65 Habitat Occupancy Models

Description: Habitat Occupancy

Found in: State of the Ecosystem - Gulf of Maine & Georges Bank (2018), State of the Ecosystem - Mid-Atlantic (2018)

Indicator category: Database pull with analysis; Extensive analysis; not yet published; Published methods

Contributor(s): Kevin Friedland

Data steward: Kevin Friedland,

Point of contact: Kevin Friedland,

Public availability statement: Source data are available upon request (see Survdat, CHL/PP, and Data Sources below for more information). Model-derived time series are available here.

65.1 Methods

Habitat area with a probability of occupancy greater than 0.5 was modeled for many species throughout the Northeast Large Marine Ecosystem (NE-LME) using random forest decision tree models.

65.1.1 Data sources

Models were parameterized using a suite of static and dynamic predictor variables, with occurrence and catch per unit effort (CPUE) of species from spring and fall Northeast Fisheries Science Center (NEFSC) bottom trawl surveys (BTS) serving as response variables. Sources of variables used in the analyses are described below.

65.1.1.1 Station depth

The NEFSC BTS data included depth observations made concurrently with trawls at each station. Station depth was a static variable for these analyses.

65.1.1.2 Ocean temperature and salinity

Sea surface and bottom water temperature and salinity measurements were included as dynamic predictor variables in the model, and were collected using Conductivity/Temperature/Depth (CTD) instruments. Ocean temperature and salinity measurements had the highest temporal coverage during the spring (February-April) and fall (September-November) months. Station salinity data were available between 1992-2016.

65.1.1.3 Habitat descriptors

A variety of benthic habitat descriptors were incorporated as predictor variables in occupancy models (Table 65.1). The majority of these parameters are based on depth (e.g. BPI, VRM, Prcury, rugosity, seabedforms, slp, and slpslp). The vorticity variable is based on current estimates, and the variable soft_sed based on sediment grain size.

Table 65.1: Table 65.2: Habitat descriptors used in model parameterization.
Variables Notes References
Namera_vrm Vector Ruggedness Measure (VRM) measures terrain ruggedness as the variation in three-dimensional orientation of grid cells within a neighborhood based on The Nature Conservancy Northwest Atlantic Marine Ecoregional Assessment (“NAMERA”) data. Hobson (1972); Sappington, Longshore, and Thompson (2007)
Prcurv (2 km, 10 km, and 20 km) Benthic profile curvature at 2km, 10km and 20 km spatial scales was derived from depth data. Winship et al. (2018)
Rugosity A measure of small-scale variations of amplitude in the height of a surface, the ratio of the real to the geometric surface area. Friedman et al. (2012)
seabedforms Seabed topography as measured by a combination of seabed position and slope. [http://www.northeastoceandata.org/
Slp (2 km, 10 km, and 20 km) Benthic slope at 2km, 10km and 20km spatial scales. Winship et al. (2018)
Slpslp (2 km, 10 km, and 20 km) Benthic slope of slope at 2km, 10km and 20km spatial scales Winship et al. (2018)
soft_sed Soft-sediments is based on grain size distribution from the USGS usSeabed: Atlantic coast offshore surficial sediment data. [http://www.northeastoceandata.org/
Vort (fall - fa; spring - sp; summer - su; winter - wi) Benthic current vorticity at a 1/6 degree (approx. 19 km) spatial scale. Kinlan et al. (2016)

65.1.1.4 Zooplankton

Zooplankton data are acquired through the NEFSC Ecosystem Monitoring Program (“EcoMon”). For more information regarding the collection process for these data, see Kane (2007), Kane (2011), and Morse et al. (2017). The bio-volume of the 18 most abundant zooplankton taxa were considered as potential predictor variables.

65.1.1.5 Remote sensing data

Both chlorophyll concentration and sea surface temperature (SST) from remote sensing sources were incorporated as static predictor variables in the model. During the period of 1997-2016, chlorophyll concentrations were derived from observations made by the Sea-viewing Wide Field of View Sensor (SeaWIFS), Moderate Resolution Imaging Spectroradiometer (MODIS-Aqua), Medium Resolution Imaging Spectrometer (MERIS), and Visible and Infrared Imaging/Radiometer Suite (VIIRS).

65.1.2 Data processing

65.1.2.1 Zooplankton

Missing values in the EcoMon time series were addressed by summing data over five-year time steps for each seasonal time frame and interpolating a complete field using ordinary kriging. Missing values necessitated interpolation for spring data in 1989, 1990, 1991, and 1994. The same was true of the fall data for 1989, 1990, and 1992.

65.1.2.2 Remote sensing data

An overlapping time series of observations from the four sensors listed above was created using a bio-optical model inversion algorithm (Maritorena et al. 2010). Monthly SST data were derived from MODIS-Terra sensor data (available here).

65.1.2.3 Ocean temperature and salinity

Date of collection corrections for ocean temperature data were developed using linear regressions for the spring and fall time frames; standardizing to collection dates of April 3 and October 11 for spring and fall. No correction was performed for salinity data. Annual data for ocean temperature and salinity were combined with climatology by season through an optimal interpolation approach. Specifically, mean bottom temperature or salinity was calculated by year and season on a 0.5° grid across the ecosystem, and data from grid cells with >80% temporal coverage were used to calculate a final seasonal mean. Annual seasonal means were then used to calculate combined anomalies for seasonal surface and bottom climatologies.

