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16 results for “occurrence probability”
Predicted occurrence probability for ticks in Great Britain (2014 to 2021) at 1 km spatial resolution
<p>The dataset contains predictions of occurrence probability for ticks in Great Britain (2014 to 2021) at 1 km spatial resolution + all covariate layers used for modeling. Over seven million electronic health records (EHRs), among which 11,741 EHRs reported tick attachment, were used to evaluate climate, environmental and animal host factors affecting the risk of tick attachment in cats and dogs in Great Britain (GB). The tick presence/absence EHRs for dogs and cats were further overlaid with spatiotemporal time-series of climatic, vegetation, human influence, hydrological and terrain variables (slope, wetness index) to produce a spatiotemporal regression matrix; an Ensemble Machine Learning framework was used to fine-tune hyperparameters for Random Forest (classif.ranger), Gradient boosting (classif.xgboost) and GLM-net (classif.glmnet) algorithms, which were then used to produce a final ensemble meta-learner that predicts the probability of occurrence of ticks across GB with monthly intervals.</p> <ul> <li>gb1km_covariates.zip contains ALL covariate layers as GeoTIFFs (time-series) used for modeling ticks dynamics;</li> <li>data_1km_2014_M01.rds = contains all covariates for January 2014 prepared as SpatialGridDataFrame (R data object);</li> </ul> <p>Codes of files indicate e.g.:</p> <ul> <li>"monthly.tick.prob_savsnet.mar_p_1km_s_2014_2021" = monthly occurrence probability for January based on the training data from 2014 to 2021;</li> <li>"monthly.tick.prob_savsnet.oct_md_1km_s_20211001_20211031" = monthly prediction (model) error derived as the standard deviation from multiple base learners;</li> </ul> <p>The dataset is described in detail in the following publication:</p> <ul> <li>Arsevska, E., Hengl, T., Singelton, D. et al. (2023?) <strong>Risk factors for tick attachment in companion animals in Great Britain: a spatiotemporal analysis covering 2014–2021</strong>. Submitted to Parasites & Vectors (in review).</li> </ul> <p>The model summary shows:</p> <pre><code>Call: stats::glm(formula = f, family = "binomial", data = getTaskData(.task, .subset), weights = .weights, model = FALSE) Deviance Residuals: Min 1Q Median 3Q Max -1.4749 -0.0557 -0.0471 -0.0430 3.7611 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -7.64495 0.02095 -364.957 < 2e-16 *** classif.ranger 4.95061 0.63615 7.782 7.13e-15 *** classif.xgboost 189.75543 5.53109 34.307 < 2e-16 *** classif.glmnet 140.24208 5.05375 27.750 < 2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 (Dispersion parameter for binomial family taken to be 1) Null deviance: 170604 on 7303013 degrees of freedom Residual deviance: 162571 on 7303010 degrees of freedom AIC: 162579 Number of Fisher Scoring iterations: 9</code></pre> <p><em>Acknowledgements</em>: We are grateful to data providers in veterinary practice (VetSolutions, Teleos, CVS, and other practitioners). We are grateful to the INRAE MIGALE bioinformatics facility (MIGALE, INRAE, 2020. Migale Bioinformatics Facility, doi: <a href="https://entrepot.recherche.data.gouv.fr/dataverse/migale">10.15454/1.5572390655343293E12</a>) for providing computing resources. We are also grateful for<br> the help and support provided by <a href="https://www.liverpool.ac.uk/savsnet/">SAVSNET team members</a> Bethaney Brant, Susan Bolan and Steven Smyth.