Modelling the population size and dynamics of the British grey seal

Thomas, Len; Russell, Debbie J. F.; Duck, Callan; Morris, Chris D.; Lonergan, Mike; Empacher, Fanny; Thompson, David; Harwood, John

doi:10.1002/aqc.3134

Cited by 38 publications

(85 citation statements)

References 40 publications

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“…show different dynamics to those of harbour seals (Russell, Morris, Duck, Thompson, & Hiby, 2019;Thomas et al, 2019). Both pup production and population estimates for grey seals in the West Scotland and the Western Isles SMUs were increasing until the mid-1990s…”

Section: Robustness Of Trendsmentioning

confidence: 96%

The status of harbour seals (Phoca vitulina) in the UK

Thompson

Duck

Morris

et al. 2019

Aquatic Conservation

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1. Estimates of population size and trends are essential for effective conservation and management of wildlife populations. For harbour seals (Phoca vitulina), these data are required to fulfil statutory reporting obligations under national and international regulations.2. Aerial survey counts of harbour seals hauled out during their annual moult were used to estimate population sizes and trends at UK, regional (seal management unit, SMU) and local (Special Area of Conservation, SAC) scales.3. Results indicate that the current UK harbour seal population is similar to estimates from the late 1990s, but there were significant declines in some subpopulations and increases in others. 4. Fitted trends suggest that the UK harbour seal population can be divided into three geographically coherent groups: South-east populations (South-East and North-East England SMUs) have shown continuous increases punctuated by phocine distemper virus epidemics in 1988 and 2002; north-east populations (East Scotland, Moray Firth, North Coast and Orkney, and Shetland SMUs) have declined since the late 1990s; north-west populations (West Scotland, Western Isles, and South-West Scotland SMUs) have remained stable or increased. Similar geographical population substructure is evident in recent population genetics results. 5. Trends within SACs generally match SMU trends since 2002. Of the nine SACs designated for harbour seals, four declined (in East Scotland, Moray Firth, and North Coast and Orkney SMUs), four remained stable (in Shetland and West Scotland SMUs), and one increased (in South-East England SMU).6. Large changes in relative abundance have resulted from differences in regional trends. For example, in 1996-1997 the West Scotland and North Coast and Orkney SMUs each held~27% of the Great Britain population but now hold 50% and~4% respectively; in 1980, the South-East England SMU population was~50% that of the Wadden Sea population, but by 2016 it was equivalent to <20% of the Wadden Sea count.

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Section: Robustness Of Trendsmentioning

confidence: 96%

The status of harbour seals (Phoca vitulina) in the UK

Thompson

Duck

Morris

et al. 2019

Aquatic Conservation

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“…This indicates that population dynamics are being driven by density-dependent processes and that the populations are at or nearing carrying capacity (Thomas et al, 2019). Combining this pup production time-series with an independent estimate of population size (Lonergan, Duck, Thompson, Moss, & Mcconnell, 2011;Russell, Duck, Morris, & Thompson, 2016), within an age-structured population model, has revealed that the main density-dependent effect is likely acting through increased pup mortality (Thomas et al, 2019). How pup production and its trends relate to estimates of population size and trajectories is dependent on fecundity and age-sex-specific survival, and changes therein.…”

Section: Overall Trends In Pup Productionmentioning

confidence: 99%

“…Thus, pup survival may still be driven by density-dependent processes acting on females, as well as those acting directly on pups. Regional population trends are discussed in Thomas et al (2019). Regional population trends are discussed in Thomas et al (2019).…”

Section: Overall Trends In Pup Productionmentioning

confidence: 99%

“…Good habitat quality is associated with access to the sea or pools (Stephenson, Matthiopoulos, & Harwood, 2007;Twiss, Caudron, Pomeroy, Thomas, & Mills, 2000;Twiss, Thomas, & Pomeroy, 2001 As well as a lack of recruiters, these declining colonies may also have lost established females to other colonies; even early in the decline of the historical Outer Hebrides colonies, the rates of decline were higher than estimated adult mortality (Thomas et al, 2019). Good habitat quality is associated with access to the sea or pools (Stephenson, Matthiopoulos, & Harwood, 2007;Twiss, Caudron, Pomeroy, Thomas, & Mills, 2000;Twiss, Thomas, & Pomeroy, 2001 As well as a lack of recruiters, these declining colonies may also have lost established females to other colonies; even early in the decline of the historical Outer Hebrides colonies, the rates of decline were higher than estimated adult mortality (Thomas et al, 2019).…”

