Estimating Cause-Specific Mortality Rates Using Recovered Carcasses

Joly, Damien O.; Heisey, Dennis M.; Samuel, Michael D.; Ribic, Christine A.; Thomas, Nancy J.; Wright, Scott D.; Wright, Irene E.

doi:10.7589/0090-3558-45.1.122

Cited by 10 publications

(7 citation statements)

References 14 publications

(20 reference statements)

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“…We restricted

n_{i k}

to be an integer at least as big as the observed number of carcasses,

R_{i k} + V_{i k}

. The probability parameter,

c_{i} \times m_{i k}

, was the cause‐specific collared mortality probability with

c_{i}

as the total collared mortality probability (non‐cause‐specific) and

m_{i k}

as the proportion of total collared mortality due to cause k (Joly et al ). The population size,

N_{i}

, was drawn from a Poisson distribution with its mean and variance parameter equal to the population count in year i ,

N o b s_{i}

N_{i} \sim P o i s s o n (N o b s_{i}) .

…”

Section: Methodsmentioning

confidence: 99%

“…Carcass recovery data often provide known numbers of individuals that die from various causes and biological samples (e.g., teeth, jaw, reproductive tracts, hair, and tissue samples) for population modeling, diet analysis, and disease assessment (Roffe and Work ). Additionally, long‐term records from recovered carcasses reflect changes in prominent mortality risks through time if detection and reporting biases are estimated and incorporated (Joly et al , Huso , Stevens and Dennis , Korner‐Nievergelt et al ). Translation of counts of carcasses to rigorous estimates of cause of mortality patterns and detection probability of carcasses has been pioneered by researchers studying the effect of wind turbines and other structures on bird and bat populations (Peron et al , Huso and Dalthorp , Korner‐Nievergelt et al ).…”

mentioning

confidence: 99%

“…These data lead to information on mortality rate and the cause‐specific allocation of that rate (Heisey and Fuller ). However, data on the number of mortalities due to a particular cause do not need to come from a radio‐collared or marked sample, and instead can come from any sample of recovered carcasses (Joly et al ). A long‐term dataset of recovered carcasses alone can be used to understand spatial, temporal, and demographic patterns in mortality causes.…”

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confidence: 99%

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Mortality patterns and detection bias from carcass data: An example from wolf recovery in Wisconsin

et al. 2015

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We developed models and provide computer code to make carcass recovery data more useful to wildlife managers. With these tools, wildlife managers can understand the spatial, temporal (e.g., across time periods, seasons), and demographic patterns in mortality causes from carcass recovery datasets. From datasets of radio-collared and non-collared carcasses, managers can calculate the detection bias by mortality cause in a non-collared carcass dataset compared to a collared carcass dataset. As a first step, we provide a standard procedure to assign mortality causes to carcasses. We provide an example of these methods for radio-collared wolves (n ¼ 208) and non-collared wolves (n ¼ 668) found dead in Wisconsin (1979Wisconsin ( -2012. We analyzed differences in mortality cause relative to season, age and sex classes, wolf harvest zones, and recovery phase (1979-1995: initial recovery, 1996-2002: early growth, 2003-2012: late growth). Seasonally, illegal kills and natural deaths were proportionally higher in winter (Oct-Mar) than summer (Apr-Sep) for collared wolves, whereas vehicle strikes and legal kills were higher in summer than winter. Spatially, more illegally killed collared wolves occurred in eastern wolf harvest zones where wolves reestablished more slowly and in the central forest region where optimal habitat is isolated by agriculture. Natural mortalities of collared wolves (e.g., disease, intraspecific strife, or starvation) were highest in western wolf harvest zones where wolves established earlier and existed at higher densities. Calculating detection bias in the non-collared dataset revealed that more than half of the non-collared carcasses on the landscape are not found. The lowest detection probabilities for non-collared carcasses (0.113-0.176) occurred in winter for natural, illegal, and unknown mortality causes. Published 2015. This article is a U.S. Government work and is in the public domain in the USA.

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“…We restricted

n_{i k}

to be an integer at least as big as the observed number of carcasses,

R_{i k} + V_{i k}

. The probability parameter,

c_{i} \times m_{i k}

, was the cause‐specific collared mortality probability with

c_{i}

as the total collared mortality probability (non‐cause‐specific) and

m_{i k}

as the proportion of total collared mortality due to cause k (Joly et al ). The population size,

N_{i}

, was drawn from a Poisson distribution with its mean and variance parameter equal to the population count in year i ,

N o b s_{i}

N_{i} \sim P o i s s o n (N o b s_{i}) .

