Centre for Research into Ecological & Environmental Modelling (CREEM) Theses
http://hdl.handle.net/10023/167
Thu, 28 Apr 2016 08:39:13 GMT2016-04-28T08:39:13ZCentre for Research into Ecological & Environmental Modelling (CREEM) Theseshttp://research-repository.st-andrews.ac.uk:80/bitstream/id/12278/polar.png
http://hdl.handle.net/10023/167
Estimating wildlife distribution and abundance from line transect surveys conducted from platforms of opportunity
http://hdl.handle.net/10023/3727
Line transect data obtained from 'platforms of opportunity' are useful for the monitoring
of long term trends in dolphin populations which occur over vast areas, yet analyses of
such data axe problematic due to violation of fundamental assumptions of line transect
methodology. In this thesis we develop methods which allow estimates of dolphin relative
abundance to be obtained when certain assumptions of line transect sampling are violated.
Generalised additive models are used to model encounter rate and mean school size as
a function of spatially and temporally referenced covariates. The estimated relationship
between the response and the environmental and locational covariates is then used to
obtain a predicted surface for the response over the entire survey region. Given those
predicted surfaces, a density surface can then be obtained and an estimate of abundance
computed by numerically integrating over the entire survey region. This approach is
particularly useful when search effort is not random, in which case standard line transect
methods would yield biased estimates.
Estimates of f (0) (the inverse of the effective strip (half-)width), an essential component
of the line transect estimator, may also be biased due to heterogeneity in detection probabilities.
We developed a conditional likelihood approach in which covariate effects are
directly incorporated into the estimation procedure. Simulation results indicated that the
method performs well in the presence of size-bias. When multiple covariates are used, it
is important that covariate selection be carried out.
As an example we applied the methods described above to eastern tropical Pacific dolphin
stocks. However, uncertainty in stock identification has never been directly incorporated
into methods used to obtain estimates of relative or absolute abundance. Therefore we
illustrate an approach in which trends in dolphin relative abundance axe monitored by
small areas, rather than stocks.
Mon, 01 Jan 2001 00:00:00 GMThttp://hdl.handle.net/10023/37272001-01-01T00:00:00ZMarques, Fernanda F. C.Line transect data obtained from 'platforms of opportunity' are useful for the monitoring
of long term trends in dolphin populations which occur over vast areas, yet analyses of
such data axe problematic due to violation of fundamental assumptions of line transect
methodology. In this thesis we develop methods which allow estimates of dolphin relative
abundance to be obtained when certain assumptions of line transect sampling are violated.
Generalised additive models are used to model encounter rate and mean school size as
a function of spatially and temporally referenced covariates. The estimated relationship
between the response and the environmental and locational covariates is then used to
obtain a predicted surface for the response over the entire survey region. Given those
predicted surfaces, a density surface can then be obtained and an estimate of abundance
computed by numerically integrating over the entire survey region. This approach is
particularly useful when search effort is not random, in which case standard line transect
methods would yield biased estimates.
Estimates of f (0) (the inverse of the effective strip (half-)width), an essential component
of the line transect estimator, may also be biased due to heterogeneity in detection probabilities.
We developed a conditional likelihood approach in which covariate effects are
directly incorporated into the estimation procedure. Simulation results indicated that the
method performs well in the presence of size-bias. When multiple covariates are used, it
is important that covariate selection be carried out.
As an example we applied the methods described above to eastern tropical Pacific dolphin
stocks. However, uncertainty in stock identification has never been directly incorporated
into methods used to obtain estimates of relative or absolute abundance. Therefore we
illustrate an approach in which trends in dolphin relative abundance axe monitored by
small areas, rather than stocks.Bayesian point process modelling of ecological communities
http://hdl.handle.net/10023/3710
The modelling of biological communities is important to further the understanding
of species coexistence and the mechanisms involved in maintaining
biodiversity. This involves considering not only interactions between individual
biological organisms, but also the incorporation of covariate information,
if available, in the modelling process. This thesis explores the use
of point processes to model interactions in bivariate point patterns within
a Bayesian framework, and, where applicable, in conjunction with covariate
data. Specifically, we distinguish between symmetric and asymmetric species
interactions and model these using appropriate point processes. In this thesis
we consider both pairwise and area interaction point processes to allow for
inhibitory interactions and both inhibitory and attractive interactions.
