Find research datasets worth reusing

opencc-by-4.0Dec 2018View details →

Figure3 in Habitat-Based Breeding Strategies of Female Hoplobatrachus occipitalis (Anura: Dicroglossidae) from Daloa Department, Midwest of Côte d'Ivoire

Figure3. Spatial variability in size (SVL) of female Hoplobatrachus occipitalis from Fatiga and Zaliohouan in Daloa Department

opencc-by-4.0Dec 2018View details →

Figure8 in Habitat-Based Breeding Strategies of Female Hoplobatrachus occipitalis (Anura: Dicroglossidae) from Daloa Department, Midwest of Côte d'Ivoire

Figure8. Regression between female size (SVL) and egg diameter in Hoplobatrachus occipitalis from Fatiga and Zaliohouan in Daloa Department

opencc-zeroOct 2021View details →

Airflow modelling predicts seabird breeding habitat across islands

Wind is fundamentally related to shelter and flight performance: two factors that are critical for birds at their nest sites. Despite this, airflows have never been fully integrated into models of breeding habitat selection, even for well-studied seabirds. Here we use computational fluid dynamics to provide the first assessment of whether flow characteristics (including wind speed and turbulence) predict the distribution of seabird colonies, taking common guillemots (Uria aalge) breeding on Skomer island as our study system. This demonstrates that occupancy is driven by the need to shelter from both wind and rain/ wave action, rather than airflow characteristics alone. Models of airflows and cliff orientation both performed well in predicting high quality habitat in our study site, identifying 80% of colonies and 93% of avoided sites, as well as 73% of the largest colonies on a neighbouring island. This suggests generality in the mechanisms driving breeding distributions, and provides an approach for identifying habitat for seabird reintroductions considering current and projected wind speeds and directions.

Fig. 2 in Larval Development And Habitat Usage Of Stream-Breeding Fire Salamanders In An Urban Environment

Fig. 2. Changes in the number of salamander larvae and rainfall. The bold black continuous line shows the mean number of salamander larvae detected during the surveys in the three 10 day intervals of months in Hűvös-ér stream, 2011–2014. The bar graph shows the mean amount (and SD) of precipitation (mm) during the three 10-day intervals of months

Fig. 3 in Larval Development And Habitat Usage Of Stream-Breeding Fire Salamanders In An Urban Environment

Fig. 3. Mean density of salamander larvae (number of larvae/m2) detected in the 16 segments during surveys every 10 days in "Hűvös-ér" stream, 2011–2014 (2011: thin line, 2012:

Fig. 5 in Larval Development And Habitat Usage Of Stream-Breeding Fire Salamanders In An Urban Environment

Fig. 5. Mean number of salamander larvae detected per a year in the "releasing" 2–6 upper segments (continuous bold line), in the "strong collector" middle segments: 7–9 (dashed line) and the "weak collector" lower segments: 10–13 (dotted line) during surveys every 10 days in "Hűvös-ér" stream between 2011–2014. Data from segment 1 have not been plotted because larvae were present at only one time point

Fig. 1 in Larval Development And Habitat Usage Of Stream-Breeding Fire Salamanders In An Urban Environment

Fig. 1. Segments of "Hűvös-ér" stream, where Salamandra salamandra larvae were surveyed. (Numbers indicate individual stream segments, bold meandering line = main branch of the stream, thin branch- ing line = tributaries of the stream, straight lines = segment boundaries, four-pointed stars at segment boundaries and in the stream bed = water steps, double line = main road between Budapest and Solymár, P = "Paprikás"-stream)

publicNov 2024View details →

Data from: Breeding Ammospiza nelsoni (Nelson’s Sparrow) exploits both saltmarsh and hayfields in northern habitats

Open the record for dataset details and reuse information.

publicJun 2025View details →

Habitat availability is insufficient to explain regional variations in white stork breeding habitat preference

Open the record for dataset details and reuse information.

publicOct 2021View details →

Airflow modelling predicts seabird breeding habitat across islands

Open the record for dataset details and reuse information.

