DOI: 10.5061/dryad.8467g
title: Data from: Integrated species distribution models: combining presence-background data and site-occupany data with imperfect detection

description: Two main sources of data for species distribution models (SDMs) are
 site-occupancy (SO) data from planned surveys, and presence-background
 (PB) data from opportunistic surveys and other sources. SO surveys give
 high quality data about presences and absences of the species in a
 particular area. However, due to their high cost, they often cover a
 smaller area relative to PB data, and are usually not representative of
 the geographic range of a species. In contrast, PB data is plentiful,
 covers a larger area, but is less reliable due to the lack of information
 on species absences, and is usually characterised by biased sampling. Here
 we present a new approach for species distribution modelling that
 integrates these two data types. We have used an inhomogeneous Poisson
 point process as the basis for constructing an integrated SDM that fits
 both PB and SO data simultaneously. It is the first implementation of an
 Integrated SO–PB Model which uses repeated survey occupancy data and also
 incorporates detection probability. The Integrated Model's
 performance was evaluated, using simulated data and compared to approaches
 using PB or SO data alone. It was found to be superior, improving the
 predictions of species spatial distributions, even when SO data is sparse
 and collected in a limited area. The Integrated Model was also found
 effective when environmental covariates were significantly correlated. Our
 method was demonstrated with real SO and PB data for the Yellow-bellied
 glider (Petaurus australis) in south-eastern Australia, with the
 predictive performance of the Integrated Model again found to be superior.
 PB models are known to produce biased estimates of species occupancy or
 abundance. The small sample size of SO datasets often results in poor
 out-of-sample predictions. Integrated models combine data from these two
 sources, providing superior predictions of species abundance compared to
 using either data source alone. Unlike conventional SDMs which have
 restrictive scale-dependence in their predictions, our Integrated Model is
 based on a point process model and has no such scale-dependency. It may be
 used for predictions of abundance at any spatial-scale while still
 maintaining the underlying relationship between abundance and area.
description: PBSOR code and instructions
 to run the model on the data providedevaporation_januaryA spatial layer of the evaporation in January in the study area used for analysing of yellow-bellied glider data. Description of all the covariates can be found in chapter 4 of the SIEvaporation_Jan.zipevaporation_julyA spatial layer of evaporation in July in the study area used for analysing of yellow-bellied glider data. Description of all the covariates can be found in chapter 4 of the SIEvaporation_Jul.ziplog_vertical_distance_major_streamsA spatial layer of the distance to the nearest stream in the study area used in the analysis of yellow-bellied glider data. Description of all the covariates can be found in chapter 4 of the SIMaxTemp_JanA spatial layer of the maximum temperature in January in the study area used in the analysis of yellow-bellied glider data. Description of all the covariates can be found in chapter 4 of the SIMinTemp_JulA spatial layer of the minimum temperature in July in the study area used in the analysis of yellow-bellied glider data. Description of all the covariates can be found in chapter 4 of the SIRainDays_JanA spatial layer of the number of rainy days in January in the study area used in the analysis of yellow-bellied glider data. Description of all the covariates can be found in chapter 4 of the SIRainfall_JulA spatial layer of the average rainfall in January in the study area used in the analysis of yellow-bellied glider data. Description of all the covariates can be found in chapter 4 of the SIRainDays_Jul.zipRainDays_JulA spatial layer of the average number of rainy days in January in the study area used in the analysis of yellow-bellied glider data. Description of all the covariates can be found in chapter 4 of the SIRainfall_JanA spatial layer of the average rainfall in January in the study area used in the analysis of yellow-bellied glider data. Description of all the covariates can be found in chapter 4 of the SIterrain_ruggedness_indexA spatial layer of the terrain ruggedness index in the study area used in the analysis of yellow-bellied glider data. Description of all the covariates can be found in chapter 4 of the SIwetness_indexA spatial layer of the wetness index in the study area used in the analysis of yellow-bellied glider data. Description of all the covariates can be found in chapter 4 of the SIvisible_skyA spatial layer of the proportion of the hemisphere visible from the location in the study area used in the analysis of yellow-bellied glider data. Description of all the covariates can be found in chapter 4 of the SISO-dataSite occupancy data. The archive contains the values of the covariates at the survey sites used in the analysis of yellow-bellied glider data (so_occupancy.csv and so_detection) and results of the two repeated surveys (yb-so.csv)yb-pb-locationList of locations of sightings of yellow-bellied glider in opportunistic surveyselevationA spatial layer of the elevation in the study area used for analysing of yellow-bellied glider data. Description of all the covariates can be found in chapter 4 of the SIDem75mInteger.zip