Section: Local Trends In Pup Productionmentioning

confidence: 99%

“…The regional pup production estimates from these regularly monitored colonies are input into a Bayesian agestructured model to provide annual estimates of population size (Thomas et al, 2019). Scouting surveys are conducted every few years to cover potential grey seal pupping sites within the study regions.…”

Section: Introductionmentioning

confidence: 99%

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Monitoring long‐term changes in UK grey seal pup production

Russell

Morris

Duck

et al. 2019

Aquatic Conservation

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1. The population size of many species, particularly those in the aquatic environment, cannot be censused directly. Counts, during the breeding season, of one component of the population (e.g. breeding females) are often used as an index to allow investigation of trends. In species, such as grey seals (Halichoerus grypus), for which births are not tightly synchronous, single counts of pups represent an unknown proportion of the total number of pups born (pup production), and thus of breeding females (i.e. each pup born represents a breeding female).2. Grey seals pup at large colonies around the coast of the UK. Information on their populations is required under national and international legislation.3. In the UK, pup production has been monitored at some colonies since 1956. Currently, large colonies (~90% of UK pup production) are monitored either using ground (~10%; annually) or aerial surveys (~80%; annually until 2010, and thereafter biennially).4. Here, the model used to estimate pup production at aerially surveyed colonies from 1987 to 2010 is described; structured pup counts from multiple surveys are combined with knowledge of life-history parameters to model birth curves.5. The resulting trends in pup production up to 2010 (aerially surveyed colonies) and 2016 (ground surveyed colonies) are examined.6. In 2010, over 45,000 pups were estimated to be born in the UK. Pup production appeared to have reached an asymptote in the Inner Hebrides, Outer Hebrides and Orkney, whereas it is still increasing exponentially in the North Sea. Although density-dependent processes acting at sea are likely to be responsible for these regional trends, we suggest that the substantial variation in trends within regions are likely caused by processes acting at the colony level. Some long-established colonies, including Special Areas of Conservation, are exhibiting decreasing trends. 7. Special Areas of Conservation often serve as de facto monitoring sites and are the focus of management efforts. The observed temporal and spatial variability in patterns of colony growth rates highlight the potential risks of using such sites to develop wider management policies.

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Bayesian hybrid analytics for uncertainty analysis and real‐time crop management

et al. 2021

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Dynamic, deterministic agricultural models, and current machine learning technologies based on sensor data, enable and support decision making for on-farm management. However, their predictions are subject to various sources of uncertainty. Hybrid analytics that leverage both modelled and sensor data provide predictive information that makes the best of both approaches in a timely fashion to inform operational decision making and enable inclusive uncertainty quantification. We describe and evaluate a probabilistic Bayesian data assimilation tool that combines the state variables from the Sirius wheat (Triticum aestivum L.) development model with time-series environmental and leaf count data. Additionally, the uncertainty associated with input parameters is quantified via expert opinion. The Bayesian approach obtained point estimates through time that were accompanied by inclusive, probabilistic, 95% credible intervals. At the end of simulation, a typical model predicted a final leaf number of 6.6 leaves, Sirius alone predicted seven leaves and the mean of the observed data was 6.7 leaves. The 95% credible interval was estimated as 5.1-8.4 leaves. Importantly, the tool was able to "redirect" simulated outputs if input parameters such as minimum leaf number or base phyllochron were incorrectly specified, with the implication that on-farm decision makers would have advance warning of variation in expected harvest date. Relatively few plants with time-intensive data were sufficient to fit the model, however, more plants would be desirable to reduce the rather wide range of credible intervals. Nevertheless, the tool shows potential and could be readily implemented with low resource requirements, providing more finely tuned harvest date information, with probabilistic uncertainty quantification built-in, for on-farm decisions.

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Modelling the population size and dynamics of the British grey seal

Cited by 38 publications

References 40 publications

The status of harbour seals (Phoca vitulina) in the UK

The status of harbour seals (Phoca vitulina) in the UK

Monitoring long‐term changes in UK grey seal pup production

Bayesian hybrid analytics for uncertainty analysis and real‐time crop management

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