Multiple long-term, landscape-scale datasets reveal intraspecific spatial variation in temporal trends for bird species
Data files
Sep 12, 2024 version files 319.48 KB
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Lindenmayer_et_al_2024_data_and_code.zip
313.99 KB
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README.md
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Abstract
Quantifying temporal changes in species occurrence has been a key part of ecology since its inception. We quantified multi-decadal site occupancy trajectories for 18 bird species in four independent long-term, large-scale studies (571 sites, ~ 1000 km latitude) in Australia. We found evidence of a year x long-term study interaction in the best-fitting models for 14 of the 18 species analysed, with differences in the temporal trajectories of the same species in multiple studies consistent with non-stationarity. Non-stationarity patterns in occupancy were not related to the distance from a species niche centroid; species in locations further from their niche centroid did not demonstrate differing temporal trajectories to those closer to their niche centroid. Further, temporal trajectories of species were not associated with climatic values for each study relative to their niche. Our findings demonstrate the need for multiple long-term studies across a species range, especially when tailoring conservation decisions for populations.
README: Data and code for Lindenmayer et. al 2024, Multiple long-term, landscape-scale datasets reveal intraspecific spatial variation in temporal trends for bird species. Ecology Letters.
Description
The data package contains several csv files, R code files, RData files
Data:
- 'All_data_combined.csv' contains the point count surveys for all data from the four studies in this paper.
- 'YearSite': the combined site code and year
- 'SiteCode': the unique identifier for the site
- 'SurveyYear': year of the survey
- 'RepeatNumber': the point count visit per year per site
- 'StudyDescription': the study in which the point count was located
- 'Treat': the main treatment for site
- 'Temperature' (scale 1-10) , 'WindId' (scale 1[calm]-4[windy]), 'CloudsId' (scale 1[clear]-6[completely overcast], 'ObserverId' (each number refers to an observer): the observation level predictors
- Following columns are binomial data of whether a given species is observed in that point count.
- 'birdcounts': the sum of the species observations (i.e. species richness)
- 'Booderee_data.csv' contains the point count surveys for the Booderee NP study.
- 'YearSite': the combined site code and year
- 'SiteCode': the unique identifier for the site
- 'SurveyYear': year of the survey
- 'RepeatNumber': the point count visit per year per site
- 'StudyDescription': the study in which the point count was located
- 'Treat': the main treatment for site
- 'Temperature' (scale 1-10) , 'WindId' (scale 1[calm]-4[windy]), 'CloudsId' (scale 1[clear]-6[completely overcast], 'ObserverId' (each number refers to an observer): the observation level predictors
- Following columns are binomial data of whether a given species is observed in that point count.
- 'Nanangroe_data.csv' contains the point count surveys for the Nanangroe study.
- 'YearSite': the combined site code and year
- 'SiteCode': the unique identifier for the site
- 'SurveyYear': year of the survey
- 'RepeatNumber': the point count visit per year per site
- 'StudyDescription': the study in which the point count was located
- 'Treat': the main treatment for site
- 'Temperature' (scale 1-10) , 'WindId' (scale 1[calm]-4[windy]), 'CloudsId' (scale 1[clear]-6[completely overcast], 'ObserverId' (each number refers to an observer): the observation level predictors
- Following columns are binomial data of whether a given species is observed in that point count.
- 'SouthWestSlopes_data.csv' contains the point count surveys for the South West Slopes study.
- 'YearSite': the combined site code and year
- 'SiteCode': the unique identifier for the site
- 'SurveyYear': year of the survey
- 'RepeatNumber': the point count visit per year per site
- 'StudyDescription': the study in which the point count was located
- 'Treat': the main treatment for site
- 'Temperature' (scale 1-10) , 'WindId' (scale 1[calm]-4[windy]), 'CloudsId' (scale 1[clear]-6[completely overcast], 'ObserverId' (each number refers to an observer): the observation level predictors
- Following columns are binomial data of whether a given species is observed in that point count.
- 'VictorianHighland_data.csv' contains the point count surveys for the Victorian Highlands study.
- 'YearSite': the combined site code and year
- 'SiteCode': the unique identifier for the site
- 'SurveyYear': year of the survey
- 'RepeatNumber': the point count visit per year per site
- 'StudyDescription': the study in which the point count was located
- 'Treat': the main treatment for site
- 'Temperature' (scale 1-10) , 'WindId' (scale 1[calm]-4[windy]), 'CloudsId' (scale 1[clear]-6[completely overcast], 'ObserverId' (each number refers to an observer): the observation level predictors
- Following columns are binomial data of whether a given species is observed in that point count.
- 'modelstofit.csv' - a dataframe with two columns specifying which species to analyse in each study
- 'Species': species
- 'StudyId': the study
- 'sp_df.RData' - a lookup table used in the code.
- 'CommonName': the common name of the bird species
- 'ScientificName': the Latin binomial scientific name for the species
- 'CommonName_no_space': variation on the common name variable
R Code:
- 'my_OccSummary.R' is code containing a function to extract model summary tables from spOccupancy objects. It is an edited version of the code provided in the spOccupancy() R package.
- 'All species multi season occupancy modelling.R' is the first bit of R code for the analysis. It contains the code to fit species occupancy models for species across all studies combined.
- 'Boo multiseason occupancy modelling.R' is the R code to fit species occupancy models for species in the Booderee study.
- 'Nan multiseason occupancy modelling.R' is the R code to fit species occupancy models for species in the Nanangroe study.
- 'SWS multiseason occupancy modelling.R' is the R code to fit species occupancy models for species in the South West slopes study.
- 'Vic multiseason occupancy modelling.R' is the R code to fit species occupancy models for species in the Victorian Highlands study.
- 'Hypervolumes and subsequent modelling.R' is the R code to construct hypervolumes of species' niches. It then contains code to fit the Bayesian mixed model towards the end of the analysis. This section of code outlines that polygons for the species should be downloaded from birdlife https://datazone.birdlife.org/home.