…”

Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Mortality patterns and detection bias from carcass data: An example from wolf recovery in Wisconsin

et al. 2015

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“…This is particularly true for cetaceans (Gaydos et al , 2004; Buck et al , 2006), mostly because sampling from large, migratory, water‐bound mammals has obvious logistical, welfare and cost implications. Owing to these constraints, the majority of data available on cetacean parasites, pathogens and disease comes from captive‐based studies or from stranded animals (Dunn, Buck & Robeck, 2001; Joly et al , 2009), which cannot be considered representative of the ‘normal’ population (Wobeser, 1994).…”

Section: Introductionmentioning

confidence: 99%

A novel non‐invasive tool for disease surveillance of free‐ranging whales and its relevance to conservation programs

Acevedo‐Whitehouse¹,

Rocha-Gosselin²,

Gendron³

2010

Animal Conservation

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The numbers of potentially pathogenic microorganisms that have been isolated from stranded cetaceans in the last three decades underscore the urgent need for methods of detection of microorganisms that might cause significant disease and increase the likelihood of population declines. We have designed and implemented two non-invasive techniques for the collection of exhaled breath condensate (blow) from free-ranging whales and demonstrated their suitability for the detection of respiratory bacteria. We successfully collected 22 individual blow samples from eight cetacean species. Using well-established molecular techniques we detected three bacterial genera (Haemophilus, Streptococcus and Staphylococcus). Haemophilus spp. was detected in fin whale Balaenoptera physalus, sperm whale Physeter macrocephalus, humpback whale Megaptera novaeangliae and gray whale Eschrichtius robustus blows, while unidentified b-hemolytic streptococci and Staphylococcus aureus were detected in gray whale and blue whale Balaenoptera musculus blows. The detection limit of the test was determined as 1 CFU mL À1 . None of the identified bacteria were found in environmental (control) samples, suggesting that their presence in the blows was genuine and not due to inadvertent contamination. While the population-level relevance of these bacteria is as yet unclear and it is possible that they are commensal microorganisms, S. aureus has been identified previously as a high-risk pathogen to cetacean health, and streptococci have increasingly been associated with cetacean mortality events. We suggest that future cetacean monitoring programs of vulnerable or threatened species include blow sampling as a means to determine the prevalence of the respiratory bacteria in the populations and monitor spatiotemporal fluctuations as indicators of changes in cetacean health.

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“…24 As hard as it is to obtain actuarial estimates in domestic animals, assessment of disease and cause-specific mor-tality rates in wild animal populations is even more challenging. 25 The available data suggest that animals in the wild have variable incidence rates of specific cancers, although mortality is often due to loss of fitness, competition, predation, accidents, or human-related causes. 26 It is also clear that the overall rate of cancer among species in captivity is variable, 27 but none are as high as those seen in domestic dogs and cats.…”

Section: Introduction: the Evolutionary Legacy Of Cancermentioning

confidence: 99%

Increased risk of cancer in dogs and humans: A consequence of recent extension of lifespan beyond evolutionarily determined limitations?

et al. 2022

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Cancer is among the most common causes of death for dogs (and cats) and humans in the developed world, even though it is uncommon in wildlife and other domestic animals. We provide a rationale for this observation based on recent advances in our understanding of the evolutionary basis of cancer. Over the course of evolutionary time, species have acquired and fine-tuned adaptive cancer-protective mechanisms that are intrinsically related to their energy demands, reproductive strategies, and expected lifespan. These cancer-protective mechanisms are general across species and/or specific to each species and their niche, and they do not seem to be limited in diversity. The evolutionarily acquired cancer-free longevity that defines a species' life history can explain why the relative cancer risk, rate, and incidence are largely similar across most species in the animal kingdom despite differences in body size and life expectancy. The molecular, cellular, and metabolic events that promote malignant transformation and cancerous growth can overcome these adaptive, species-specific protective mechanisms in a small proportion of individuals, while independently, some individuals in the population might achieve exceptional longevity. In dogs and humans, recent dramatic alterations in healthcare and social structures have allowed increasing numbers of individuals in both species to far exceed their species-adapted longevities (by two to four times) without allowing the time necessary for compensatory natural selection. In other words, the cancer-protectiveThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Estimating Cause-Specific Mortality Rates Using Recovered Carcasses

Cited by 10 publications

References 14 publications

Mortality patterns and detection bias from carcass data: An example from wolf recovery in Wisconsin

Mortality patterns and detection bias from carcass data: An example from wolf recovery in Wisconsin

A novel non‐invasive tool for disease surveillance of free‐ranging whales and its relevance to conservation programs

Increased risk of cancer in dogs and humans: A consequence of recent extension of lifespan beyond evolutionarily determined limitations?

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