Animal Counting Toolkit : a practical guide to small-boat surveys for estimating abundance of coastal marine mammals

The authors thank Synchronicity Earth, Marisla Foundation, and the US Marine Mammal Commission for seed funding for this program.Small cetaceans (dolphins and porpoises) face serious anthropogenic threats in coastal habitats. These include bycatch in fisheries; exposure to noise, plastic and chemical pollution; disturbance from boaters; and climate change. Generating reliable abundance estimates is essential to assess sustainability of bycatch in fishing gear or any other form of anthropogenic removals and to design conservation and recovery plans for endangered species. Cetacean abundance estimates are lacking from many coastal waters of many developing countries. Lack of funding and training opportunities makes it difficult to fill in data gaps. Even if international funding were found for surveys in developing countries, building local capacity would be necessary to sustain efforts over time to detect trends and monitor biodiversity loss. Large-scale, shipboard surveys can cost tens of thousands of US dollars each day. We focus on methods to generate preliminary abundance estimates from low-cost, small-boat surveys that embrace a ‘training-while-doing’ approach to fill in data gaps while simultaneously building regional capacity for data collection. Our toolkit offers practical guidance on simple design and field data collection protocols that work with small boats and small budgets, but expect analysis to involve collaboration with a quantitative ecologist or statistician. Our audience includes independent scientists, government conservation agencies, NGOs and indigenous coastal communities, with a primary focus on fisheries bycatch. We apply our Animal Counting Toolkit to a small-boat survey in Canada’s Pacific coastal waters to illustrate the key steps in collecting line transect survey data used to estimate and monitor marine mammal abundance.Publisher PDFPeer reviewe

A guide to state-space modeling of ecological time series

State–space models (SSMs) are an important modeling framework for analyzing ecological time series. These hierarchical models are commonly used to model population dynamics, animal movement, and capture–recapture data, and are now increasingly being used to model other ecological processes. SSMs are popular because they are flexible and they model the natural variation in ecological processes separately from observation error. Their flexibility allows ecologists to model continuous, count, binary, and categorical data with linear or nonlinear processes that evolve in discrete or continuous time. Modeling the two sources of stochasticity separately allows researchers to differentiate between biological variation and imprecision in the sampling methodology, and generally provides better estimates of the ecological quantities of interest than if only one source of stochasticity is directly modeled. Since the introduction of SSMs, a broad range of fitting procedures have been proposed. However, the variety and complexity of these procedures can limit the ability of ecologists to formulate and fit their own SSMs. We provide the knowledge for ecologists to create SSMs that are robust to common, and often hidden, estimation problems, and the model selection and validation tools that can help them assess how well their models fit their data. We present a review of SSMs that will provide a strong foundation to ecologists interested in learning about SSMs, introduce new tools to veteran SSM users, and highlight promising research directions for statisticians interested in ecological applications. The review is accompanied by an in-depth tutorial that demonstrates how SSMs can be fitted and validated in R. Together, the review and tutorial present an introduction to SSMs that will help ecologists to formulate, fit, and validate their models

Marine mammal hotspots across the circumpolar Arctic

Aim: Identify hotspots and areas of high species richness for Arctic marine mammals. Location: Circumpolar Arctic. Methods: A total of 2115 biologging devices were deployed on marine mammals from 13 species in the Arctic from 2005 to 2019. Getis-Ord Gi* hotspots were calculated based on the number of individuals in grid cells for each species and for phyloge-netic groups (nine pinnipeds, three cetaceans, all species) and areas with high spe-cies richness were identified for summer (Jun-Nov), winter (Dec-May) and the entire year. Seasonal habitat differences among species’ hotspots were investigated using Principal Component Analysis. Results: Hotspots and areas with high species richness occurred within the Arctic continental-shelf seas and within the marginal ice zone, particularly in the “Arctic gateways” of the north Atlantic and Pacific oceans. Summer hotspots were generally found further north than winter hotspots, but there were exceptions to this pattern, including bowhead whales in the Greenland-Barents Seas and species with coastal distributions in Svalbard, Norway and East Greenland. Areas with high species rich-ness generally overlapped high-density hotspots. Large regional and seasonal dif-ferences in habitat features of hotspots were found among species but also within species from different regions. Gap analysis (discrepancy between hotspots and IUCN ranges) identified species and regions where more research is required. Main conclusions: This study identified important areas (and habitat types) for Arctic marine mammals using available biotelemetry data. The results herein serve as a benchmark to measure future distributional shifts. Expanded monitoring and teleme-try studies are needed on Arctic species to understand the impacts of climate change and concomitant ecosystem changes (synergistic effects of multiple stressors). While efforts should be made to fill knowledge gaps, including regional gaps and more com-plete sex and age coverage, hotspots identified herein can inform management ef-forts to mitigate the impacts of human activities and ecological changes, including creation of protected areas

Assessment of Competition between Fisheries and Steller Sea Lions in Alaska Based on Estimated Prey Biomass, Fisheries Removals and Predator Foraging Behaviour.

