Presentation on theme: "Erin Meyer-Gutbrod - Cornell University Dr. Andrew Pershing – Gulf of Maine Research Institute Dr. Charles Greene - Cornell University"— Presentation transcript:
Erin Meyer-Gutbrod - Cornell University Dr. Andrew Pershing – Gulf of Maine Research Institute Dr. Charles Greene - Cornell University
Right Whale Feeding and Breeding Zones RWs spend Spring, Summer and Fall feeding in the Gulf of Maine and off the Scotian Shelf Pregnant cows migrate to waters off Georgia and Florida to calve during the winter months Adapted from E. Paul Oberlander, Woods Hole Oceanographic Institution Graphics Critically endangered: fewer than 500 individuals
Chief Prey Group: Calanus finmarchicus Abundance levels respond to climate forcing from the Arctic and within the North Atlantic basin, and are correlated with the NAO (Greene et al.) Due to the high energetic demands of pregnancy and nursing, right whale vulnerabilities to prey limitations are most likely to be manifest in the reproduction cycle
Continuous Plankton Recorder Data collected from silk screens dragged behind ships of opportunity between Boston and Cape Sable, Nova Scotia CPR data sorted by region and by bimonthly time period to create a high resolution, long term data set
Calving rates driven by prey abundance High calving rates in 1980s and 2000s driven by high copepod abundances Low rates in 1990s driven by fewer copepods
3-Stage Reproduction Model A calving cycle takes at least 3 years: 1 year in each stage 2 Probabilities must be computed: 1. Transition between Recovery and Pregnancy Φ Transition between Pregnancy and Nursing a calf Φ 32
Calculating Transition Probabilities Probabilities ϕ 21 and ϕ 32 are modeled as logistic regressions to constrain them between 0 and 1 The logit is a linear combination of an intercept and a suite of prey terms which are multiplied by coefficients In the prey-independent model, the logit will only contain an intercept
Projecting Right Whale Population Insert transition probabilities into a demographic population matrix: Multiply the population matrix by a vector of individuals categorized into reproductive stages to determine the distribution of individuals in the following time step:
Model Optimization This is a big job. Using R and AD Model Builder we test each of the possible CPR data sets by season and geographical region. Look for combinations of CPR data sets that yield accurate calving series. Find the set of parameters (intercepts and coefficients inside the two logits) that best fit the model to the observed time series of calf births.
3 Calving Models: 1. Independent model with no prey dependency – transition probabilities are constant through time 2. Prey-dependent model using only yearly average values from the Continuous Plankton Recorder 3. Prey-dependent model using bimonthly values from the Continuous Plankton Recorder
Parameters of the 3 models
Comparing 2 calving models: a simple independent model and a prey-dependent model Prey-independent model follows general trend of increased calving Prey-dependent model captures the dips and spikes in reproduction that are caused by environmental conditions
Viable cow distribution Total number of viable cows increases steadily Cows spend more time in the “resting” stage After a period of low prey abundance, cows wait in the “resting” stage until a year of high prey abundance leads to a spike in pregnancies.
Given average prey availability, cows are very likely to get pregnant Great declines in calf production are explained by the connection between low prey abundance and low pregnancy rates Chance of successful delivery is much lower than chance of conception given any set of prey conditions This may result from the increased nutritional requirements for pregnant cows.
What’s next? Fit prey-driven reproduction model separately for each decade to look for different patterns between the three decadal regimes Choose a general model that is robust enough to withstand regime shifts Reproductive model can be nested into a complete demographic model to project future population growth This model can be used to project right whale extinction risk under varying prey conditions related to climate change and / or varying rates of anthropogenic mortalities
Thanks to: Cornell University National Defense Science and Engineering Graduate fellowship Office of Naval Research Atkinson Center for a Sustainable Future Gulf of Maine Research Institute New England Aquarium