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Daniel Linden

Publications and source records attributed to Daniel Linden.

2 recordsLinked to original sources

Update to a management-focused population viability analysis for North Atlantic right whales

We provide an update to the recently published population viability analysis for North Atlantic right whales ( Eubalaena glacialis ). The update includes improvements to the reproduction modeling and also shares additional context given evidence of reduced mortality indicated by recent population monitoring. Projections from the analysis are used to quantify simulated population sizes across 100 years and resulting quasi-extinction probabilities (falling below 50 mature females that have proven ability to reproduce) to compare hypothetical scenarios related to management of threats and changing environmental conditions. Under a status quo scenario reflecting conditions of 2019, prior to the enactment of new regulations by the U.S. and Canada, the North Atlantic right whale population would be expected to continue to fall, with a median decline of 88 percent (95% projection interval, –98 percent to –45 percent change) and a probability of falling below 50 proven females (i.e., quasi-extinction) of 0.988 at 100 years. In hypothetical scenarios that fully remove each of the three primary threats to right whales one at a time, removal of the entanglement threat alone reduces the probability of falling below 50 proven females in 100 years to 0.070; removal of the vessel strike threat alone reduces it to 0.522; and a return to historical prey abundance patterns (pre-2010), but with both human-related threats still in place, reduces it to 0.524. Although additional baseline scenarios were explored to examine the potential effects of recent regulations, we found that the most up-to-date mortality rates (2020–2022) are similar to those simulated under a 70% reduction in severe entanglement injury compared to rates estimated during 2013–2019. If management measures implemented in the U.S. and Canada continue to reduce mortality, the estimated probability of falling below 50 proven females in 100 years is 0.234. Our model continues to provide a tool for assessing North Atlantic right whale recovery.

NOAA Technical Memorandum

Accurate recapture identification for genetic mark–recapture studies with error-tolerant likelihood-based match calling and sample clustering

Error-tolerant likelihood-based match calling presents a promising technique to accurately identify recapture events in genetic mark–recapture studies by combining probabilities of latent genotypes and probabilities of observed genotypes, which may contain genotyping errors. Combined with clustering algorithms to group samples into sets of recaptures based upon pairwise match calls, these tools can be used to reconstruct accurate capture histories for mark–recapture modelling. Here, we assess the performance of a recently introduced error-tolerant likelihood-based match-calling model and sample clustering algorithm for genetic mark–recapture studies. We assessed both biallelic (i.e. single nucleotide polymorphisms; SNP) and multiallelic (i.e. microsatellite; MSAT) markers using a combination of simulation analyses and case study data on Pacific walrus ( Odobenus rosmarus divergens ) and fishers ( Pekania pennanti ). A novel two-stage clustering approach is demonstrated for genetic mark–recapture applications. First, repeat captures within a sampling occasion are identified. Subsequently, recaptures across sampling occasions are identified. The likelihood-based matching protocol performed well in simulation trials, demonstrating utility for use in a wide range of genetic mark–recapture studies. Moderately sized SNP (64+) and MSAT (10–15) panels produced accurate match calls for recaptures and accurate non-match calls for samples from closely related individuals in the face of low to moderate genotyping error. Furthermore, matching performance remained stable or increased as the number of genetic markers increased, genotyping error notwithstanding.

Royal Society Open Science