publicApr 2024View details →

Data for: Canopy coverage, light, and moisture affect thermoregulatory trade-offs in an amphibian breeding habitat

Open the record for dataset details and reuse information.

opencc-by-4.0May 2012View details →

Habitat suitability maps for boreal-breeding passerine species

About the Maps Maps depict model-predicted species distribution and provide information about relative habitat suitability based on current climate and landcover. The suitability ranking of any mapped grid cell is the sum of the probabilities of that grid cell and all other grid cells with equal or lower probability, multiplied by 100 to give a percentage. This value represents the % of grid cells with a lower suitability value within the boreal/hemiboreal study region. Higher value pixels represent higher habitat suitability for a given species. Models do not account for physiographic barriers that may prevent colonization of otherwise suitable habitat, e.g., the Canadian Cordillera (<a href="#_ENREF_4">Erskine 1977</a>). Therefore actual species distributions may be over-estimated in certain regions, particularly in Alaska.  Modeling Overview The maximum entropy (Maxent) method (<a href="#_ENREF_9">Phillips et al. 2006</a>, <a href="#_ENREF_10">Phillips and Dudik 2008</a>) was used to develop species distribution models (SDM) for passerine species during the breeding season.  Maxent is a powerful machine-learning algorithm with demonstrated high predictive accuracy compared to other SDM methods (<a href="#_ENREF_3">Elith et al. 2006</a>). Although Maxent was developed for presence-only data (e.g., museum records), it is also appropriate for datasets compiled from disparate sources with varying levels of effort, such that information on species absence varies across model units. Although resulting predictions cannot be interpreted as probability of occurrence, they are robust representations of rank-order suitability. The power of Maxent lies in the complexity of relationships (e.g., non-linear, threshold, multiplicative) that it can readily handle, producing detailed, high-accuracy predictions. The key consideration in development of Maxent models, as with traditional resource selection functions (<a href="#_ENREF_8">Manly 1993</a>), is the selection of appropriate “background” data (<a href="#_ENREF_11">Phillips et al. 2009</a>). Otherwise, sample bias can lead to biased predictions. As recommended (<a href="#_ENREF_11">Phillips et al. 2009</a>), we constrained our background to all locations surveyed for birds. Due to high spatial aggregation of survey locations and the resulting potential for bias, we aggregated occurrence records at the level of 4-km grid cells corresponding with the resolution of our climate data.  A species was considered present in a grid cell if at least one individual had been counted over all point-count surveys contained in the grid cell. Model background was thus defined as all surveyed 4-km grid cells (n = 29,059). Climate Data Climate variables were derived from 4-km monthly climate normals (1961-1990) based on a combination of PRISM (<a href="#_ENREF_2">Daly et al. 2002</a>) and WorldClim (<a href="#_ENREF_6">Hijmans et al. 2005</a>) climate data. The western North America portion of these data are described in Wang et al. (<a href="#_ENREF_12">2011</a>). We used a set of 17 derived bioclimatic variables presumed to adequately summarize climate conditions within the boreal forest region (Table 1 in report). We were not concerned with high correlation among these covariates because models were developed for prediction purposes only and our goal for this particular exercise was not to interpret the importance of individual covariates. Landcover Data Landcover data consisted of a 2005 classified landcover map of North America developed by the Council on Economic Development (<a