A leading hypothesis to explain the dramatic decline of Steller sea lions (Eumetopias jubatus) in western Alaska during the latter part of the 20th century is a change in prey availability due to commercial fisheries. We tested this hypothesis by exploring the relationships between sea lion population trends, fishery catches, and the prey biomass accessible to sea lions around 33 rookeries between 2000 and 2008. We focused on three commercially important species that have dominated the sea lion diet during the population decline: walleye pollock, Pacific cod and Atka mackerel. We estimated available prey biomass by removing fishery catches from predicted prey biomass distributions in the Aleutian Islands, Bering Sea and Gulf of Alaska; and modelled the likelihood of sea lions foraging at different distances from rookeries (accessibility) using satellite telemetry locations of tracked animals. We combined this accessibility model with the prey distributions to estimate the prey biomass accessible to sea lions by rookery. For each rookery, we compared sea lion population change to accessible prey biomass. Of 304 comparisons, we found 3 statistically significant relationships, all suggesting that sea lion populations increased with increasing prey accessibility. Given that the majority of comparisons showed no significant effect, it seems unlikely that the availability of pollock, cod or Atka mackerel was limiting sea lion populations in the 2000s

Average proportion of locations in each distance interval (in 1 nautical mile increments).

<p>Proportions are shown for (a) Steller sea lions older (n = 33) (y>10 = 0.2756e^-0.2639x) and younger (n = 56) (y<10 = 0.6757x-1.8506) than 10 months of age, (b) sea lions from the Aleutian Islands (n = 41) (yAI = 0.4964x-1.5478) and Gulf of Alaska (n = 48) (yGOA = 0.4591x-1.4068), and (c) male (n = 51) (ymale = 0.4512x-1.4515) and female (n = 38) (yfemale = 0.5367x-1) sea lions. Proportions are shown as mean ± s.e. Telemetry data were provided by Brian Fadely, NMFS.</p

Statistical model types and derivations used to test the relationship between sea lion population change and prey abundance (a and b); and between sea lion population change and fisheries catch (c and d).

<p>Multiple regression and linear mixed-effects models (LME) were used to test for relationships between different age groups of Steller sea lions (SSL), prey biomass distributions, catch, accessible distances and regions. Derivation of the number omodels (N) analysed with each different combination is shown.</p

Predicted prey biomass accessible to sea lions.

<p>Predicted biomass (1000s of tons in the absence of fishing) of (a) Atka mackerel, (b) Pacific cod and (c) walleye pollock accessible to Steller sea lions within 10, 20 and 50 km of the rookeries. Biomasses are averages for 2000/2002/2004 in the Aleutian Islands (AI) and 2001/2003 in the Gulf of Alaska (GOA). No data were available for Atka mackerel in the Bering Sea and Gulf of Alaska due to the small amounts predicted to occur in those regions.</p

Relationships between accessible prey biomass and sea lion population change in the Aleutian Islands.

<p>The relationships between predicted prey biomass accessible to Steller sea lions (a, b, c) using the reduced prey biomass (Scenario 3) and the annual rate of non-pup population change in the Aleutian Islands were significant for walleye pollock only (a), with western Aleutian rookeries (west, from rookeries 1–8, see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0123786#pone.0123786.g001" target="_blank">Fig 1</a>) showing a greater change with pollock biomass than eastern Aleutian rookeries (east, from rookeries 9–15, see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0123786#pone.0123786.g001" target="_blank">Fig 1</a>).</p

Annual biomass of pollock, cod and mackerel caught by fisheries.

<p>Average annual biomass (1000s of tons) of Atka mackerel (a,d), Pacific cod (b,e) and walleye pollock (c,f) commercially caught within 10 and 20 (d, e, f) and 50 and 100 (a, b, c) km of the rookeries from 2000–2004. There has been no directed fishery for mackerel in the Bering Sea and Gulf of Alaska since 1996.</p

Sea lion population change and the biomass of prey accessible to sea lions.

<p>The average numbers of Steller sea lions (age 1+ y), annual rate of sea lion population change and predicted biomass of groundfish accessible (calculated according to our accessibility model) to sea lions at each rookery or rookery cluster (shown with brackets): (a) Average non-pup population change and population size from 2000–2008, (b) average biomass of Atka mackerel accessible, (c) average biomass of Pacific cod accessible; and (d) average biomass of walleye pollock accessible. Biomasses averages are for 2000/2002/2004 in the Aleutian Islands (AI) and 2001/2003 in the Gulf of Alaska (GOA). Mackerel surveys are not conducted in the Bering Sea and Gulf of Alaska as the species’ distribution is limited in those regions.</p