A matter of scale: Identifying the best spatial and temporal scale of environmental variables to model the distribution of a small cetacean
Cite this dataset
Goh, Tiffany (2024). A matter of scale: Identifying the best spatial and temporal scale of environmental variables to model the distribution of a small cetacean [Dataset]. Dryad. https://doi.org/10.5061/dryad.s7h44j1dv
Abstract
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.
README: 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
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 --> Chlorophyll-a (chla) \
2) HPSightings_MLD.csv --> Mixed layer depth (mld)\
3) HPSightings_SAL.csv --> Salinity (sal)\
4) HPSightings_SSH.csv --> 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 --> Thermal fronts (slopeSST)\
6) HPSightings_SST.csv --> 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.
Funding
Department of the Environment, Climate and Communications
Department of Housing, Local Government and Heritage
Sustainable Energy Authority of Ireland
Geological Survey of Ireland