href="http://www.cec.org/Page.asp?PageID=924&ContentID=2819">http://www.cec.org/Page.asp?PageID=924&ContentID=2819</a>). We used 15 of the 19 landcover classifications as inputs to bird models (Table 2 in report). Because landcover was mapped at a 250-m resolution, we summarized the proportion of each landcover type within a 4-km grid cell for prediction purposes. For model-building purposes, we summarized landcover proportions according to the distribution of survey locations within the 4-km grid cell. This was based on the landcover type at the point-count center, reflecting the dominant type surveyed. Avian Data Distribution models were developed for all passerine species (+ 2 non-passerine landbird species) with at least 100 occurrence records in separate 4-km grid cells (n=94).   Avian occurrence records were obtained from two major datasets:  (1) the Boreal Avian Modelling (BAM) point-count dataset (Cumming et al. 2010) and the North American Breeding Bird Survey (BBS) point-count dataset from USGS (<a href="http://www.pwrc.usgs.gov/bbs/">http://www.pwrc.usgs.gov/bbs/</a>). Due to large discrepancies in survey characteristics and species detectability, which have already been addressed for the purpose of density estimation (Sólymos et al. 2013), the focus here was on the occurrence portion of the dataset only. Future efforts will integrate detectability offsets into bioclimatic density models that can be used to generate regional abundance estimates. In order to improve model predictive power within the boreal forest region, data from neighboring hemiboreal regions were also incorporated (as well as data from arctic and mountain regions where possible).  Because the core BAM dataset is largely restricted to the boreal forest region, ancillary data consisted primarily of point-level BBS data (breeding bird atlas datasets were notable exceptions). BBS data were obtained for the level 3 ecoregions (<a href="http://www.epa.gov/wed/pages/ecoregions/na_eco.htm#Level III">http://www.epa.gov/wed/pages/ecoregions/na_eco.htm#Level III</a>) that intersected the boundary of the combined Brandt (<a href="#_ENREF_1">2009</a>) boreal/hemiboreal boundary. This additional data improved coverage of climate and landcover conditions at species’ range limits, thereby providing more opportunities to detect differences in habitat suitability. A total of 117,179 point-count locations were used to summarize species occurrence within 29,059 surveyed grid cells. See Table 3 in report for numbers of individual species occurrence records. Maxent Model Details and Accuracy Assessment Models were developed using Maxent version 3.3.3e.  We used the cumulative probability output format, allowed all feature types except hinge features, and used a regularization multiplier of 1. We ran the model 10 times using bootstrapped subsamples of the BAM/BBS dataset, each time holding out a random 50% for validation purposes (test data). Model predictions were averaged across the 10 bootstrap replicates. Although models were developed using data from outside of the Brandt boreal/hemiboreal boundary, predictions were constrained to this region.. The accuracy of each model was assessed by calculating the area under the curve (AUC) of the receiver operating characteristic plot (<a href="#_ENREF_5">Fielding and Bell 1997</a>) based on test data. AUC values were also averaged across the 10 replicates. The AUC value can be interpreted as the likelihood that a randomly-selected presence location will have a higher suitability score than a randomly-selected background location.  