Hidden Process Models For Animal Population Dynamics

Newman, Ken; Buckland, Stephen T.; Lindley, Steven T.; Thomas, Len; Fernández, Carmen

doi:10.1890/04-0592

Cited by 119 publications

(108 citation statements)

References 20 publications

(26 reference statements)

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“…Simulations that contained both process and observation error were used to assess the validity of this assumption. We found that the methodology was robust to violations of the assumption of no observation error (see SI Text and Table S1 for details), thus obviating the need for a more sophisticated approach of fitting a model with both types of noise (33,34). Further studies are needed to assess the robustness of our findings to more realistic stochasticity and observation error (36).…”

Section: Methodsmentioning

confidence: 82%

Host–pathogen time series data in wildlife support a transmission function between density and frequency dependence

Smith

Telfer

Kallio

et al. 2009

Proc. Natl. Acad. Sci. U.S.A.

130

194

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A key aim in epidemiology is to understand how pathogens spread within their host populations. Central to this is an elucidation of a pathogen's transmission dynamics. Mathematical models have generally assumed that either contact rate between hosts is linearly related to host density (density-dependent) or that contact rate is independent of density (frequency-dependent), but attempts to confirm either these or alternative transmission functions have been rare. Here, we fit infection equations to 6 years of data on cowpox virus infection (a zoonotic pathogen) for 4 natural populations to investigate which of these transmission functions is best supported by the data. We utilize a simple reformulation of the traditional transmission equations that greatly aids the estimation of the relationship between density and host contact rate. Our results provide support for an infection rate that is a saturating function of host density. Moreover, we find strong support for seasonality in both the transmission coefficient and the relationship between host contact rate and host density, probably reflecting seasonal variations in social behavior and/or host susceptibility to infection. We find, too, that the identification of an appropriate loss term is a key component in inferring the transmission mechanism. Our study illustrates how time series data of the hostpathogen dynamics, especially of the number of susceptible individuals, can greatly facilitate the fitting of mechanistic disease models.cowpox ͉ disease ͉ population cycles ͉ Markov chain Monte Carlo T he seminal studies of Anderson and May (1, 2) introduced a framework for modeling the dynamics of pathogens and their hosts that has since underpinned most predictive models of host-pathogen dynamics. It has been standard practice when modeling the dynamics of host-microparasite interactions (viral and bacterial infections) to represent the rate of change of infected hosts I(t) at time t by dI͑t͒ dt ϭ transmission rate ͑infection͒ Ϫ loss rate ͑death ϩ recovery͒.[1]However, empirically based identification of appropriate functional forms for the ''transmission rate'' and ''loss rate'' terms has not generally been possible for systems with host dynamics because of a lack of sufficient data (although refs. 3 and 4 have recently done this for infectious diseases of human populations).To date, most studies have used transmission rate terms that are either density-dependent or frequency-dependent (5, 6). The underlying difference between these is the assumption about how host contact rate c, varies with host density [(N(t))/A], where N(t) is host abundance and A, the area occupied by the population, is usually assumed constant and omitted from the equations (6). For density-dependent transmission, host contact rate varies linearly with density [typically adopted for directly transmitted diseases such as measles (7) and foot and mouth disease (8)], whereas for frequencydependent transmission it is constant [typically adopted for sexually transmitted diseases such as HIV in ...

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Section: Methodsmentioning

confidence: 82%

Host–pathogen time series data in wildlife support a transmission function between density and frequency dependence

Smith

Telfer

Kallio

et al. 2009

Proc. Natl. Acad. Sci. U.S.A.

130

194

View full text Add to dashboard Cite

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“…The full model could be either empirical, in which case it can be fitted using autoregressive techniques (Forchhammer et al 2002), or it could incorporate detailed population dynamic mechanisms using 'hidden process' modelling (Newman et al 2006), which, by modelling the demographic processes explicitly, allows separation of the different sources of uncertainty. If these hidden processes can be expressed in a linear form, the models can be fitted using classical statistical techniques; more complex, non-linear models will require computer-intensive Bayesian methods (Buckland et al 2007), although these are becoming increasingly available.…”

Section: Knowledge Gapsmentioning

confidence: 99%

Travelling through a warming world: climate change and migratory species

Robinson¹,

Crick²,

Learmonth³

et al. 2009

Endang. Species. Res.

343

301

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Long-distance migrations are among the wonders of the natural world, but this multitaxon review shows that the characteristics of species that undertake such movements appear to make them particularly vulnerable to detrimental impacts of climate change. Migrants are key components of biological systems in high latitude regions, where the speed and magnitude of climate change impacts are greatest. They also rely on highly productive seasonal habitats, including wetlands and ocean upwellings that, with climate change, may become less food-rich and predictable in space and time. While migrants are adapted to adjust their behaviour with annual changes in the weather, the decoupling of climatic variables between geographically separate breeding and nonbreeding grounds is beginning to result in mistimed migration. Furthermore, human land-use and activity patterns will constrain the ability of many species to modify their migratory routes and may increase the stress induced by climate change. Adapting conservation strategies for migrants in the light of climate change will require substantial shifts in site designation policies, flexibility of management strategies and the integration of forward planning for both people and wildlife. While adaptation to changes may be feasible for some terrestrial systems, wildlife in the marine ecosystem may be more dependent on the degree of climate change mitigation that is achievable.

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“…State-space models have great potential for modelling population time series data and have been generalized to admit a variety of population data types and analyses (Newman et al 2006). The state-space approach has also been proposed as a powerful tool for modelling animal movement data because of its ability to deal simultaneously with potentially large measurement errors and variability in the dynamics of movement (Jonsen et al 2003).…”

Section: Introductionmentioning

confidence: 99%

Identifying leatherback turtle foraging behaviour from satellite telemetry using a switching state-space model

Jonsen¹,

Myers²,

James³

2007

Mar. Ecol. Prog. Ser.

274

306

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Identifying the foraging habitat of marine predators is vital to understanding the ecology of these species and for their management and conservation. Foraging habitat for many marine predators is dynamic, and this poses a serious challenge for understanding how oceanographic features may shape the ecology of these animals. To help resolve this issue, we present a switching state-space model (SSSM) for discerning different movement behaviours hidden within error-prone satellite telemetry data. Along with modelling the movement dynamics, the SSSM estimates the probability that an animal is in a particular discrete behavioural mode, such as transiting or foraging. Using Argos satellite telemetry for leatherback sea turtles, we show that the SSSM readily identifies distinct classes of movement behaviour from the noisy data. Moreover, patterns in simultaneously collected diving data, to which the model is blind, match well with behavioural mode estimates. By combining behavioural mode estimates from the model with the diving data, we show that while transiting, leatherbacks make longer, deeper dives; and while foraging, they encounter cooler waters that range from 13 to 22°C. These differences are consistent among the turtles studied and within the same turtle in different years. This modelling approach can enhance standard kernel density estimators for identifying habitat use by incorporating behavioural information into the estimation procedure. Ultimately, we can build predictive models of habitat use by incorporating environmental data and diving behaviour directly into the SSSM framework.

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Hidden Process Models For Animal Population Dynamics

Cited by 119 publications

References 20 publications

Host–pathogen time series data in wildlife support a transmission function between density and frequency dependence

Host–pathogen time series data in wildlife support a transmission function between density and frequency dependence

Travelling through a warming world: climate change and migratory species

Identifying leatherback turtle foraging behaviour from satellite telemetry using a switching state-space model

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