strong></p> <p>Western Marsh Harrier nest record data of in the Netherlands.</p> <p>year = year</p> <p>clutchSize = number of eggs</p> <p>young = number of chicks (if known)</p> <p>fledgelings = number of fledgelings</p> <p> </p> <p> </p> <p> </p> <p><strong>marshharriersurvival.csv</strong></p> <p>Ringing and resighting data (using EURING coding) on Western Marsh Harriers in the Netherlands. </p> <p>ringID = ring identifier</p> <p>date = observation date</p> <p>metalRingInformation</p> <p>1 = Metal ring added (where no metal ring was present), position (on tarsus or above) unknown or unrecorded.</p> <p>2 = Metal ring added (where no metal ring was present), definitely on tarsus.</p> <p>3 = Metal ring added (where no metal ring was present), definitely above tarsus.</p> <p>4 = Metal ring is already present.</p> <p>condition </p> <p>0 = Condition completely unknown.</p> <p>1 = Dead but no information on how recently the bird had died (or been killed).</p> <p>2 = Freshly dead – within about a week.</p> <p>3 = Not freshly dead – information available that it had been dead for more than about a week.</p> <p>4 = Found sick, wounded, unhealthy etc. and known to have been released (including ring or other mark identified on a bird in poor condition without the bird having being caught).</p> <p>5 = Found sick, wounded, unhealthy etc. and not released or not known if released.</p> <p>6 = Alive and probably healthy but taken into captivity.</p> <p>7 = Alive and probably healthy and certainly released (including ring or other mark identified on a healthy bird without the bird having being caught).</p> <p>8 = Alive and probably healthy and released by a ringer (including ring or other mark identified on the bird by a ringer without the bird having being caught). </p> <p>ageReported </p> <p>0 = Age unknown, i.e. not recorded.</p> <p>1 = Pullus: nestling or chick, unable to fly freely, still able to be caught by hand.</p> <p>2 = Full-grown: able to fly freely but age otherwise unknown.</p> <p>3 = First-year: full-grown bird hatched in the breeding season of this calendar year.</p> <p>4 = Afer first-year: full-grown bird hatched before this calendar year; year of hatching otherwise unknown.</p> <p>5 = 2<sup>nd</sup> year: a bird hatched last calendar year and now in its second calendar year.</p> <p>6 = Afer 2<sup>nd</sup> year: full-grown bird hatched before last calendar year; year of hatching otherwise unknown.</p> <p>7 = 3<sup>rd</sup> year: a bird hatched two calendar years before, and now in its third calendar year.</p> <p>8 = Afer 3<sup>rd</sup> year: a full-grown bird hatched more than three calendar years ago (including present year as one); year if bird otherwise unknown.</p> <p>9 = 4<sup>th</sup> year: a bird hatched three calendar years before, and now in its fourth calendar year.</p> <p>A = Afer 4<sup>th</sup> year: a bird older than category 9 – age otherwise unknown.</p> <p>sexReported</p> <p>U = Unknown</p> <p>M = Male</p> <p>F = Female</p> <p> </p> <p><strong>eurasianspoonbill19942008.csv</strong></p> <p>For each year in the 1994-2008 period, age-specific (first-year, second-year, third-year, older) annual survival rates are given for the Dutch Spoonbill population.</p> <p>year = start year</p> <p>fled = number of fledgelings per breeding pair</p> <p>s1 = first-year survival rate</p> <p>s2 = second-year survival rate</p> <p>s3 = third-year survival rate</p> <p>s4 = older birds' annual survival rate</p> <p> </p> <p><strong>whitestork19772000.csv</strong></p> <p>Demographic data on White Storks in Switzerland from 1977 till 2000.</p> <p>year = start year</p> <p>fled = number of fledgelings per pair</p> <p>sj = first-year survival of fledgelings</p> <p>sa = annual survival of older birds</p> <p> </p> <p><strong>commontern19942009.csv</strong></p> <p>Mean vital rate estimates for the Common Tern for the Dutch Waddenzee population, including the Northern part of the IJsselmeer, between 2000 and 2010.</p> <p>year = start year</p> <p>r = number of daughter fledgelings per adult female</p> <p>s1 = first-year survival rate</p> <p>s2 = second-year survival rate</p> <p>sA = older birds' annual survival rate</p> <p> </p> <p><strong>whitetailedeaglepmat1.csv</strong></p> <p><strong>whitetailedeaglepmat2.csv</strong></p> <p><strong>whitetailedeaglefmat1.csv</strong></p> <p><strong>whitetailedeaglefmat2.csv</strong></p> <p>White-Tailed Eagle age-specific survival (Pmat) and reproduction (Fmat) matrices as found in COMADRE v.2.0.1, for Schleswig-Holstein, Germany, studied over the period 1947-2008. Period 1 lasts upto 1975, period 2 from 1975. </p>