<br> This study was funded mainly by a grant from the <strong>Biotechnology and Biological Sciences Research Council</strong>,<br> BB/NO19547/1 and <strong>British Small Animal Veterinary Association</strong> (BSAVA). The research was partly funded by the National Institute for <strong>Health Research Health Protection Research Unit</strong> (NIHR HPRU) in Emerging and Zoonotic Infections at the <strong>University of Liverpool</strong> in partnership with <strong>Public Health England</strong> (PHE) and <strong>Liverpool School of Tropical Medicine</strong> (LSTM). This work has been partially funded by the <em>“Monitoring outbreak events for disease surveillance in a data science context"</em> (MOOD) project from the European Union’s Horizon 2020 research and innovation program under grant agreement No. 874850 (<a href="https://mood-h2020.eu/">https://mood-h2020.eu/</a>). The views expressed are those of the authors and not necessarily those of the NHS, the NIHR, the Department of Health or Public Health England.</p>
Prevalent trends in realized probability of occurrence of main European forest tree species for 2000–2020
<p>High resolution maps resulting from a trend analysis conducted for the period 2000–2020 on the probability of occurrence maps prepared by <a href="https://doi.org/10.7717/peerj.13728">Bonannella et al. (2022)</a>. For this analysis we selected the realized distribution time series layers at 30m spatial resolution for 6 out of 16 species described in the mentioned publication:</p> <ul> <li>Silver fir (<em>Abies alba </em>Mill.)</li> <li>European beech (<em>Fagus sylvatica </em>L.)</li> <li>Norway spruce (<em>Picea abies </em>L.)</li> <li>Black pine (<em>Pinus nigra </em>J. F. Arnold)</li> <li>Scots pine (<em>Pinus sylvestris </em>L.)</li> <li>Common oak (<em>Quercus robur </em>L.)</li> </ul> <p>The trend analysis was conducted per pixel on each of these species individually. We fitted simple OLS regression models with the probability of occurrence as the dependent variable and time as the independent variable. After the model fitting, we also calculated the t-test statistics to determine the presence of an increasing (positive) or decreasing (negative) trend or no trend at all.</p> <p>By combining the regression slope coefficient (<em>β</em>) and the <em>p</em>-value from the t-test statistics we assigned each pixel to one of three classes:</p> <ul> <li><em>positive</em>: <em>β</em> > 0.25 AND <em>p</em>-value < 0.05</li> <li><em>negative</em>: <em>β</em> < −0.25 AND <em>p</em>-value < 0.05</li> <li><em>no trend / stable</em>: −0.25 ≤ <em>β</em> ≥ 0.25 OR <em>p</em>-value > 0.05</li> </ul> <p>We then aggregated the resulting classes at 1km resolution maps to capture the prevalent trend in probability of occurrence over a certain area. Files are named according to the following naming convention, e.g.:</p> <ul> <li>veg_abies.alba_slope_30m_0..0cm_epsg3035_v1.0</li> </ul> <p>with the following fields:</p> <ul> <li>theme: e.g. <strong>veg</strong>,</li> <li>species code: e.g. <strong>abies.alba</strong>,</li> <li>variable name: e.g. <strong>slope</strong>,</li> <li>resolution in meters e.g. <strong>30m</strong>,</li> <li>reference depths (vertical dimension): e.g. <strong>0..0cm</strong>,</li> <li>coordinate system: e.g. <strong>epsg3035</strong>,</li> <li>data set version: e.g. <strong>v1.0</strong>.