It is envisaged that the analyses and innovations presented in this thesis
will contribute to the parsimonious modelling of biological communities.
Fri, 28 Jun 2013 00:00:00 GMThttp://hdl.handle.net/10023/37102013-06-28T00:00:00ZNightingale, Glenna FaithThe modelling of biological communities is important to further the understanding
of species coexistence and the mechanisms involved in maintaining
biodiversity. This involves considering not only interactions between individual
biological organisms, but also the incorporation of covariate information,
if available, in the modelling process. This thesis explores the use
of point processes to model interactions in bivariate point patterns within
a Bayesian framework, and, where applicable, in conjunction with covariate
data. Specifically, we distinguish between symmetric and asymmetric species
interactions and model these using appropriate point processes. In this thesis
we consider both pairwise and area interaction point processes to allow for
inhibitory interactions and both inhibitory and attractive interactions.
It is envisaged that the analyses and innovations presented in this thesis
will contribute to the parsimonious modelling of biological communities.Estimating whale abundance using sparse hydrophone arrays
http://hdl.handle.net/10023/3463
Passive acoustic monitoring has been used to investigate many aspects of marine mammal ecology, although methods to estimate absolute abundance and density using acoustic data have only been developed in recent years. The instrument configuration in an acoustic survey determines which abundance estimation methods can be used. Sparsely distributed arrays of instruments are useful because wide geographic areas can be covered. However, instrument spacing in sparse arrays is such that the same vocalisation will not be detected on multiple instruments, excluding the use of some abundance estimation methods. The aim of this thesis was to explore cetacean abundance and density estimation using novel sparse array datasets, applying existing methods where possible, or developing new approaches.
The wealth of data collected by sparse arrays was demonstrated by analysing a 10-year dataset collected by the U.S. Navy’s Sound Surveillance System in the north-east Atlantic. Spatial and temporal patterns of blue (Balaenoptera musculus) and fin whale (Balaenoptera physalus) vocal activity were investigated using generalised additive models.
Distance sampling-based methods were applied to fin whale calls recorded by an array of Ocean Bottom Seismometers in the north-east Atlantic. Estimated call density was 993 calls/1000 km².hr⁻¹ (CV: 0.39). Animal density could not be estimated because the call rate was unknown. Further development of the call localisation method is required so the current density estimate may be biased. Furthermore, analysing a single day of data resulted in a high variance estimate.
Finally, a new simulation-based method developed to estimate density from single hydrophones was applied to blue whale calls recorded in the northern Indian Ocean. Estimated call density was 3 calls/1000 km².hr⁻¹ (CV: 0.17). Again, density of whales could not be estimated as the vocalisation rate was unknown. Lack of biological knowledge poses the greatest limitation to abundance and density estimation using acoustic data.
Wed, 20 Jun 2012 00:00:00 GMThttp://hdl.handle.net/10023/34632012-06-20T00:00:00ZHarris, Danielle VeronicaPassive acoustic monitoring has been used to investigate many aspects of marine mammal ecology, although methods to estimate absolute abundance and density using acoustic data have only been developed in recent years. The instrument configuration in an acoustic survey determines which abundance estimation methods can be used. Sparsely distributed arrays of instruments are useful because wide geographic areas can be covered. However, instrument spacing in sparse arrays is such that the same vocalisation will not be detected on multiple instruments, excluding the use of some abundance estimation methods. The aim of this thesis was to explore cetacean abundance and density estimation using novel sparse array datasets, applying existing methods where possible, or developing new approaches.
The wealth of data collected by sparse arrays was demonstrated by analysing a 10-year dataset collected by the U.S. Navy’s Sound Surveillance System in the north-east Atlantic. Spatial and temporal patterns of blue (Balaenoptera musculus) and fin whale (Balaenoptera physalus) vocal activity were investigated using generalised additive models.
Distance sampling-based methods were applied to fin whale calls recorded by an array of Ocean Bottom Seismometers in the north-east Atlantic. Estimated call density was 993 calls/1000 km².hr⁻¹ (CV: 0.39). Animal density could not be estimated because the call rate was unknown. Further development of the call localisation method is required so the current density estimate may be biased. Furthermore, analysing a single day of data resulted in a high variance estimate.
