Chapter 9 : Hierarchical modeling with environmental covariates: Marine Mammals and Turtles

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1 Chapter 9 : Hierarchical modeling with environmental covariates: Marine Mammals and Turtles Logan Pallin Duke University Introduction The conservation and management of large marine vertebrates requires reliable information on distribution, abundance, and trends in habitat usage. Quantitative research on marine species is important for numerous, yet sometimes conflicting reasons. One prime example in the North East United States includes the abundance of grey seals and their interaction with the cod fisheries. Marine mammals and sea turtles are often of management and conservation concern as their large home ranges and habitat requirements often overlap and conflict with human interests and activities such as offshore development and commercial fishing (Trites et al. 1997). Cetaceans (whales, dolphins, and porpoises) and sea turtles, with their vast ranges, represent a particular challenge for population monitoring (Berggren et al. 2002). Turtles and some cetaceans spend small fractions of their time at the surface, and even then, they exhibit cryptic behaviors resulting in only small portions of the animals bodies being visible. Given the difficulties associated with estimating animal abundance (or occurrence) based on count data from large-scale surveys (Royle et al. 2007), modeling spatial and temporal distributions of animals can help to determine areas of high and low use and inform decisions for development (Garthe and Hüppop 2004, Kinlan et al. 2012). However, distributions of animals in the offshore environment can be highly variable, and are driven by environmental and biophysical factors working at a variety of temporal and spatial scales (O Connell et al. 2009, Zipkin et al. 2010). By combining our boat and aerial survey data with oceanographic habitat data as well as anthropogenic influences we can use hierarchical spatialtemporal modeling methods to estimate these influences on the distributions and relative abundances of a species of interest. Accurately assessing such relationships is essential for predicting spatial distributions and the potential shifts that could occur in these geographic distributions. We collected line-transect survey data on cetaceans and sea turtles from both boat and aerial surveys. Our primary goal is to quantify sea turtle and marine mammal densities and abundance seasonally throughout the study region, and to develop hierarchical models to examine spatial patterns and trends based on interactions with environmental conditions. Our modeling effort will help identify species at potential risk from turbine construction and operation due to their movements, behavior, or migration strategies. To date, we have begun analysis of boat survey and environmental covariate data and plan on developing models incorporating aerial survey data by April Part III: 9-1

2 Methods Survey Methods Standardized boat-based surveys are a widely used method of obtaining density data for birds, sea turtles, and marine mammals. In our boat-based surveys, transects extend perpendicularly to the coastline, from three nautical miles offshore to the 30 m isobath or the eastern extent of the mid- Atlantic Wind Energy Areas (WEAs), whichever is furthest. Boat transects are spaced 10 km apart and extend at least one transect north and south of each WEA. We conducted eight surveys per year on a scheduled basis as the weather allows, from April 2012 April Cetaceans were photographed when possible. Photos were submitted for individual identification using the established North Atlantic Fin Whale, Humpback and North Atlantic Right Whale catalogues. Surveys were conducted in passing mode, meaning that the boat stays on transect and at constant survey speed (10 knots) except when complying with National Marine Fisheries Service (NMFS) rules about approaching marine mammals, including rules regarding vessel speed and encounters with endangered North Atlantic Right Whales (Eubalaena glacialis). More detailed methods for boat surveys are found in Chapter 5. Data Preparation Boat survey observations of marine mammals and turtles were compiled, and are summarized below in Table 9-1. Delphinid observations were grouped by season at the taxa level, and exploratory preparations are still underway for whale and sea turtle observations. Effort and species observation data were modeled using the count method (Hedley et al. 1999). Boat survey track lines were divided into segments approximately 5 kilometers (km) in length. Start and end locations of these segments were calculated using the COGO proportions function in ArcMap The location of the midpoint of each segment (latitude and longitude) was calculated using the feature to point command in ArcMap Sea surface temperature (SST) and chlorophyll a (ChlA), were calculated using the Marine Geospatial Ecology Toolbox (MGET) data products function provided by Dr. Patrick Halpin s lab at Duke University. SST and ChlA data were extracted at a monthly average, for all twelve months, with 4km spatial resolution. The monthly averages were then averaged by season for the seasonal comparisons. A three second resolution bathymetric coastal relief model was extracted from the NOAA geophysical data center and depth was calculated in MGET s spatial analyst interpolate raster values at points tool using a digital bathymetric database for the study area. Additionally, the distance from each segment s midpoint to the nearest coastline (distance from shore, or DFS) was calculated using ArcMap s near (spatial analyst) tool. Habitat modeling for predicting unknown individuals In order to aid in the identification of individuals that were not identifiable in the field, we are in the process of developing habitat models for dolphins and sea turtles that were identified during the boat surveys. Whales will not be included in this analysis due to the low numbers of animals sighted. In order to achieve this, a larger data set was created of known individuals and their habitat preference using the OBIS Seamap dataset (Halpin et al. 2013). Only sighting data from the last 8 years was used in order to avoid any environmental changes that our field site may have experienced due to climate change. Part III: 9-2

