DOI: 10.5061/dryad.s7h44j1dv
title: A matter of scale: Identifying the best spatial and temporal scale of environmental variables to model the distribution of a small cetacean

description: The importance of scale when investigating ecological patterns and
 processes is recognised across many species. In marine ecosystems, the
 processes that drive species distribution have a hierarchical structure
 over multiple nested spatial and temporal scales. Hence, multi-scale
 approaches should be considered when developing accurate distribution
 models to identify key habitats, particularly for populations of
 conservation concern. Here, we propose a modelling procedure to identify
 the best spatial and temporal scale for each modelled and remotely sensed
 oceanographic variable to model harbour porpoise (Phocoena phocoena)
 distribution. Harbour porpoise sightings were recorded during dedicated
 line-transect aerial surveys conducted in the summer of 2016, 2021 and
 2022 in the Northeast Atlantic. Binary generalised additive models were
 used to assess the relationships between porpoise presence and
 oceanographic variables at different spatial (5, 20 and 40 km) and
 temporal (daily, monthly and across survey period) scales. Selected
 variables included sea surface temperature, thermal fronts, chlorophyll-a,
 sea surface height, mixed layer depth and salinity. A total of 30,514 km
 was covered on-effort with 216 harbour porpoise sightings recorded.
 Overall, the best spatial scale corresponded to the coarsest resolution
 considered in this study (40 km), while porpoise presence showed stronger
 association with oceanographic variables summarised at a longer temporal
 scale (monthly and averaged over survey period). Habitat models
 including covariates at coarse spatial and temporal scales may better
 reflect the processes driving availability and abundance of prey resources
 at the large scales covered during the surveys. These findings support the
 hypothesis that a multi-scale approach should be applied when
 investigating species distribution. Identifying suitable spatial and
 temporal scale would improve the functional interpretation of the
 underlying relationships, particularly when studying how a small marine
 predator interacts with its environment and responds to climate and
 ecosystem changes. 
description: # A matter of scale: Identifying the best spatial and temporal scale of
 environmental variables to model the distribution of a small cetacean
 [https://doi.org/10.5061/dryad.s7h44j1dv](https://doi.org/10.5061/dryad.s7h44j1dv) Harbour porpoise sightings were recorded during dedicated line-transect aerial surveys conducted in the summer of 2016, 2021 and 2022. Binary generalised additive models were used to assess the relationships between porpoise presence and oceanographic variables at different spatial (5, 20 and 40 km) and temporal (daily, monthly and across survey period) scales. Selected variables included sea surface temperature, thermal fronts, chlorophyll-a, sea surface height, mixed layer depth and salinity. ## Description of the data and file structure Each dataset contains presence and absence data of harbour porpoise in a grid cell and associated values of one oceanographic variable at various spatial and temporal scale. The covariates were extracted from the Copernicus Marine Service. These six datasets were used to run the binary generalised additive models. **File list:** 1\) HPSightings_CHLA.csv\ 2\) HPSightings_Fronts.csv\ 3\) HPSightings_MLD.csv\ 4\) HPSightings_SAL.csv\ 5\) HPSightings_SSH.csv\ 6\) HPSightings_SST.csv **Information on data files** Common column headers for all files: wkt_geom: combined coordinates of centroid of each data point id: cell id number left, top, right, bottom: coordinates of cell effort_m2: effort covered within the cell in square metres Transect_ID: name of transect that was covered within the cell xCentroid, yCentroid: separate coordinates of centroids (in metres) PreAb: presence or absence of harbour porpoise (values 1 or 0, respectively) NumIndivid: number of animals per cell NumSightin: number of sightings per cell lat, lon: latitude and longitude coordinates of centroids date, newDate, year, month, day: date of survey conducted **Information on other variables:** *1) HPSightings_CHLA.csv --&gt; Chlorophyll-a (chla)* \ *2) HPSightings_MLD.csv --&gt; Mixed layer depth (mld)*\ *3) HPSightings_SAL.csv --&gt; Salinity (sal)*\ *4) HPSightings_SSH.csv --&gt; Sea surface height (ssh)* a. chla/mld/sal/ssh_7km_daily: CHLA at a spatial resolution of 7km and a daily temporal resolution\ b. chla/mld/sal/ssh_7km_month: CHLA at a spatial resolution of 7km and a monthly temporal resolution\ c. chla/mld/sal/ssh_7km_period: CHLA at a spatial resolution of 7km and a temporal resolution averaged across the survey period for each year d. chla/mld/sal/ssh_21km_daily: CHLA at a spatial resolution of 21km and a daily temporal resolution\ e. chla/mld/sal/ssh_21km_month: CHLA at a spatial resolution of 21km and a monthly temporal resolution\ f. chla/mld/sal/ssh_21km_period: CHLA at a spatial resolution of 21km and a temporal resolution averaged across the survey period for each year g. chla/mld/sal/ssh_42km_daily: CHLA at a spatial resolution of 42km and a daily temporal resolution\ h. chla/mld/sal/ssh_42km_month: CHLA at a spatial resolution of 42km and a monthly temporal resolution\ i. chla/mld/sal/ssh_42km_period: CHLA at a spatial resolution of 42km and a temporal resolution averaged across the survey period for each year *5) HPSightings_Fronts.csv --&gt; Thermal fronts (slopeSST)*\ *6) HPSightings_SST.csv --&gt; Sea surface temperature (SST)* a. slopeSST/SST_5km_daily: CHLA at a spatial resolution of 5km and a daily temporal resolution\ b. slopeSST/SST_5km_month: CHLA at a spatial resolution of 5km and a monthly temporal resolution\ c. slopeSST/SST_5km_period: CHLA at a spatial resolution of 5km and a temporal resolution averaged across the survey period for each year d. slopeSST/SST_20km_daily: CHLA at a spatial resolution of 20km and a daily temporal resolution\ e. slopeSST/SST_20km_month: CHLA at a spatial resolution of 20km and a monthly temporal resolution\ f. slopeSST/SST_20km_period: CHLA at a spatial resolution of 20km and a temporal resolution averaged across the survey period for each year g. slopeSST/SST_40km_daily: CHLA at a spatial resolution of 40km and a daily temporal resolution\ h. slopeSST/SST_40km_month: CHLA at a spatial resolution of 40km and a monthly temporal resolution\ i. slopeSST/SST_40km_period: CHLA at a spatial resolution of 40km and a temporal resolution averaged across the survey period for each year ## Sharing/Access information The oceanographic variables covering the study area were acquired from the Copernicus Marine Service. * [https://data.marine.copernicus.eu/](https://data.marine.copernicus.eu/)