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<p>Human use of the oceans is increasingly in conflict with conservation of endangered
species. Methods for managing the spatial and temporal placement of industries such
as military, fishing, transportation and offshore energy, have historically been post
hoc; i.e. the time and place of human activity is often already determined before
assessment of environmental impacts. In this dissertation, I build robust species
distribution models in two case study areas, US Atlantic (Best et al. 2012) and British
Columbia (Best et al. 2015), predicting presence and abundance respectively, from
scientific surveys. These models are then applied to novel decision frameworks for
preemptively suggesting optimal placement of human activities in space and time to
minimize ecological impacts: siting for offshore wind energy development, and routing
ships to minimize risk of striking whales. Both decision frameworks relate the tradeoff
between conservation risk and industry profit with synchronized variable and map views
as online spatial decision support systems.</p><p>For siting offshore wind energy
development (OWED) in the U.S. Atlantic (chapter 4), bird density maps are combined
across species with weights of OWED sensitivity to collision and displacement and
10 km2 sites are compared against OWED profitability based on average annual wind
speed at 90m hub heights and distance to transmission grid. A spatial decision support
system enables toggling between the map and tradeoff plot views by site. A selected
site can be inspected for sensitivity to a cetaceans throughout the year, so as to
capture months of the year which minimize episodic impacts of pre-operational activities
such as seismic airgun surveying and pile driving.</p><p>Routing ships to avoid whale
strikes (chapter 5) can be similarly viewed as a tradeoff, but is a different problem
spatially. A cumulative cost surface is generated from density surface maps and conservation
status of cetaceans, before applying as a resistance surface to calculate least-cost
routes between start and end locations, i.e. ports and entrance locations to study
areas. Varying a multiplier to the cost surface enables calculation of multiple routes
with different costs to conservation of cetaceans versus cost to transportation industry,
measured as distance. Similar to the siting chapter, a spatial decisions support system
enables toggling between the map and tradeoff plot view of proposed routes. The user
can also input arbitrary start and end locations to calculate the tradeoff on the
fly.</p><p>Essential to the input of these decision frameworks are distributions of
the species. The two preceding chapters comprise species distribution models from
two case study areas, U.S. Atlantic (chapter 2) and British Columbia (chapter 3),
predicting presence and density, respectively. Although density is preferred to estimate
potential biological removal, per Marine Mammal Protection Act requirements in the
U.S., all the necessary parameters, especially distance and angle of observation,
are less readily available across publicly mined datasets.</p><p>In the case of predicting
cetacean presence in the U.S. Atlantic (chapter 2), I extracted datasets from the
online OBIS-SEAMAP geo-database, and integrated scientific surveys conducted by ship
(n=36) and aircraft (n=16), weighting a Generalized Additive Model by minutes surveyed
within space-time grid cells to harmonize effort between the two survey platforms.
For each of 16 cetacean species guilds, I predicted the probability of occurrence
from static environmental variables (water depth, distance to shore, distance to continental
shelf break) and time-varying conditions (monthly sea-surface temperature). To generate
maps of presence vs. absence, Receiver Operator Characteristic (ROC) curves were used
to define the optimal threshold that minimizes false positive and false negative error
rates. I integrated model outputs, including tables (species in guilds, input surveys)
and plots (fit of environmental variables, ROC curve), into an online spatial decision
support system, allowing for easy navigation of models by taxon, region, season, and
data provider.</p><p>For predicting cetacean density within the inner waters of British
Columbia (chapter 3), I calculated density from systematic, line-transect marine mammal
surveys over multiple years and seasons (summer 2004, 2005, 2008, and spring/autumn
2007) conducted by Raincoast Conservation Foundation. Abundance estimates were calculated
using two different methods: Conventional Distance Sampling (CDS) and Density Surface
Modelling (DSM). CDS generates a single density estimate for each stratum, whereas
DSM explicitly models spatial variation and offers potential for greater precision
by incorporating environmental predictors. Although DSM yields a more relevant product
for the purposes of marine spatial planning, CDS has proven to be useful in cases
where there are fewer observations available for seasonal and inter-annual comparison,
particularly for the scarcely observed elephant seal. Abundance estimates are provided
on a stratum-specific basis. Steller sea lions and harbour seals are further differentiated
by ‘hauled out’ and ‘in water’. This analysis updates previous estimates (Williams
& Thomas 2007) by including additional years of effort, providing greater spatial
precision with the DSM method over CDS, novel reporting for spring and autumn seasons
(rather than summer alone), and providing new abundance estimates for Steller sea
lion and northern elephant seal. In addition to providing a baseline of marine mammal
abundance and distribution, against which future changes can be compared, this information
offers the opportunity to assess the risks posed to marine mammals by existing and
emerging threats, such as fisheries bycatch, ship strikes, and increased oil spill
and ocean noise issues associated with increases of container ship and oil tanker
traffic in British Columbia’s continental shelf waters.</p><p>Starting with marine
animal observations at specific coordinates and times, I combine these data with environmental
data, often satellite derived, to produce seascape predictions generalizable in space
and time. These habitat-based models enable prediction of encounter rates and, in
the case of density surface models, abundance that can then be applied to management
scenarios. Specific human activities, OWED and shipping, are then compared within
a tradeoff decision support framework, enabling interchangeable map and tradeoff plot
views. These products make complex processes transparent for gaming conservation,
industry and stakeholders towards optimal marine spatial management, fundamental to
the tenets of marine spatial planning, ecosystem-based management and dynamic ocean
management.</p>
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