Finally, a new simulation-based method developed to estimate density from single hydrophones was applied to blue whale calls recorded in the northern Indian Ocean. Estimated call density was 3 calls/1000 km².hr⁻¹ (CV: 0.17). Again, density of whales could not be estimated as the vocalisation rate was unknown. Lack of biological knowledge poses the greatest limitation to abundance and density estimation using acoustic data.Quantifying biodiversity trends in time and space
http://hdl.handle.net/10023/3414
The global loss of biodiversity calls for robust large-scale diversity assessment. Biological diversity is a multi-faceted concept; defined as the “variety of life”, answering questions such as “How much is there?” or more precisely “Have we succeeded in reducing the rate of its decline?” is not straightforward. While various aspects of biodiversity give rise to numerous ways of quantification, we focus on temporal (and spatial) trends and their changes in species diversity.
Traditional diversity indices summarise information contained in the species abundance distribution, i.e. each species' proportional contribution to total abundance. Estimated from data, these indices can be biased if variation in detection probability is ignored. We discuss differences between diversity indices and demonstrate possible adjustments for detectability.
Additionally, most indices focus on the most abundant species in ecological communities. We introduce a new set of diversity measures, based on a family of goodness-of-fit statistics. A function of a free parameter, this family allows us to vary the sensitivity of these measures to dominance and rarity of species.
Their performance is studied by assessing temporal trends in diversity for five communities of British breeding birds based on 14 years of survey data, where they are applied alongside the current headline index, a geometric mean of relative abundances. Revealing the contributions of both rare and common species to biodiversity trends, these "goodness-of-fit" measures provide novel insights into how ecological communities change over time.
Biodiversity is not only subject to temporal changes, but it also varies across space. We take first steps towards estimating spatial diversity trends. Finally, processes maintaining biodiversity act locally, at specific spatial scales. Contrary to abundance-based summary statistics, spatial characteristics of ecological communities may distinguish these processes. We suggest a generalisation to a spatial summary, the cross-pair overlap distribution, to render it more flexible to spatial scale.
Fri, 30 Nov 2012 00:00:00 GMThttp://hdl.handle.net/10023/34142012-11-30T00:00:00ZStudeny, Angelika C.The global loss of biodiversity calls for robust large-scale diversity assessment. Biological diversity is a multi-faceted concept; defined as the “variety of life”, answering questions such as “How much is there?” or more precisely “Have we succeeded in reducing the rate of its decline?” is not straightforward. While various aspects of biodiversity give rise to numerous ways of quantification, we focus on temporal (and spatial) trends and their changes in species diversity.
Traditional diversity indices summarise information contained in the species abundance distribution, i.e. each species' proportional contribution to total abundance. Estimated from data, these indices can be biased if variation in detection probability is ignored. We discuss differences between diversity indices and demonstrate possible adjustments for detectability.
Additionally, most indices focus on the most abundant species in ecological communities. We introduce a new set of diversity measures, based on a family of goodness-of-fit statistics. A function of a free parameter, this family allows us to vary the sensitivity of these measures to dominance and rarity of species.
Their performance is studied by assessing temporal trends in diversity for five communities of British breeding birds based on 14 years of survey data, where they are applied alongside the current headline index, a geometric mean of relative abundances. Revealing the contributions of both rare and common species to biodiversity trends, these "goodness-of-fit" measures provide novel insights into how ecological communities change over time.
Biodiversity is not only subject to temporal changes, but it also varies across space. We take first steps towards estimating spatial diversity trends. Finally, processes maintaining biodiversity act locally, at specific spatial scales. Contrary to abundance-based summary statistics, spatial characteristics of ecological communities may distinguish these processes. We suggest a generalisation to a spatial summary, the cross-pair overlap distribution, to render it more flexible to spatial scale.Bayesian modelling of integrated data and its application to seabird populations
http://hdl.handle.net/10023/1635
Integrated data analyses are becoming increasingly popular in studies of wild animal populations where two or more separate sources of data contain information about common parameters. Here we develop an integrated population model using abundance and demographic data from a study of common guillemots (Uria aalge) on the Isle of May, southeast Scotland. A state-space model for the count data is supplemented by three demographic time series (productivity and two mark-recapture-recovery (MRR)), enabling the estimation of prebreeder emigration rate - a parameter for which there is no direct observational data, and which is unidentifiable in the separate analysis of MRR data. A Bayesian approach using MCMC provides a flexible and powerful analysis framework.