An annual field was then estimated using raw data observations for a year, season, and depth using universal kriging (Hiemstra et al. 2008), with depth included as a covariate (on a standard 0.1° grid). This field was then combined with the climatology anomaly field and adjusted by the annual mean using the variance field from kriging as the basis for a weighted mean between the two. The variance field was divided into quartiles with the lowest quartile assigned a weighting of 4:1 between the annual and climatology values. The optimally interpolated field at these locations was therefore skewed towards the annual data, reflecting their proximity to actual data locations and associated low kriging variance. The highest kriging variance quartile (1:1) reflected less information from the annual field and more from the climatology.

65.1.3 Data analysis

65.1.3.1 Occupancy models

Prior to fitting the occupancy models, predictor variables were tested for multi-collinearity and removed if found to be correlated. The final model variables were then chosen utilizing a model selection process as shown by Murphy, Evans, and Storfer (2010) and implemented with the R package rfUtilities (Evans and Murphy 2018). Occupancy models were then fit as two-factor classification models (absence as 0 and presence as 1) using the randomForest R package (Liaw and Wiener 2002).

65.1.3.2 Selection criteria and variable importance

The irr R package (Gamer, Lemon, and Singh 2012) was used to calculate Area Under the ROC Curve (AUC) and Cohen’s Kappa for assessing accuracy of occupancy habitat models. Variable importance was assessed by plotting the occurrence of a variable as a root variable versus the mean minimum node depth for the variable (Paluszynska and Biecek 2017), as well as by plotting the Gini index decrease versus accuracy decrease.

catalog link No associated catalog page

References

Evans, Jeffrey S., and Melanie A. Murphy. 2018. rfUtilities. https://cran.r-project.org/package=rfUtilities.
Friedman, Ariell, Oscar Pizarro, Stefan B. Williams, and Matthew Johnson-Roberson. 2012. Multi-Scale Measures of Rugosity, Slope and Aspect from Benthic Stereo Image Reconstructions.” PLoS ONE 7 (12). https://doi.org/10.1371/journal.pone.0050440.
Gamer, Matthias, Jim Lemon, and Ian Fellows Puspendra Singh. 2012. Irr: Various Coefficients of Interrater Reliability and Agreement. https://CRAN.R-project.org/package=irr.
Hiemstra, P. H., E. J. Pebesma, C. J. W. Twenh"ofel, and G. B. M. Heuvelink. 2008. Real-time automatic interpolation of ambient gamma dose rates from the Dutch Radioactivity Monitoring Network.” Computers & Geosciences. https://doi.org/10.1016/j.cageo.2008.10.011.
Hobson, R. D. 1972. Surface roughness in topography: quantitative approach. New York, New York: Harper; Row.
Kane, Joseph. 2007. Zooplankton abundance trends on Georges Bank, 1977-2004.” ICES Journal of Marine Science 64 (5): 909–19. https://doi.org/10.1093/icesjms/fsm066.
———. 2011. Multiyear variability of phytoplankton abundance in the Gulf of Maine.” ICES Journal of Marine Science 68 (9): 1833–41. https://doi.org/10.1093/icesjms/fsr122.
Kinlan, Brian P., Arliss J. Winship, Timothy P. White, and John Christensen. 2016. Modeling At-Sea Occurrence and Abundance of Marine Birds to Support Atlantic Marine Renewable Energy Planning Phase I Report.” https://www.boem.gov/ESPIS/5/5512.pdf.
Liaw, Andy, and Matthew Wiener. 2002. Classification and Regression by randomForest.” R News 2 (3): 18–22. https://CRAN.R-project.org/doc/Rnews/.
Maritorena, Stéphane, Odile Hembise Fanton D’Andon, Antoine Mangin, and David A Siegel. 2010. Merged satellite ocean color data products using a bio-optical model: Characteristics,benefits and issues.” Remote Sensing of Environment 114: 1791–1804. https://doi.org/10.1016/j.rse.2010.04.002.
Morse, R. E., K. D. Friedland, D. Tommasi, C. Stock, and J. Nye. 2017. Distinct zooplankton regime shift patterns across ecoregions of the U.S. Northeast continental shelf Large Marine Ecosystem.” Journal of Marine Systems 165: 77–91. https://doi.org/10.1016/j.jmarsys.2016.09.011.
Murphy, Melanie A., Jeffrey S. Evans, and Andrew Storfer. 2010. Quantifying Bufo boreas connectivity in Yellowstone National Park with landscape genetics.” Ecology 91 (1): 252–61. https://doi.org/10.1890/08-0879.1.
Paluszynska, Aleksandra, and Przemyslaw Biecek. 2017. randomForestExplainer: Explaining and Visualizing Random Forests in Terms of Variable Importance. https://CRAN.R-project.org/package=randomForestExplainer.
Sappington, J. M., Kathleen M. Longshore, and Daniel B. Thompson. 2007. Quantifying Landscape Ruggedness for Animal Habitat Analysis: A Case Study Using Bighorn Sheep in the Mojave Desert.” Journal of Wildlife Management 71 (5): 1419–26. https://doi.org/10.2193/2005-723.
Winship, A. J., B. P. Kinlan, T. P. White, J. B. Leirness, and J. Christensen. 2018. Modeling At-Sea Density of Marine Birds to Support Atlantic Marine Renewable Energy Planning: Final Report.” OCS Study BOEM 2018-010. https://coastalscience.noaa.gov/data_reports/modeling-at-sea-density-of-marine-birds-to-support-atlantic-marine-renewable-energy-planning-final-report/.