In general, models were reasonably accurate in their prediction of species’ distributions. Average AUC scores ranged from 0.56 for American Robin to 0.97 for American Tree Sparrow (Table 3). Across all 94 species, AUC scores averaged 0.81 ±0.09 (SD). Models for 31 species were considered acceptable 0.7 ≤ AUC < 0.8), 31 were excellent (0.8 ≤ AUC < 0.9), and 17 had outstanding discrimination ability (AUC ≥ 0.9) (<a href="#_ENREF_7">Hosmer and Lemeshow 1989</a>). AUC scores reflected the ability to discriminate among different levels of habitat suitability within the greater boreal/hemiboreal region. Thus, species with distinct range limits within this region were more accurately predicted. NatureServe Comparisons Models and occurrence records were overlaid with published range maps from NatureServe (<a href="http://datazone.birdlife.org/species/requestdis">http://datazone.birdlife.org/species/requestdis</a>) for comparison purposes. For all but three species, the BAM/BBS dataset contained occurrence records outside of NatureServe range map limits. This discrepancy is reflected in the Maxent model predictions. Thus, both occurrence records and model predictions may be used to refine the range limits for several species. All but nine species had data observations north of their published range limits. Although better range maps may exist for many species (e.g., in recently revised Birds of North America volumes, <a href="http://bna.birds.cornell.edu/bna/">http://bna.birds.cornell.edu/bna/</a>), digital versions are not generally available for comparison. Literature Cited Brandt, J. P. 2009. The extent of the North American boreal zone. Environmental Reviews 17:101–161. Cumming, S. G., K. L. Lefevre, E. Bayne, T. Fontaine, F. K. A. Schmiegelow, and S. J. Song. 2010. Toward conservation of Canada's boreal forest avifauna: design and application of ecological models at continental extents. Avian Conservation and Ecology 5(2):8. Daly, C., W. P. Gibson, G. H. Taylor, G. L. Johnson, and P. Pasteris. 2002. A knowledge-based approach to the statistical mapping of climate. Climate Research 22:99-113. Elith, J., C. H. Graham, R. P. Anderson, M. Dudik, S. Ferrier, A. Guisan, R. J. Hijmans, F. Huettmann, J. R. Leathwick, A. Lehmann, J. Li, L. G. Lohmann, B. A. Loiselle, G. Manion, C. Moritz, M. Nakamura, Y. Nakazawa, J. McC. M. Overton, A. Townsend Peterson, S. J. Phillips, K. Richardson, R. Scachetti-Pereira, R. E. Schapire, J. Soberón, S. Williams, M. S. Wisz, and N. E. Zimmermann. 2006. Novel methods improve prediction of species' distributions from occurrence data. Ecography 29:129-151. Erskine, A. J. 1977. Birds in boreal Canada: communities, densities, and adaptations. Ottawa, Canada. Fielding, A. H. and J. F. Bell. 1997. A review of methods for the assessment of prediction errors in conservation presence/absence models. Environmental Conservation 24:38-49. Hijmans, R. J., S. E. Cameron, J. L. Parra, P. G. Jones, and A. Jarvis. 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25:1965-1978. Hosmer, D. W. and S. Lemeshow. 1989. Applied logistic regression. John Wiley and Sons, New York. Manly, B. F. J. 1993. Resource Selection by Animals: Statistical Design and Analysis for Field Studies. Chapman and Hall, London. Phillips, S. J., R. P. Anderson, and R. E. Schapire. 2006. Maximum entropy modeling of species geographic distributions. Ecological Modelling 190:231-259. Phillips, S. J. and M. Dudik. 2008. Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography 31:161-175. Phillips, S. J., M. Dudik, J. Elith, C. H. Graham, A. Lehmann, J. Leathwick, and S. Ferrier. 2009. Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data. Ecological Applications 19:181-197. Sólymos, P., S. M. Matsuoka, E. M. Bayne, S. R. Lele, P. Fontaine, S. G. Cumming, D. Stralberg, F. K. A. Schmiegelow, and S. J. Song. 2013. Calibrating indices of avian density from non-standardized survey data: making the most of a messy situation. Methods in Ecology and Evolution 4:1047-1058. Wang, T., A. Hamann, D. L. Spittlehouse, and T. Q. Murdock. 2011. ClimateWNA-High-Resolution Spatial Climate Data for Western North America. Journal of Applied Meteorology and Climatology 51.