Combining seascape connectivity with cumulative impact assessment in support of ecosystem-based marine spatial planning
<p>1. Cumulative impact assessment (CIA) is a promising approach to guide marine spatial planning (MSP) and management. One limitation of CIA is the neglect of seascape connectivity, which may spread the impact of localised pressures to ambient areas, e.g. through lost dispersal and recruitment of organisms.</p> <p>2. We here, for the first time, incorporate seascape connectivity into a traditional CIA model using a connectivity matrix, exemplified by dispersal of propagules estimated through biophysical modelling. Two <i>connectivity impacts</i>are identified: the <i>source impact</i> represents downstream areas losing recruits because of reduced larval dispersal from sites affected by the pressure, and the <i>sink impact</i> represents loss of recruits originating from upstream areas prevented from settlement in the site affected by the local pressure.</p> <p>3. By including seascape connectivity into the Swedish MSP-guiding CIA tool Symphony we demonstrate how to practically account for remote effects of local environmental impact. Our example on blue mussel shows how reducing mussel fitness in a given area may have impacts on mussels far from the acting pressures. Overall, results indicate that connectivity impact for blue mussels plays a minor role in most areas, less than 10% of the ordinary cumulative impact. However, in some smaller areas, e.g. on offshore banks and the Danish Straits, seascape connectivity may increase ordinary cumulative impact with 20-30%. In an example of scenario-based CIA analyses of MSP projections, we demonstrate how impacts of particular management actions, e.g. shipping rerouting and wind power developments, can be tracked far from the original area of influence.</p> <p>4. Depending on the dispersal ability of ecosystem components, a local pressure may impact a considerable area through seascape connectivity, transgressing management units and national borders. Although the mean connectivity impact may be modest for a single ecosystem component, the consideration of seascape connectivity across multiple ecosystem components may significantly alter the mapping of cumulative impact and the assessment of different MSP scenarios.</p> <p>5. Synthesis and applications.<span><span><span><span><span><span><span><span><span><span><span> Our extension of Cumulative Impact Assessment offers a new method for mapping and practically integrating seascape connectivity with ecosystem-based MSP and other spatial instruments for policy making, such as marine protected areas.</span></span></span></span></span></span></span></span></span></span></span></p>
Assessing seasonal demographic covariation to understand environmental-change impacts on a hibernating mammal
<p>Natural populations are exposed to seasonal variation in environmental factors that simultaneously affect several demographic rates (survival, development, reproduction). The resulting covariation in these rates determines population dynamics, but accounting for its numerous biotic and abiotic drivers is a significant challenge. Here, we use a factor-analytic approach to capture partially unobserved drivers of seasonal population dynamics. We use 40 years of individual-based demography from yellow-bellied marmots (Marmota flaviventer) to fit and project population models that account for seasonal demographic covariation using a latent variable. We show that this latent variable, by producing positive covariation among winter demographic rates, depicts a measure of environmental quality. Simultaneous, negative responses of winter survival and reproductive-status change to declining environmental quality result in a higher risk of population quasi-extinction, regardless of summer demography where recruitment takes place. We demonstrate how complex environmental processes can be summarized to understand population persistence in seasonal environments.</p>
Assessing the impact of static and fluctuating ocean acidification on the behavior of Amphiprion percula