3 Distributions of habitat selection (SST, ChlA, depth, and DFS) were developed by species and unidentified individuals. Covariates were extracted from ArcGIS using the MGET data products toolbox. For this comparison the date-time stamp of the sighting was used to link an 8-day average with 4km resolution for both SST and ChlA. The data were then grouped together by taxa and a k-means analysis will be calculated in order to infer a 95% confidence in the identity of unidentified individuals and groups. Leatherback sea turtles were not included in the k-means seasonal analyses for sea turtles as they are more visibly distinct than their relatively hard-shelled counterparts. Modeling detection probability Exploratory analysis of the boat sighting data was conducted in the program Distance. Detection probability for each taxa (Delphinidae, Mysticeti, and Chelonioidea) was modeled as a smooth function of perpendicular distance (x). Candidate forms for the detection function were the half-normal models with a cosine smoothing term (Buckland et al. 2001). Sea state was included as a candidate covariate, and models were selected using Akaike s Information Criterion (AIC; Akaike 1973). Building descriptive models: The covariates for each 5km boat transect midpoint and predicted density from Distance were extracted and input into R for model fitting. The output GAMs were a result of different combinations of covariates. The selection of the best model was based according to their AIC score. SST and ChlA seasonal climatologies were created using the MGET toolbox in ArcGIS. Monthly climatologies were created at 4 km resolution and then averaged by season. A 4km square gridded data set was created for each season, containing a value in every grid cell for each explanatory variable in the model. The seasonal prediction grids were passed to the chosen descriptive model for dolphins and sea turtles using the predict command in R. The output of the model was an estimate of the predicted density in each grid cell, based on each cell s sea surface temperature, chlorophyll a, distance offshore, and depth. This predicted density was then smoothed using a kriging spatial analyst smoothing function provided in ArcMap Descriptive model development is still being conducted. Results A total of 388 marine mammal and sea turtle sightings were reported in boat surveys, representing1,374 individuals. Of these, 1,215 individuals were identified to the species level (Table 9-1). Of these individuals, 1,202 were dolphins, 35 were whales, and 117 were sea turtles (Table 9-1). Locations of whales, dolphins, and sea turtles are presented in Figure 9-1Figure 9-3. Observations per hour were highest in June and July surveys (Table 9-2). Humpback Whales were the most common large whale observed, and five species were observed overall (Figure 9-1). Bottlenose Dolphins were the most common cetacean species observed, and they had a fairly inshore distribution across the study area Figure 9-2. Common Dolphins were the next most abundant species, and their distribution was more offshore. Sea turtle distributions were more offshore in the southern half of the study area, and Loggerhead Turtles were the most abundant (Figure 9-3). Model refinements and analyses are ongoing. Part III: 9-3

4 Literature Cited Akaike, H. (1973). Maximum likelihood identification of Gaussian autoregressive moving average models. Biometrika, 60(2), Garthe, S., & Hüppop, O. (2004). Scaling possible adverse effects of marine wind farms on seabirds: developing and applying a vulnerability index. Journal of Applied Ecology, 41(4), Halpin, P. N., Read, A. J., Best, B. D., Hyrenbach, K. D., Fujioka, E., Coyne, M. S.,... & Spoerri, C. (2006). OBIS-SEAMAP: developing a biogeographic research data commons for the ecological studies of marine mammals, seabirds, and sea turtles. Marine Ecology Progress Series, 316(23), 246. Hammond, P. S., Berggren, P., Benke, H., Borchers, D. L., Collet, A., Heide Jørgensen, M. P.,... & Øien, N. (2002). Abundance of harbour porpoise and other cetaceans in the North Sea and adjacent waters. Journal of Applied Ecology, 39(2), Roberts JJ, Best BD, Dunn DC, Treml EA, Halpin PN (2010) Marine Geospatial Ecology Tools: An integrated framework for ecological geoprocessing with ArcGIS, Python, R, MATLAB, and C++. Environmental Modelling & Software 25: doi: /j.envsoft Royle, J. A., Kéry, M., Gautier, R., & Schmid, H. (2007). Hierarchical spatial models of abundance and occurrence from imperfect survey data. Ecological Monographs, 77(3), Thomas, L., Buckland, S. T., Rexstad, E. A., Laake, J. L., Strindberg, S., Hedley, S. L.,... & Burnham, K. P. (2010). Distance software: design and analysis of distance sampling surveys for estimating population size. Journal of Applied Ecology, 47(1), Trites, A. W., Christensen, V., & Pauly, D. (1997). Competition between fisheries and marine mammals for prey and primary production in the Pacific Ocean. Journal of Northwest Atlantic Fishery Science, 22, Zipkin, E. F., Andrew Royle, J., Dawson, D. K., & Bates, S. (2010). Multi-species occurrence models to evaluate the effects of conservation and management actions. Biological Conservation, 143(2), Part III: 9-4

5 Figures and Tables Figure 9-1. Whale sightings from boat survey transects. Part III: 9-5

6 Figure 9-2. Delphinid sightings from boat survey transects. Part III: 9-6

7 Figure 9-3. Sea turtle sightings from boat survey transects. Part III: 9-7

8 Table 9-1. Preliminary summary data for boat surveys by season. Spring: March - May; Summer: June - August; Fall: September - November; Winter: December - February. Species Group Spring Summer Fall Winter Total Count (Ind.) Tursiops truncates (Bottlenose) Delphinus delphis (Common) Stenella frontalis (Spotted) Unidentified Delphinid Dolphins Total Balaenoptera physalus (Fin) Balaenoptera borealis (Sei) Balaenoptera acutorostrata (Minke) Eubalaena glacialis (Right) Megaptera noveangliae (Humpback) Unidentified Whale Whales Total Caretta careta (Loggerhead) Dermochels coriacea (Leatherback) Unidentified Sea Turtle Sea Turtles Total Percent of Total by Season: Grand Total ,374 Table 9-2. Analysis of on-effort correlation with number of individual marine mammal and sea turtle sightings by month. Month Jan Feb Mar Apr May Jun July Aug Sept Oct Nov Dec Total Avg Number of Sightings On-Effort Hours # Sightings per Hour Part III: 9-8