This model is extended to provide predictions of future population trajectories. Adopting random effects models for the survival and productivity parameters, we implement the MCMC algorithm to obtain a posterior sample of the underlying process means and variances (and population sizes) within the study period. Given this sample, we predict future demographic parameters, which in turn allows us to predict future population sizes and obtain the corresponding posterior distribution. Under the assumption that recent, unfavourable conditions persist in the future, we obtain a posterior probability of 70% that there is a population decline of >25% over a 10-year period.
Lastly, using MRR data we test for spatial, temporal and age-related correlations in guillemot survival among three widely separated Scottish colonies that have varying overlap in nonbreeding distribution. We show that survival is highly correlated over time for colonies/age classes sharing wintering areas, and essentially uncorrelated for those with separate wintering areas. These results strongly suggest that one or more aspects of winter environment are responsible for spatiotemporal variation in survival of British guillemots, and provide insight into the factors driving multi-population dynamics of the species.
Tue, 30 Nov 2010 00:00:00 GMThttp://hdl.handle.net/10023/16352010-11-30T00:00:00ZReynolds, Toby J.Integrated data analyses are becoming increasingly popular in studies of wild animal populations where two or more separate sources of data contain information about common parameters. Here we develop an integrated population model using abundance and demographic data from a study of common guillemots (Uria aalge) on the Isle of May, southeast Scotland. A state-space model for the count data is supplemented by three demographic time series (productivity and two mark-recapture-recovery (MRR)), enabling the estimation of prebreeder emigration rate - a parameter for which there is no direct observational data, and which is unidentifiable in the separate analysis of MRR data. A Bayesian approach using MCMC provides a flexible and powerful analysis framework.
This model is extended to provide predictions of future population trajectories. Adopting random effects models for the survival and productivity parameters, we implement the MCMC algorithm to obtain a posterior sample of the underlying process means and variances (and population sizes) within the study period. Given this sample, we predict future demographic parameters, which in turn allows us to predict future population sizes and obtain the corresponding posterior distribution. Under the assumption that recent, unfavourable conditions persist in the future, we obtain a posterior probability of 70% that there is a population decline of >25% over a 10-year period.
Lastly, using MRR data we test for spatial, temporal and age-related correlations in guillemot survival among three widely separated Scottish colonies that have varying overlap in nonbreeding distribution. We show that survival is highly correlated over time for colonies/age classes sharing wintering areas, and essentially uncorrelated for those with separate wintering areas. These results strongly suggest that one or more aspects of winter environment are responsible for spatiotemporal variation in survival of British guillemots, and provide insight into the factors driving multi-population dynamics of the species.Acoustic and ecological investigations into predator-prey interactions between Antarctic krill (Euphausia superba) and seal and bird predators
http://hdl.handle.net/10023/579
1. Antarctic krill (Euphausia superba) form aggregations known as swarms that vary greatly in size and density. Six acoustic surveys were conducted as part of multidisciplinary studies at two study sites, the western and eastern core boxes (WCB and ECB), during the 1997, 1998 and 1999 austral summers, at South Georgia. A quantitative, automated, image processing algorithm was used to identify swarms, and calculate swarm descriptors, or metrics. In contrast to acoustic surveys of aggregations of other pelagic species, a strong correlation (r = 0.88, p = 0.02, 95% C.I.= 0.24 to 0.99) between the number of krill swarms and the mean areal krill density [rho.hat] was found. Multivariate analysis was used to partition swarms into three types, based on contrasting morphological and internal krill density parameters. Swarm types were distributed differently between inter-surveys and between on and off-shelf regions.
This swarm type variation has implications for krill predators, by causing spatial heterogeneity in swarm detectability, suggesting that for optimal foraging to occur, predators must engage in some sort of adaptive foraging strategy.
2. Krill predator-prey interactions were found to occur at multiple spatial and temporal scales, in a nested, or hierarchical structure. At the largest inter-survey scale, an index of variability, I, was developed to compare variation in survey-scale predator sightings, sea temperature and [rho.hat]. Using I and a two-way ANOVA, core box, rather than year, was found to be a more important factor in determining species distribution. The absence of Blue-petrels (Halobaena caerulea) and the elevated number of Antarctic fur seals (Arctocephalus gazella) suggest that 1998 was a characterised by colder than average water surrounding South Georgia, and a high [rho.hat] in the ECB. At the smaller, intra-survey scales (<80 km, <5 day), the characteristic scale (distances in which
predator group size, or krill density were similar, L_s) were determined. For krill and predators L_s varied by survey and the L_s of krill also varied by depth within a survey. Overlap in L_s were stronger between predator species than between a predator species and krill, indicating predators were taking foraging cues from the activity of predators, rather than from the underlying krill distribution. No relationship was found between swarm characteristics and predator activity, suggesting either there is no relationship between krill swarms and predators, or that the predator and acoustic observation techniques may not be appropriate to detect such a relationship.