<p>Attached is the complete raw data from Vaughan and Dixson 2021 ‘Assessing the impact of static and fluctuating ocean acidification on the behavior of <em>Amphiprion percula</em>’. </p> <p>Data collected from the behavioral lateralization trials has been inputted into the file ‘Vaughan_2020_Lateralization_Raw’. Column A indicate the CO<sub>2</sub> treatment group, where “SPD” = Static Present Day, “SFD” = Static Future Day, “FPD” = Fluctuating Present Day, and “FFD” = Fluctuating Future Day. Each individual fish used from each treatment group (n=30) is displayed in Column B. Column C shows the binary results, in order, of each fish’s turns in the T-maze, and was scored as 0 (right turn) or 1 (left turn) for a total of 10 turns. The total number of turns to the right and left are provided in Column D-E. The relative lateralization (<em>L</em><sub>R</sub>) of each fish was calculated {L<sub>R</sub> = [(Turn to the right – Turn to the left)/(Turn to the right + Turn to the left)] ∗ 100} in Column F. Absolute lateralization (<em>L</em><sub>A</sub>) is provided in Column G.</p> <p>Chemosensory response data has been inputted into the file ‘Vaughan_2020_Chemosensory_Raw’. Column A and B display treatment group and fish ID (n=20) as outlined above. The cue used in trial of either Tang (nonpredator) or Cod (predator) is provided in Column C, and the control in Column D. Numbers in these are used solely for the purpose of data analysis. The side of the cue in the flume is provided in Column E, and corresponds with the cue labelled in Column C. Buckets containing either the cue or control were placed above the flume and color coded as “BS” (blue side) and “RS” (red side), as the person scoring the trials was blinded. This also helped account for the switch (from one side of the flume to the other) that occurs halfway through each trial. Columns F-G represent results from the first 2min recording period, and Columns H-I represent results from the second 2min recording period. The total tallies from each fish are provided in Column J; the totals from each side are calculated in Columns K-L, and then sorted by either cue or control in Columns M-N. Proportions and percentages in cue and control are calculated and provided in Columns O-P and Q-R, respectively. </p> <p>Carbonate chemistry data is compiled and provided in the ‘Vaughan_2020_Carbonate_Chemistry’. Measurements were taken each week (Column A) of each treatment group (as stated above, Column B). Column C reflects the time recordings were taken in the fluctuating treatments to hit the high, mid and low CO<sub>2</sub> points at “6:30”, “12:30” and “18:30”. Measurements of static treatment groups were taken at randomly selected times to get the reflection of the carbonate chemistry of these treatments, but for the purpose of clarity in this document they are listed as “Static”. Measurements were taken from a subset of tanks each that rotated each week (Column D). Our target pH<sub>NBS</sub> values (i.e. what was programmed into the APEX System) are listed in Column E. Columns F-H displayed pH<sub>NBS</sub> (taken with APEX probes), temperature °C (taken with a portable Mettler Toledo probe) and salinity (taken with a refractometer). Water samples were analyzed spectrophotometrically to provide pH<sub>T</sub> and dissolved inorganic carbon, with values provided in Columns I-J. Using the program CO2SYS, total alkalinity and <em>p</em>CO<sub>2</sub> were calculated, with values provided in Columns K-L.</p>
Is your OCR good enough? A comprehensive assessment of the impact of OCR quality on downstream tasks
<p>Is an average OCR quality of 70% enough for my study? What OCR quality should we ask from external suppliers? Should we re-do the OCR of our collections to bring it from 80% to 85%? Libraries and researchers alike face the same dilemma in our times of textual abundance: when is OCR quality good enough? User access, scientific results and the investment of limited resources increasingly depend on answering this question.</p> <p>This project focuses on a comprehensive assessment of the impact of OCR quality in Dutch newspaper, journal and book collections, comparing it with published results for English and French. This is be done via <em>extrinsic evaluation</em>: assessing results from a set of representative downstream tasks, such as text classification or clustering. The ultimate goal of the project is to contribute guidelines detailing when OCR quality is to be considered good enough, in order to inform the development and use of textual collections.</p> <p>The datasets released here are described in <a href="https://github.com/Giovanni1085/KB_OCR_impact/wiki/Datasets">this Wiki page</a>. Please refer to the <a href="https://github.com/Giovanni1085/KB_OCR_impact">project's repository</a> for more information.</p>
Assessing the Ecological Impacts of Biomass Harvesting along a Disturbance Severity Gradient