dryad36/100

Hornbill abundance and breeding incidence in relation to habitat modification and fig fruit availability

Asian hornbills are known to forage and breed in fragmented rainforests and agroforestry plantations in human‐modified landscapes adjoining contiguous protected forests. However, the factors influencing year‐round hornbill abundance, demography and tracking of key food resources such as wild fig Ficus fruits in modified habitats and protected forests remain poorly understood. We carried out monthly surveys of two species of high conservation concern, the Vulnerable Great Hornbill (GH, Buceros bicornis) and the endemic Malabar Grey Hornbill (MGH, Ocyceros griseus) for 15 months and monitored ripe fig fruit availability for 12 months along 11 line transects (total length 24 km) in shade‐coffee plantations and adjoining continuous rainforests in a protected area (PA) in the Anamalai Hills, Western Ghats, India. Both hornbill species used plantations and the PA year‐round but distance sampling density estimates were higher in the PA in both nesting (GH by 57%; MGH by 50%) and non‐nesting (GH by 53%; MGH by 144%) seasons. Relative to estimates from 2004 to 2005, mean GH density appeared stable or increasing, whereas MGH had declined by 39% in the PA and by 56% in plantations. Monthly encounter rate of both hornbills tended to be higher in the PA and that of MGH was also positively related to the density of fig trees with ripe fruit. Sex ratios of observed adult birds in the non‐nesting season were relatively even (GH) or slightly female‐biased (MGH), but became male‐biased in both species during the nesting season when females were confined in tree‐cavity nests. We used change in the adult sex ratio of observed birds from the non‐nesting to nesting season to estimate an index of the proportion of adult pairs breeding at any point within the season, providing the first such estimates for any hornbill species. The proportion of breeding pairs was higher in the PA (GH – 56%, MGH – 64%) than in the plantations (GH – 33%, MGH – 30%). Although hornbills use shade‐coffee plantations year‐round, partly due to fig fruit availability, differences in hornbill density and breeding incidence, as assessed from the sex ratios of observed adult birds, indicate that plantations are a sub‐optimal habitat for both species.

opencc-zeroJan 2021View details →

dryad36/100

Data from: Sound settlement: noise surpasses land cover in explaining breeding habitat selection of secondary cavity-nesting birds

Birds breeding in heterogeneous landscapes select nest sites by cueing in on a variety of factors from landscape features and social information to the presence of natural enemies. We focus on determining the relative impact of anthropogenic noise on nest site occupancy, compared to amount of forest cover, which is known to strongly influence the selection process. We examine chronic, industrial noise from natural gas wells directly measured at the nest box as well as site-averaged noise, using a well-established field experimental system in northwestern New Mexico. We hypothesized that high levels of noise, both at the nest site and in the environment, would decrease nest box occupancy. We set up nest boxes using a geospatially paired control and experimental site design and analyzed four years of occupancy data from four secondary cavity-nesting birds common to the Colorado Plateau. We found different effects of noise and landscape features depending on species, with strong effects of noise observed in breeding habitat selection of Myiarchus cinerascens, the Ash-throated Flycatcher, and Sialia currucoides, the Mountain Bluebird. In contrast, the amount of forest cover less frequently explained habitat selection for those species or had a smaller standardized effect than the acoustic environment. Although forest cover characterization and management is commonly employed by natural resource managers, our results show that characterizing and managing the acoustic environment should be an important tool in protected area management.

opencc-zeroDec 2015View details →

opencc-by-4.0Feb 2022View details →

Data from: Can attraction to and competition for high-quality habitats shape breeding propensity?

This archive contains the data analysed in the paper 'Can attraction to and competition for high-quality habitats shape breeding propensity?' by Acker et al. The dataset is provided as a .Rdata file ('dataset.Rdata'), and full description of the content is provided in the file 'README.txt'. All additional details are available in the main text and appendices of the paper.

opencc-by-4.0Mar 2016View details →

Fig. 11 in Breeding Bird Assemblage In A Mosaic Of Urbanized Habitats In A Cenral European City

Fig. 11. Distribution of occupied territories of Streptopelia decaocto and Columba palumbus.

opencc-by-4.0Mar 2016View details →

Fig. 8 in Breeding Bird Assemblage In A Mosaic Of Urbanized Habitats In A Cenral European City

Fig. 8. Distribution of occupied territories of Phoenicurus phoenicurus.

opencc-by-4.0Mar 2016View details →

Fig. 10 in Breeding Bird Assemblage In A Mosaic Of Urbanized Habitats In A Cenral European City

Fig. 10. Distribution of occupied territories of Corvus cornix, C. frugilegus and Pica pica.

opencc-by-4.0Jan 2016View details →

Fig. 3 in SHORT COMMUNICATION Monitoring a population of Cruziohyla craspedopus (Funkhouser, 1957) using an artificial breeding habitat

Fig. 3. Newly metamorphosed juvenile Cruziohyla craspedopus.