3. To overcome the 2-D sampling limitations of conventional echosounders, a multibeam echosounder (MBE) observed entire swarms in three-dimensions. Swarms found in the nearshore environment of Livingston Island situated in the South Shetland Islands, exhibited only a narrow range of surface area to volume ratios or roughnesses (R = 3.3, CV = 0.23), suggesting that krill adopt a consistent group behaviour to maintain swarm shape. Generalized additive models (GAM) suggested that the presence of air-breathing predators influenced the shape of a krill swarm (R decreased in the presence of predators: the swarm became more spherical). A 2D distance sampling framework was used to estimate the abundance, N, and associated variance of krill swarms. This technique took into account angular and range detectability (half-normal, [sigma_r.hat] = 365.00 m, CV = 0.16) and determined the vertical distribution of krill swarms to be best approximated by a beta-distribution ([alpha.hat] = 2.62, [CV.hat] = 0.19; [beta.hat] = 2.41, [CV.hat] = 0.15), giving the abundance of swarms in survey region as [N.hat] = 5,062 ([CV.hat] = 0.35). This research represents a substantial contribution to developing estimation of pelagic biomass using MBEs.
4. When using a single- or split-beam missing pings occur when the transmit or receive cycles are interrupted, often by aeration of the water column, under the echosounder transducer during rough weather. A thin-plate regression spline based approach was used to model the missing krill data, with knots chosen using a branch and bound algorithm. This method performs well for acoustic observations of krill swarms where data are tightly clustered and change rapidly. For these data the technique outperformed the standard MGCV GAM, and the technique is applicable for estimating acoustically derived biomass from line transect surveys.
Thu, 27 Nov 2008 00:00:00 GMThttp://hdl.handle.net/10023/5792008-11-27T00:00:00ZCox, Martin James1. Antarctic krill (Euphausia superba) form aggregations known as swarms that vary greatly in size and density. Six acoustic surveys were conducted as part of multidisciplinary studies at two study sites, the western and eastern core boxes (WCB and ECB), during the 1997, 1998 and 1999 austral summers, at South Georgia. A quantitative, automated, image processing algorithm was used to identify swarms, and calculate swarm descriptors, or metrics. In contrast to acoustic surveys of aggregations of other pelagic species, a strong correlation (r = 0.88, p = 0.02, 95% C.I.= 0.24 to 0.99) between the number of krill swarms and the mean areal krill density [rho.hat] was found. Multivariate analysis was used to partition swarms into three types, based on contrasting morphological and internal krill density parameters. Swarm types were distributed differently between inter-surveys and between on and off-shelf regions.
This swarm type variation has implications for krill predators, by causing spatial heterogeneity in swarm detectability, suggesting that for optimal foraging to occur, predators must engage in some sort of adaptive foraging strategy.
2. Krill predator-prey interactions were found to occur at multiple spatial and temporal scales, in a nested, or hierarchical structure. At the largest inter-survey scale, an index of variability, I, was developed to compare variation in survey-scale predator sightings, sea temperature and [rho.hat]. Using I and a two-way ANOVA, core box, rather than year, was found to be a more important factor in determining species distribution. The absence of Blue-petrels (Halobaena caerulea) and the elevated number of Antarctic fur seals (Arctocephalus gazella) suggest that 1998 was a characterised by colder than average water surrounding South Georgia, and a high [rho.hat] in the ECB. At the smaller, intra-survey scales (<80 km, <5 day), the characteristic scale (distances in which
predator group size, or krill density were similar, L_s) were determined. For krill and predators L_s varied by survey and the L_s of krill also varied by depth within a survey. Overlap in L_s were stronger between predator species than between a predator species and krill, indicating predators were taking foraging cues from the activity of predators, rather than from the underlying krill distribution. No relationship was found between swarm characteristics and predator activity, suggesting either there is no relationship between krill swarms and predators, or that the predator and acoustic observation techniques may not be appropriate to detect such a relationship.