</li> </ul> <p>For each species here we provide the following layers:</p> <ul> <li>veg_abies.alba_<strong>slope</strong>:<strong> </strong>slope coefficient (scaling factor: 10000)</li> <li>veg_abies.alba_<strong>pvalue</strong>:<strong> </strong><em>p</em>-value (scaling factor: 1000)</li> <li>veg_abies.alba_<strong>pos.trends_30m</strong>: pixels classified as <em>positive </em>on the original maps at 30m resolution (boolean layer with range 0–100, only the two extremes values are present)</li> <li>veg_abies.alba_<strong>pos.trends_1km</strong>: proportion of pixels of the <em>positive </em>class over a 1×1 km area (range 0–100)</li> <li>veg_abies.alba_<strong>neg.trends_30m</strong>: pixels classified as <em>negative </em>on the original maps at 30m resolution (boolean layer with range 0–100, only the two extremes values are present)</li> <li>veg_abies.alba_<strong>neg.trends_1km</strong>: proportion of pixels of the <em>negative </em>class over a 1×1 km area (range 0–100)</li> <li>veg_abies.alba_<strong>no.trends_30m</strong>: (pixels classified as <em>no trend / stable </em>on the original maps at 30m resolution (boolean layer with range 0–100, only the two extremes values are present)</li> <li>veg_abies.alba_<strong>no.trends_1km</strong>:<strong> </strong>proportion of pixels of the <em>no trend / stable </em>class over a 1×1 km area (range 0–100)</li> </ul> <p>Files are provided as GeoTIFFs and projected in the Coordinate Reference System ETRS89 / LAEA Europe (= EPSG code 3035). Styling files are provided in <em>QML</em> format</p> <p>A publication describing, in detail, all processing steps is currently in review. See at:<br> <br> Bonannella, C., Parente, L., de Bruin, S. and Herold, M. (2023). Multi-decadal trend analysis and forest disturbance assessment of European tree species: concerning signs of a subtle shift, PREPRINT (Version 1) available at Research Square [<a href="https://doi.org/10.21203/rs.3.rs-3288937/v1">https://doi.org/10.21203/rs.3.rs-3288937/v1</a>]</p> <p> </p>
Fig. 4 in Ephemeral Occurrence of the Echiuran Listriolobus brevirostris (Annelida: Echiura) in Osaka Bay between 1995 and 2002; a New Record for Japan, Probably Resulting from Human-mediated Introduction
Fig. 4. Relationship between trunk length and proboscis length in 52 fixed specimens of Listriolobus brevirostris, collected from Osaka Bay in 1999 (NSMT-Ec 164 to 183).
Fig. 3 in Ephemeral Occurrence of the Echiuran Listriolobus brevirostris (Annelida: Echiura) in Osaka Bay between 1995 and 2002; a New Record for Japan, Probably Resulting from Human-mediated Introduction
Fig. 3. Internal morphology (dorsal view) of Listriolobus brevirostris from Osaka Bay, collected off Kobe on 26 November 2001 (NSMT-Ec 186). A, anterior end of trunk; B, posterior end of trunk (ventral vessel undetectable due to deterioration). Abbreviations: al, alimentary canal; av, anal vesicle; dv, dorsal vessel; gd, gonoduct; gl, gonostomal lip; im, interbasal muscle; nv, neurointestinal vessel; rc, rectal caecum; re, rectum; rv, ring vessel; tb, terminal bulb; vn, ventral nerve cord: vs, ventral seta; vv, ventral vessel. Scale bars: 1 mm.
Fig. 5 in Ephemeral Occurrence of the Echiuran Listriolobus brevirostris (Annelida: Echiura) in Osaka Bay between 1995 and 2002; a New Record for Japan, Probably Resulting from Human-mediated Introduction
Fig. 5. Seasonal changes in trunk length of Listriolobus brevirostris from Osaka Bay in 1999. Solid bars indicate mature specimen(s), dotted lines, measurements from live specimens, and double-headed arrow of dotted line, the length range in live specimens (see text).
Vertebrate-habitat relationships: Logistic regression models predict probability of occurrence of bird and small mammal species in western Oregon
Logistic regression models predicting probability of occurrence of bird and of small-mammal species were produced using animal-habitat data sets from throughout western Oregon (Garman and Cole 1999 - Vertebrate Habitat Relationships Data Bank (VHRDB), Report to Coastal Landscape Analysis and Modeling Study). Regression coefficients, variables, and metrics related to model predictions are provided here under Entity 1, and in VHRDB as VERTLOGR.