<p>Disturbance is a central driver of forest development and ecosystem processes with variable effects within and across ecosystems. Despite the high levels of variation in disturbance severity often observed in forests following natural and anthropogenic disturbance, studies quantifying disturbance impacts often rely on categorical classifications, thus limiting opportunities to examine potential gradients in ecosystem response to a given disturbance or management regime. Given the potential increases in disturbance severity associated with global change, as well as shifts in management regimes related to procurement of biofuel feedstocks, there is an increasing need to quantitatively describe disturbance severity and associated responses of forest development, soil processes, and structural conditions. This study took advantage of two replicated large-scale studies of forest biomass harvesting in <i>Populus tremuloides</i> and <i>Pinus bansksiana </i>forests, respectively, to develop and test the utility of a continuous, quantitative disturbance severity index (DSI) for describing post-harvest response of plant communities and nutrient pools to different levels of biomass removal and legacy retention (i.e., live trees and coarse woody material). There was a high-degree of variability in DSI within categorical treatments associated with different levels of legacy retention and regression models using DSI as a predictor explained a portion of the variation (>50%) for many of the ecosystem- and community-level responses to biomass harvesting examined. Nutrient losses associated with biomass harvesting were positively related to disturbance severity, particularly in <i>P. tremuloides</i> forests, with post-harvest nutrient availability generally declining along the gradient of impacts. Consistent with expectations from ecological theory, species richness and diversity of woody plant communities were greatest at intermediate disturbance severities and regeneration densities of dominant trees species were most abundant at highest levels of disturbance. Although categorical benchmarks will continue to be the primary way through which management guidelines are conveyed to practitioners, evaluation of their effectiveness at sustaining ecosystem functioning should be through continuous analyses, such as the DSI approach used in this study, to allow for the more precise identification of thresholds that ensure a range of desirable outcomes exist across managed landscapes.</p>
Data from: Scanning SNPs from a large set of expressed genes to assess the impact of artificial selection on the undomesticated genetic diversity of white spruce
A scan involving 1134 single-nucleotide polymorphisms (SNPs) from 709 expressed genes was used to assess the potential impact of artificial selection for height growth on the genetic diversity of white spruce. Two case populations of different sizes simulating different family selection intensities (K = 13% and 5%, respectively) were delineated from the Quebec breeding program. Their genetic diversity and allele frequencies were compared with those of control populations of the same size and geographic origin to assess the effect of increasing the selection intensity. The two control populations were also compared to assess the effect of reducing the sampling size. On one hand, in all pairwise comparisons, genetic diversity parameters were comparable and no alleles were lost in the case populations compared with the control ones, except for few rare alleles in the large case population. Also, the distribution of allele frequencies did not change significantly (P ≤ 0.05) between the populations compared, but ten and nine SNPs (0.8%) exhibited significant differences in frequency (P ≤ 0.01) between case and control populations of large and small sizes, respectively. Results of association tests between breeding values for height at 15 years of age and these SNPs supported the hypothesis of a potential effect of selection on the genes harboring these SNPs. On the other hand, contrary to expectations, there was no evidence that selection induced an increase in linkage disequilibrium in genes potentially affected by selection. These results indicate that neither the reduction in the sampling size nor the increase in selection intensity was sufficient to induce a significant change in the genetic diversity of the selected populations. Apparently, no loci were under strong selection pressure, confirming that the genetic control of height growth in white spruce involves many genes with small effects. Hence, selection for height growth at the present intensities did not appear to compromise background genetic diversity but, as predicted by theory, effects were detected at a few gene SNPs harboring intermediate allele frequencies.
Data from: Assessing the impacts of positive selection on coalescent-based species tree estimation and species delimitation.