3. To overcome the 2-D sampling limitations of conventional echosounders, a multibeam echosounder (MBE) observed entire swarms in three-dimensions. Swarms found in the nearshore environment of Livingston Island situated in the South Shetland Islands, exhibited only a narrow range of surface area to volume ratios or roughnesses (R = 3.3, CV = 0.23), suggesting that krill adopt a consistent group behaviour to maintain swarm shape. Generalized additive models (GAM) suggested that the presence of air-breathing predators influenced the shape of a krill swarm (R decreased in the presence of predators: the swarm became more spherical). A 2D distance sampling framework was used to estimate the abundance, N, and associated variance of krill swarms. This technique took into account angular and range detectability (half-normal, [sigma_r.hat] = 365.00 m, CV = 0.16) and determined the vertical distribution of krill swarms to be best approximated by a beta-distribution ([alpha.hat] = 2.62, [CV.hat] = 0.19; [beta.hat] = 2.41, [CV.hat] = 0.15), giving the abundance of swarms in survey region as [N.hat] = 5,062 ([CV.hat] = 0.35). This research represents a substantial contribution to developing estimation of pelagic biomass using MBEs.
4. When using a single- or split-beam missing pings occur when the transmit or receive cycles are interrupted, often by aeration of the water column, under the echosounder transducer during rough weather. A thin-plate regression spline based approach was used to model the missing krill data, with knots chosen using a branch and bound algorithm. This method performs well for acoustic observations of krill swarms where data are tightly clustered and change rapidly. For these data the technique outperformed the standard MGCV GAM, and the technique is applicable for estimating acoustically derived biomass from line transect surveys.Incorporating measurement error and density gradients in distance sampling surveys
http://hdl.handle.net/10023/391
Distance sampling is one of the most commonly used methods for estimating density
and abundance. Conventional methods are based on the distances of detected animals
from the center of point transects or the center line of line transects. These distances
are used to model a detection function: the probability of detecting an animal, given
its distance from the line or point. The probability of detecting an animal in the
covered area is given by the mean value of the detection function with respect to
the available distances to be detected. Given this probability, a Horvitz-Thompson-
like estimator of abundance for the covered area follows, hence using a model-based
framework. Inferences for the wider survey region are justified using the survey design.
Conventional distance sampling methods are based on a set of assumptions. In
this thesis I present results that extend distance sampling on two fronts.
Firstly, estimators are derived for situations in which there is measurement error in
the distances. These estimators use information about the measurement error in two
ways: (1) a biased estimator based on the contaminated distances is multiplied by an
appropriate correction factor, which is a function of the errors (PDF approach), and
(2) cast into a likelihood framework that allows parameter estimation in the presence
of measurement error (likelihood approach).
Secondly, methods are developed that relax the conventional assumption that the
distribution of animals is independent of distance from the lines or points (usually
guaranteed by appropriate survey design). In particular, the new methods deal with
the case where animal density gradients are caused by the use of non-random sampler
allocation, for example transects placed along linear features such as roads or streams.
This is dealt with separately for line and point transects, and at a later stage an
approach for combining the two is presented.
A considerable number of simulations and example analysis illustrate the performance of the proposed methods.
Thu, 01 Nov 2007 00:00:00 GMThttp://hdl.handle.net/10023/3912007-11-01T00:00:00ZMarques, Tiago Andre Lamas OliveiraDistance sampling is one of the most commonly used methods for estimating density
and abundance. Conventional methods are based on the distances of detected animals
from the center of point transects or the center line of line transects. These distances
are used to model a detection function: the probability of detecting an animal, given
its distance from the line or point. The probability of detecting an animal in the
covered area is given by the mean value of the detection function with respect to
the available distances to be detected. Given this probability, a Horvitz-Thompson-
like estimator of abundance for the covered area follows, hence using a model-based
framework. Inferences for the wider survey region are justified using the survey design.
Conventional distance sampling methods are based on a set of assumptions. In
this thesis I present results that extend distance sampling on two fronts.
Firstly, estimators are derived for situations in which there is measurement error in
the distances. These estimators use information about the measurement error in two
ways: (1) a biased estimator based on the contaminated distances is multiplied by an
appropriate correction factor, which is a function of the errors (PDF approach), and
(2) cast into a likelihood framework that allows parameter estimation in the presence
of measurement error (likelihood approach).
