Data from: Using camera traps to estimate habitat preferences and occupancy patterns of vertebrates in boreal wetlands
Data files
Oct 10, 2023 version files 52.08 KB
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Cam_multisp_v4.Rdata
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cam_ocsite_v3.csv
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Code_Multispecies_model.docx
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det_cam_active.csv
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det_degel.csv
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det_precip_acum.csv
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README.md
Abstract
Wetlands are a critical habitat for boreal mammals and birds that rely on them for breeding, foraging, and resting. However, wetlands in boreal regions are increasingly experiencing natural and human pressures. These impacts can lead to a reduction in the availability of wetland habitats for boreal mammals and birds that rely on wetlands for breeding, foraging, and resting. To inform management and conservation, camera traps provide an opportunity to survey mammals and birds to investigate their habitat preferences. We aimed to evaluate the effect of habitat features on the occupancy of mammals and birds in boreal wetlands. We used a multispecies occupancy model to estimate the habitat associations of 11 mammals and 45 avian species detected at 50 sampling ponds during the summers of 2018 and 2019 in Northern Quebec. Our results indicate that certain mammals, such as Red Fox and River Otters, and birds including the American Pipit, Common Raven, Hooded Merganser, and Greater Yellowlegs showed a preference for peatland ponds, whereas the Common Grackle preferred beaver ponds. We found few effects of distance to roads, and no effect of amount of forest cover on species occupancy. The occupancy of 27% of mammals and 24% of birds decreased with increasing latitude. These findings offer valuable insights for informing conservation initiatives focused on the preservation of wetlands in northern Quebec. By discerning the specific types of ponds preferred by each species, conservationists can strategically ensure the preservation and proper management of these habitats, thereby enhancing their conservation efforts.
README
This README file was generated on 2023-10-09 by Mariano J. Feldman.
GENERAL INFORMATION
- Title of Dataset: Data from: Using camera traps to estimate habitat preferences and occupancy patterns of vertebrates in boreal wetlands
- Author Information
A. Principal Contact Information
Name: Mariano J. Feldman
Institution: Université du Quebec en Abitibi-Temiscamingue (UQAT), Conservation Biology Group. Landscape Dynamics and Biodiversity programme. Forest Science and Technology Center of Catalonia (CTFC), Solsona, Catalonia, Spain.
Email: mariano.feldman@ctfc.cat
<br>
B. Alternative Contact Information
Name: Marc J. Mazerolle
Institution: Université Laval Email: marc.mazerolle@sbf.ulaval.ca - Date of data collection: Data was collected from May to August 2018 and 2019.
- Geographic location of data collection: All peatland and beaver ponds were located in Northern Quebec, Canada.
- Information about funding sources that supported the collection of the data: NSERC of Canada, Université du Québec en Abitibi-Témiscamingue (UQAT) industrial research chair on northern biodiversity in a mining context
SHARING/ACCESS INFORMATION
- Licenses/restrictions placed on the data: None
- Links to publications that cite or use the data: Feldman M.J., Mazerolle, M.J., Imbeau, I., Fenton, N.J. (2023). Using camera traps to estimate habitat preferences and occupancy patterns of vertebrates in boreal wetlands. Wetlands.
- Links to other publicly accessible locations of the data: None
- Links/relationships to ancillary data sets: None
- Was data derived from another source? No A. If yes, list source(s): NA
- Recommended citation for this dataset:
Feldman M.J., Mazerolle, M.J., Imbeau, I., Fenton, N.J. (2023). Data from: Using camera traps to estimate habitat preferences and occupancy patterns of vertebrates in boreal wetlands. Dryad Digital.
DATA & FILE OVERVIEW
- File List:
Dataset is divided into 6 files: 4 spreadsheets, 1 table code including the multispecies model structure,
and one R file that contains presence/absence data for 11 mammal species and 45 avian species in a form of an array
3-dimensional table (A- “Cam_multisp_v4.Rdata”). All occupancy variables used for analysis are included in the same form
(B- “cam_ocsite_v3.csv”). Three detection covariates are included in separate files, containing the cumulative rainfall
during a given day names as C- “det_precip_acum.csv”. The number of days since snowmelt is included as a numerical
variable in the same form, and named as D- “det_degel.csv”. The number of active cameras during a given day is included as
E- “det_cam_active.csv”. The multispecies model code is named F- “Code_Multispecies model.docx”.