L4D - Probability map of giant trees occurrence (> 70 m) in the Brazilian Amazon
<p>The probability of giant trees occurrence (> 70m) based on environmental conditions. The observations higher than 70 m were filtered out and used to adjust an envelope model based on maximum entropy. In its optimization routine, the algorithm tracked how much the model gain was improved when small changes were made to each coefficient value associated with a particular variable. The resulting map of predicted occurrence of the tallest trees in the Amazon from the MaxEnt model shows that the probability of maximum tree height occurrence is highest in the northeastern Amazon (Fig. 6), more specifically in the Roraima and Guianan Lowlands. We considered 18 environmental variables: (1) fraction of absorbed photosynthetically active radiation (FAPAR; in %); (2) elevation above sea level (Elevation; in m); (3) the component of the horizontal wind towards east, i.e. zonal velocity (u-speed ; in m s<sup>-1</sup>); (4) the component of the horizontal wind towards north, i.e. meridional velocity (v-speed ; in m s<sup>-1</sup>); (5) the number of days not affected by cloud cover (clear days; in days yr<sup>-1</sup>); (6) the number of days with precipitation above 20 mm (days > 20mm; in days yr<sup>-1</sup> ); (7) the number of months with precipitation below 100 mm (months < 100mm; in months yr<sup>-1</sup> ) ; (8) lightning frequency (flashes rate); (9) annual precipitation (in mm); (10) potential evapotranspiration (in mm); (11) coefficient of variation of precipitation (precipitation seasonality; in %); (12) amount of precipitation on the wettest month (precip. wettest; in mm); (13) amount of precipitation on the driest month (precip. driest; in mm); (14) mean annual temperature (in °C); (15) standard deviation of temperature (temp. seasonality; in °C); (16) annual maximum temperature (in °C); (17) soil clay content (in %); and (18) soil water content (in %). </p>
Probability of occurrence and phenology of pine wilt disease transmission by insect vectors in the Rocky Mountains
<p>1. Pine wilt disease, caused by pinewood nematode (Bursaphelenchus xylophilus; PWN), is a damaging and globally distributed insect-vectored forest pathogen. Native forest tree mortality associated with PWN is newly reported from the Front Range of Colorado, but there is no regional information on PWN frequency or biology of local insect vectors, limiting management options.</p> <p>2. A sampling array was established to survey PWN in native pines (Pinus ponderosa) and longhorn beetles (Monochamus clamator & Monochamus scutellatus) over two years and across natural and urban forest landscapes. We developed flight phenology models and evaluated effects of landscape factors on vector abundance and probability of infection.</p> <p>3. Flight phenology was similar for vectors; Monochamus flight initiated in mid-July and continued into October for both species. We report the first M. clamator–PWN association in the United States. PWN was distributed in the region at rates lower than reported from its putative native range: 3.6 and 4.2% of sampled pines and beetles, respectively, tested positive for PWN. Many host trees were outwardly asymptomatic; infection frequency in tree populations varied considerably and four epicenters of vector infectivity were identified.</p> <p>4. Epicenters varied in timing of anomalous infective vector frequency—some epicenters had high abundances of infected beetles early in the growing season whereas others had high abundances of infected beetles late in the growing season, though PWN-positive beetles were captured at all sites. Monochamus populations were found primarily in natural forest stands but migrated to urban areas late in the growing season. The only landscape factor positively correlated with abundances of both Monochamus species was distance to previous wildfire.</p> <p>5. Synthesis and applications: PWN epicenters in the southern Rocky Mountains exhibit specific temporal windows of vector activity that differ from proximal sites. Urban forests, where the disease was initially observed in the region, do not support vector populations. Our results suggest that natural forest landscapes in the region are important reservoirs of PWN and vector populations are especially abundant near burned stands. Collectively, our findings are important for timing disease management activities appropriately and help to distinguish priority areas for mitigation efforts.</p>
Database for Stand competition and slope increase the probability of occurrence of Gremmeniella abietina in forest stands in Spain
<p>Database</p>
Probability of occurrence and phenology of pine wilt disease transmission by insect vectors in the Rocky Mountains
Open the record for dataset details and reuse information.
Data from: Modeling and mapping the probability of occurrence of invasive wild pigs across the contiguous United States
Open the record for dataset details and reuse information.
Fig. 1 in Ephemeral Occurrence of the Echiuran Listriolobus brevirostris (Annelida: Echiura) in Osaka Bay between 1995 and 2002; a New Record for Japan, Probably Resulting from Human-mediated Introduction
Fig. 1. Photograph of a living individual of Listriolobus brevirostris, dredged on 18 August 1999 in the northern end of Osaka Bay, Japan (NSMT-Ec 165). Scale bar: 1 cm.
