2024-03-29T01:27:29Zhttps://research-repository.st-andrews.ac.uk/oai/requestoai:research-repository.st-andrews.ac.uk:10023/6292019-04-01T11:10:16Zcom_10023_625com_10023_165com_10023_39col_10023_626
Point and interval estimates of abundance using multiple covariate distance sampling: an example using great bustards.
Rexstad, Eric
multiple covariate distance sampling
bootstrap
program Distance
estimation of population size
density
Description of computations to produce sex-specific estimates of density from a multiple-covariate distance sampling analysis. Program Distance 5.0 has limited capacity to bootstrap certain types of analytical situations (e.g., cluster size as a covariate). Herein I describe steps and code to perform an analysis of this sort. Possible ways to adapt this code for similar analyses are described.
2009-01-12T17:23:39Z
2009-01-12T17:23:39Z
2009-01-12T17:23:39Z
2007
Report
CREEM technical report ; 2007-02
http://hdl.handle.net/10023/629
en
14 p.
CREEM, University of St Andrews
oai:research-repository.st-andrews.ac.uk:10023/6282019-04-01T11:10:16Zcom_10023_625com_10023_165com_10023_39col_10023_626
Non-uniform coverage estimators for distance sampling.
Rexstad, Eric
Density surface model
estimator efficiency
Horvitz-Thompson estimator
probability proportional to size (pps) estimators
Allocation of sampling effort in the context of distance sampling is considered.
Specifically, allocation of effort in proportion to portions of the survey region that likely
contain high concentrations of animals are explored. The probability of a portion of the
survey region being included in the sample is proportional to the estimated number of
animals in that portion. These estimated numbers of animals may be derived from a
density surface model. This results in unequal coverage probability, and a Horvitz-
Thompson like estimator can be used to estimate population abundance. The properties
of this estimator is explored here via simulation. The benefits, measured in terms of
increased precision over traditional equal coverage probability estimators, are meagre,
and largely manifested when the underlying population distribution is a smooth gradient.
2009-01-12T16:31:00Z
2009-01-12T16:31:00Z
2009-01-12T16:31:00Z
2007
Report
CREEM technical report ; 2007-01
http://hdl.handle.net/10023/628
en
12 p.
CREEM, University of St Andrews
oai:research-repository.st-andrews.ac.uk:10023/6352019-04-01T11:10:17Zcom_10023_625com_10023_165com_10023_39col_10023_626
Incorporating Model Uncertainty into the Sequential Importance Sampling Framework using a Model Averaging Approach, or Trans-Dimensional Sequential Importance Sampling.
Lynam, Christopher
King, Ruth
Thomas, Len
Buckland, Stephen T.
particle filtering
model space
sequential Monte Carlo
Markov chain
A sequential Bayesian Monte Carlo approach is proposed in which model space can be explored during the Sequential Importance Sampling (SIS, a.k.a. Particle Filtering) fitting process. The algorithm allows model space to be explored while filtering forwards through time and takes a similar approach to Reversible Jump Markov Chain Monte Carlo (RJMCMC) strategies, whereby parameters jump into and out of the model structure. Possible efficiency gains of the new Trans-Dimensional SIS routine are discussed and the approach is considered most beneficial when the exploration of large model space in the SIS framework is desired.
2009-01-14T16:28:41Z
2009-01-14T16:28:41Z
2009-01-14T16:28:41Z
2007
Report
CREEM technical report ; 2007-06
http://hdl.handle.net/10023/635
en
14 p.