A) Cam_multisp_v4.Rdata
B) cam_ocsite_v3.csv
C) det_precip_acum.csv
D) det_degel.csv
E) det_cam_active.csv
F) Code_Multispecies model.docx
- Relationship between files, if important: Files from A to E are the source for the F
- Additional related data collected that was not included in the current data package: None
- Are there multiple versions of the dataset? No A. If yes, name of file(s) that was updated: NA i. Why was the file updated? NA ii. When was the file updated? NA
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DATA-SPECIFIC INFORMATION FOR: cam_ocsite_v3.csv
Occupancy variables used for analysis
- Number of variables: 5
- Number of cases/rows: 100
- Variable List:
- id_site: site identification
- anne: year ("0"=2018, "1"=2019)
- type_hab: pond type ("castor": beaver pond; "tourb": peatland pond)
- chemin_dist: distance from the road to the pond site in metres
- latitud: latitude of pond sites
- foret: Forest cover surrounding ponds within 1 km
- Missing data codes: Data not available for some sites in column "foret"
- Specialized formats or other abbreviations used: None
#########################################################################
DATA-SPECIFIC INFORMATION FOR: det_precip_acum.csv
Detection covariate: cumulative rainfall during a given day
- Number of variables: 14
- Number of cases/rows: 100
- Variable List:
- ident: site identification
- vis 1 to vis 7: visit survey of pond site from day 1 ("vis 1") to day 7 ("vis 7") in 2018
- vis 8 to vis 14: visit survey of pond site from day 1 ("vis 8") to day 7 ("vis 14") in 2019
- Missing data codes: Data not available for one site in 2019 ("CASA_TO_8 2019")
- Specialized formats or other abbreviations used: None
#########################################################################
DATA-SPECIFIC INFORMATION FOR: det_degel.csv
Detection covariate: number of days since snowmelt
- Number of variables: 14
- Number of cases/rows: 100
- Variable List:
- ident: site identification
- vis 1 to vis 7: visit survey of pond site from day 1 ("vis 1") to day 7 ("vis 7") in 2018
- vis 8 to vis 14: visit survey of pond site from day 1 ("vis 8") to day 7 ("vis 14") in 2019
- Missing data codes: Data not available for one site in 2019 ("CASA_TO_8 2019")
- Specialized formats or other abbreviations used: None
#########################################################################
DATA-SPECIFIC INFORMATION FOR: det_cam_active.csv
Detection covariate: number of days since snowmelt
- Number of variables: 14
- Number of cases/rows: 100
- Variable List:
- ident: site identification
- vis 1 to vis 7: visit survey of pond site from day 1 ("vis 1") to day 7 ("vis 7") in 2018
- vis 8 to vis 14: visit survey of pond site from day 1 ("vis 8") to day 7 ("vis 14") in 2019
- Missing data codes: Data not available for one site in 2019 ("CASA_TO_8 2019")
- Specialized formats or other abbreviations used: None
Methods
Camera trap survey
We conducted camera trap surveys at 50 wetlands over two summers in 2018 and 2019, using three cameras per pond in a triangular configuration. We used scent lures as attractants, and cameras were rotated and baited accordingly. Data collection occurred during two sessions of seven consecutive days per year. Each camera array consisted of three infrared Bushnell Trophy Cam HD motion-sensing digital cameras set to be active 24 hours/day. Cameras were placed at the edge of the pond and secured to a tree or a wooden stake at an average height of 30–60 cm at about 2–5 m from the water. Cameras were triggered by animal movements and programmed to take three photographs per trigger event, and a following video of 10-s, with a 1-min interval delay between detections to avoid that a single animal would be the subject of a long event of recording. We placed scent lure on a stick at 2 -5 m in front of the camera trap to increase the probability of detecting animals approaching the lure. At the end of each session, cameras were checked, photos were analyzed to identify bird and mammal species, and records from the same species at the same pond on the same day were combined into one detection event. Covariates such as pond type, forest cover, year, latitude, and distance to the nearest road were considered in analyzing bird and mammal occupancy and detection probability. Variables such as cumulative rainfall, days since snowmelt, and sampling effort were also factored into the detection analysis.
Data processing and analysis
To enhance species detection, we combined observations from three camera traps at a specific site on a given day, resulting in a data matrix of 100 rows and 14 columns per species. Using a multispecies occupancy framework, we analyzed bird and mammal communities, considering various factors such as pond type, forest cover, year, latitude, and distance to the nearest road. Model parameters were estimated using a Bayesian approach with Markov chain Monte Carlo in JAGS 4.3.0 within R 4.1.2. Convergence of the chains was assessed through trace plots, posterior density plots, and the Brooks-Gelman-Rubin statistic. Model fit was evaluated using posterior predictive checks and the area under the receiver operating characteristic curve.
Usage notes
We estimated model parameters using a Bayesian approach based on Markov chain Monte Carlo (MCMC) in JAGS 4.3.0 within R 4.1.2 using the jagsUI package (Plummer et al. 2006; Lunn et al. 2013; Kellner 2019; R Core Team 2021). The description of each column header for each spreadsheet is provided in an associated ReadMe file. The steps to follow for each analysis are provided in the R code files. The final dataset included detection/non-detection data for 11 mammal species and 45 avian species from 50 ponds from May to August 2018 and 2019 ("Cam_multisp_v4.Rdata"). This dataset is shared in one file in the form of a 3-dimensional array table, which contained rows for pond units (n=100), columns for visits (n=14), and rectangular matrices for species (n=56). Please contact the author for any additional inquiries of how data was collected and analyze.