Fig. 6 in Ephemeral Occurrence of the Echiuran Listriolobus brevirostris (Annelida: Echiura) in Osaka Bay between 1995 and 2002; a New Record for Japan, Probably Resulting from Human-mediated Introduction
Fig. 6. Annual fluctuations in minimal dissolved oxygen content (mg/L) of the bottom water layer in the most oxygen-depleted (eastern) portion of Osaka Bay. Solid line indicates data from area formerly inhabited by Listriolobus brevirostris (northeastern Osaka Bay); dashed line indicates data from area devoid of L. brevirostris (remaining eastern portion of Osaka Bay).
Fig. 2 in Ephemeral Occurrence of the Echiuran Listriolobus brevirostris (Annelida: Echiura) in Osaka Bay between 1995 and 2002; a New Record for Japan, Probably Resulting from Human-mediated Introduction
Fig. 2. Map showing the only known localities of Listriolobus brevirostris, each with earliest recorded year(s) of occurrence (see text).
Habituation to abrupt onset distractors with different spatial occurrence probability
<p>Dataset and experimental scripts related to the publication "Habituation to abrupt onset distractors with different spatial occurrence probability", by Matteo Valsecchi and Massimo Turatto, published in Attention, Perception & Psychophysics.</p> <p>The readme file inside the folder describes the contents.</p>
Subspecies and Distribution. H.a.aquaticusOgilby,1841—WAfricafromGuineaandSierraLeonetoGhana. H.a.bates:Lydekker,1906—Nigeria,Cameroon,andpresumablyneighboringcountries. H. a. cottoni Lydekker, 1906 — Republic of the Congo, DR Congo, and presumably Uganda. The Water Chevrotain reportedly has a disjunct distribution, occurring in coastal forests from West Africa and in the rainforests of Central Africa from Nigeria to DR Congo, marginally entering Uganda. It has been listed for the following countries in Central Africa: Angola (Cabinda), Cameroon, Central African Republic, DR Congo, Equatorial Guinea, Gabon, Nigeria, Republic of the Congo, and Uganda (Semliki Valley). A record from Angola's Lunda Norte Province, near the Cassai River,is the southernmost record of the species. The species' status in some countries remains unclear. It is apparently absent from the Republic of Benin and Togo (but the speciesis listed as probable in the Ot Basin in Togo); its supposed occurrence in Guinea Bissau and Senegal remains unsupported by evidence. The species was listed for Sierra Leone, although its presence had been called into question. Photographic evidence seems to clarify that the species occurs in Sierra Leone. In 1850, a specimen was recorded from Gambia, but the present status of the species is unclear. Local people report the species from the Boké Préfecture in NW Guinea, which might be the northernmost area from which the species has been recently reported. Extensive field and market surveys there and in the southern Guinea savanna belt did notfind evidence for the species' presence. in Tragulidae
Subspecies and Distribution. H.a.aquaticusOgilby,1841—WAfricafromGuineaandSierraLeonetoGhana. H.a.bates:Lydekker,1906—Nigeria,Cameroon,andpresumablyneighboringcountries. H. a. cottoni Lydekker, 1906 — Republic of the Congo, DR Congo, and presumably Uganda. The Water Chevrotain reportedly has a disjunct distribution, occurring in coastal forests from West Africa and in the rainforests of Central Africa from Nigeria to DR Congo, marginally entering Uganda. It has been listed for the following countries in Central Africa: Angola (Cabinda), Cameroon, Central African Republic, DR Congo, Equatorial Guinea, Gabon, Nigeria, Republic of the Congo, and Uganda (Semliki Valley). A record from Angola's Lunda Norte Province, near the Cassai River,is the southernmost record of the species. The species' status in some countries remains unclear. It is apparently absent from the Republic of Benin and Togo (but the speciesis listed as probable in the Ot Basin in Togo); its supposed occurrence in Guinea Bissau and Senegal remains unsupported by evidence. The species was listed for Sierra Leone, although its presence had been called into question. Photographic evidence seems to clarify that the species occurs in Sierra Leone. In 1850, a specimen was recorded from Gambia, but the present status of the species is unclear. Local people report the species from the Boké Préfecture in NW Guinea, which might be the northernmost area from which the species has been recently reported. Extensive field and market surveys there and in the southern Guinea savanna belt did notfind evidence for the species' presence.
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