DOI: 10.5061/dryad.34tmpg4s1
title: Multiple long-term, landscape-scale datasets reveal intraspecific spatial variation in temporal trends for bird species

description: 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. 
description: # 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.