Secondly, methods are developed that relax the conventional assumption that the
distribution of animals is independent of distance from the lines or points (usually
guaranteed by appropriate survey design). In particular, the new methods deal with
the case where animal density gradients are caused by the use of non-random sampler
allocation, for example transects placed along linear features such as roads or streams.
This is dealt with separately for line and point transects, and at a later stage an
approach for combining the two is presented.
A considerable number of simulations and example analysis illustrate the performance of the proposed methods.A Bayesian approach to modelling field data on multi-species predator prey-interactions
http://hdl.handle.net/10023/174
Multi-species functional response models are required to model the predation of generalist preda-
tors, which consume more than one prey species. In chapter 2, a new model for the multi-species
functional response is presented. This model can describe generalist predators that exhibit func-
tional responses of Holling type II to some of their prey and of type III to other prey. In chapter
3, I review some of the theoretical distinctions between Bayesian and frequentist statistics and
show how Bayesian statistics are particularly well-suited for the fitting of functional response
models because uncertainty can be represented comprehensively. In chapters 4 and 5, the multi-
species functional response model is fitted to field data on two generalist predators: the hen
harrier Circus cyaneus and the harp seal Phoca groenlandica. I am not aware of any previous
Bayesian model of the multi-species functional response that has been fitted to field data.
The hen harrier's functional response fitted in chapter 4 is strongly sigmoidal to the densities
of red grouse Lagopus lagopus scoticus, but no type III shape was detected in the response to
the two main prey species, field vole Microtus agrestis and meadow pipit Anthus pratensis. The
impact of using Bayesian or frequentist models on the resulting functional response is discussed.
In chapter 5, no functional response could be fitted to the data on harp seal predation. Possible
reasons are discussed, including poor data quality or a lack of relevance of the available data for
informing a behavioural functional response model.
I conclude with a comparison of the role that functional responses play in behavioural, population
and community ecology and emphasise the need for further research into unifying these different
approaches to understanding predation with particular reference to predator movement.
In an appendix, I evaluate the possibility of using a functional response for inferring the abun-
dances of prey species from performance indicators of generalist predators feeding on these prey.
I argue that this approach may be futile in general, because a generalist predator's energy intake
does not depend on the density of any single of its prey, so that the possibly unknown densities
of all prey need to be taken into account.
Sun, 01 Jan 2006 00:00:00 GMThttp://hdl.handle.net/10023/1742006-01-01T00:00:00ZAsseburg, ChristianMulti-species functional response models are required to model the predation of generalist preda-
tors, which consume more than one prey species. In chapter 2, a new model for the multi-species
functional response is presented. This model can describe generalist predators that exhibit func-
tional responses of Holling type II to some of their prey and of type III to other prey. In chapter
3, I review some of the theoretical distinctions between Bayesian and frequentist statistics and
show how Bayesian statistics are particularly well-suited for the fitting of functional response
models because uncertainty can be represented comprehensively. In chapters 4 and 5, the multi-
species functional response model is fitted to field data on two generalist predators: the hen
harrier Circus cyaneus and the harp seal Phoca groenlandica. I am not aware of any previous
Bayesian model of the multi-species functional response that has been fitted to field data.
The hen harrier's functional response fitted in chapter 4 is strongly sigmoidal to the densities
of red grouse Lagopus lagopus scoticus, but no type III shape was detected in the response to
the two main prey species, field vole Microtus agrestis and meadow pipit Anthus pratensis. The
impact of using Bayesian or frequentist models on the resulting functional response is discussed.
In chapter 5, no functional response could be fitted to the data on harp seal predation. Possible
reasons are discussed, including poor data quality or a lack of relevance of the available data for
informing a behavioural functional response model.
I conclude with a comparison of the role that functional responses play in behavioural, population
and community ecology and emphasise the need for further research into unifying these different
approaches to understanding predation with particular reference to predator movement.
In an appendix, I evaluate the possibility of using a functional response for inferring the abun-
dances of prey species from performance indicators of generalist predators feeding on these prey.
I argue that this approach may be futile in general, because a generalist predator's energy intake
does not depend on the density of any single of its prey, so that the possibly unknown densities
of all prey need to be taken into account.