The assumption of strictly neutral evolution is fundamental to the multispecies coalescent model and permits the derivation of gene tree distributions and coalescent times conditioned on a given species tree. In this study, we conduct computer simulations to explore the effects of violating this assumption in the form of species-specific positive selection when estimating species trees, species delimitations, and coalescent parameters under the model. We simulated datasets under an array of evolutionary scenarios that differ in both speciation parameters (i.e., divergence times, strength of selection) and experimental design (i.e., number of loci sampled) and incorporated species-specific positive selection occurring within branches of a species tree to identify the effects of selection on multispecies coalescent inferences. Our results highlight particular evolutionary scenarios and parameter combinations in which inferences may be more, or less, susceptible to the effects of positive selection. In some extreme cases, selection can decrease error in species delimitation and increase error in species tree estimation, yet these inferences appear to be largely robust to the effects of positive selection under many conditions likely to be encountered in empirical datasets.
Data from: Assessing cumulative impacts of forest development on the distribution of furbearers using expert-based habitat modeling
Cumulative impacts of anthropogenic landscape change must be considered when managing and conserving wildlife habitat. Across the central-interior of British Columbia, Canada, industrial activities are altering the habitat of furbearer species. This region has witnessed unprecedented levels of anthropogenic landscape change following rapid development in a number of resource sectors, particularly forestry. Our objective was to create expert-based habitat models for three furbearer species: fisher (Pekania pennanti), Canada lynx (Lynx canadensis), and American marten (Martes americana) and quantify habitat change for those species. We recruited 10 biologist and 10 trapper experts and then used the analytical hierarchy process to elicit expert knowledge of habitat variables important to each species. We applied the models to reference landscapes (i.e., registered traplines) in two distinct study areas and then quantified the change in habitat availability from 1990 to 2013. There was strong agreement between expert groups in the choice of habitat variables and associated scores. Where anthropogenic impacts had increased considerably over the study period, the habitat models showed substantial declines in habitat availability for each focal species (78% decline in optimal fisher habitat, 83% decline in optimal lynx habitat, and 79% decline in optimal marten habitat). For those traplines with relatively little forest harvesting, the habitat models showed no substantial change in the availability of habitat over time. The results suggest that habitat for these three furbearer species declined significantly as a result of the cumulative impacts of forest harvesting. Results of this study illustrate the utility of expert knowledge for understanding large-scale patterns of habitat change over long time periods.
Data from: Disease, predation and demography: assessing the impacts of bovine tuberculosis on African buffalo by monitoring at individual and population levels
1. Understanding the effects of disease is critical to determining appropriate management responses, but estimating those effects in wildlife species is challenging. We used bovine tuberculosis (BTB) in the African buffalo Syncerus caffer population of Kruger National Park, South Africa, as a case study to highlight the issues associated with estimating chronic disease effects in a long-lived host. 2. We used known and radiocollared buffalo, aerial census data, and a natural gradient in pathogen prevalence to investigate if: (i) at the individual level, BTB infection reduces reproduction; (ii) BTB infection increases vulnerability to predation; and (iii) at the population level, increased BTB prevalence causes reduced population growth. 3. There was only a marginal reduction in calving success associated with BTB infection, as indexed by the probability of sighting a known adult female with or without a calf ( P = 0·065). 4. Since 1991, BTB prevalence increased from 27 to 45% in the southern region and from 4 to 28% in the central region of Kruger National Park. The prevalence in the northern regions was only 1·5% in 1998. Buffalo population growth rates, however, were neither statistically different among regions nor declining over time. 5. Lions Panthera leo did not appear to preferentially kill test-positive buffalo. The best (Akaike's Information Criterion corrected for small sample size) AIC c model with BTB as a covariate [exp( β ) = 0·49; 95% CI = (0·24–1·02)] suggested that the mortality hazard for positive individuals was no greater than for test-negative individuals. 6. Synthesis and applications . Test accuracy, time-varying disease status, and movement among populations are some of the issues that make the detection of chronic disease impacts challenging. For these reasons, the demographic impacts of bovine tuberculosis in the Kruger National Park remain undetectable despite 6 years of study on known individuals and 40 years of population counts. However, the rainfall and forage conditions during this study were relatively good and the impacts of many chronic diseases may be a non-linear function of environmental conditions such that
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.