CREEM, University of St Andrews
oai:research-repository.st-andrews.ac.uk:10023/77412023-04-26T00:22:24Zcom_10023_625com_10023_165com_10023_39com_10023_879com_10023_878col_10023_626col_10023_880
Status and future of research on the behavioural responses of marine mammals to U.S. Navy sonar
Harris, Catriona M
Thomas, Len
Office of Naval Research
Office of Naval Research
University of St Andrews. School of Biology
University of St Andrews. Scottish Oceans Institute
University of St Andrews. Centre for Research into Ecological & Environmental Modelling
University of St Andrews. Statistics
University of St Andrews. Marine Alliance for Science & Technology Scotland
University of St Andrews. Sea Mammal Research Unit
GC Oceanography
QH301 Biology
SDG 14 - Life Below Water
A review of the status and future of research into behavioral responses of marine mammals to naval sonar exposure was undertaken to evaluate the return on investment of current US Navy funded programs, identify the data needs and the contributions of current research programs to meeting data needs, and determine the ability to meet outstanding data needs given the current state of technology. As part of this review, a workshop was held from 21-22 April 2015 in Monterey, California. Workshop attendees were key representatives of Navy-funded behavioral response studies, as well as three external reviewers who were selected because of their expertise in animal behavior and behavioral responses to anthropogenic stimuli in the aquatic and terrestrial environments. Prior to the workshop, a questionnaire was circulated to canvass the opinions of members of the scientific community (primarily workshop attendees exclusive of external reviewers) on each of the research approaches taken to address this topic. The workshop was then structured around the questionnaire and responses received, via a series of discussion sessions. Afterwards, each research approach was evaluated independently by the external reviewers. This report presents a synthesis of the evaluations and recommendations of the external reviewers on current and future behavioral response research relevant to naval sonar. All reviewers agreed that excellent progress has been made on this topic and that each of the research approaches has contributed to our understanding of cetacean responses to naval sonar. The report includes specific comments and recommendations of the reviewers relevant to each approach, but also includes suggestions for priority species and a comprehensive list of recommendations for the future of BRS research in general (Tables 1 and 2). In summary it was recommended that BRS research be continued and extended to increase sample sizes and experimental replication, and temporal duration and spatial scale including more research in areas where the animals are presumably more naïve than on the naval ranges. It was noted that future investigations would benefit from combining experimentation and observation to enable linkage of short-term behavioral response to long-term fitness consequences of repeated exposure. Beaked whales were the species group ranked highest in terms of research priority. The importance of baseline studies and longer-term monitoring of animals before and after exposure is emphasized throughout.
2015-11-05T11:10:05Z
2015-11-05T11:10:05Z
2015-11-05T11:10:05Z
2015
Report
Harris , C M & Thomas , L 2015 , Status and future of research on the behavioural responses of marine mammals to U.S. Navy sonar . CREEM Technical Report , no. 2015-3 , University of St Andrews .
PURE: 228455497
PURE UUID: 2e7388ae-5819-4a9c-9b49-b3b04c57bace
ORCID: /0000-0002-7436-067X/work/29591671
ORCID: /0000-0001-9198-2414/work/60887682
http://hdl.handle.net/10023/7741
http://hdl.handle.net/10023/7741
N00014-15-1-2664
N/A
eng
CREEM Technical Report
University of St Andrews
oai:research-repository.st-andrews.ac.uk:10023/130372023-04-18T09:37:40Zcom_10023_625com_10023_165com_10023_39com_10023_879com_10023_878col_10023_626col_10023_880
Evaluating the effect of measurement error in pairs of 3D bearings in point transect sampling estimates of density
Marques, Tiago A.
Duarte, Pedro
Peixe, Telmo
Moretti, David
Thomas, Len
University of St Andrews. School of Mathematics and Statistics
University of St Andrews. Scottish Oceans Institute
University of St Andrews. Centre for Research into Ecological & Environmental Modelling
University of St Andrews. Marine Alliance for Science & Technology Scotland
QA Mathematics
GE Environmental Sciences
2018-03-28T08:30:08Z
2018-03-28T08:30:08Z
2018-03-28T08:30:08Z
2018-03-27
Report
Marques , T A , Duarte , P , Peixe , T , Moretti , D & Thomas , L 2018 , Evaluating the effect of measurement error in pairs of 3D bearings in point transect sampling estimates of density . CREEM Technical Report , no. 2018-1 , University of St Andrews .
PURE: 252626559
PURE UUID: e839ae6a-7039-4285-8ea8-2185ff87be69
ORCID: /0000-0002-7436-067X/work/43149682
ORCID: /0000-0002-2581-1972/work/56861281
http://hdl.handle.net/10023/13037
eng
CREEM Technical Report
University of St Andrews
oai:research-repository.st-andrews.ac.uk:10023/6362019-04-01T11:10:17Zcom_10023_625com_10023_165com_10023_39col_10023_626
Estimating the distribution of demersal fishing effort from VMS data using hidden Markov models.
Borchers, David L.
Reid, David G.
vessel monitoring system
hidden Markov model
fishing effort
mixture model
2009-01-14T17:27:21Z
2009-01-14T17:27:21Z
2009-01-14T17:27:21Z
2008
Report
CREEM technical report ; 2008-01
http://hdl.handle.net/10023/636
en
23 p.
CREEM, University of St Andrews
oai:research-repository.st-andrews.ac.uk:10023/77202023-04-18T09:37:15Zcom_10023_625com_10023_165com_10023_39com_10023_879com_10023_878col_10023_626col_10023_880
A description of LATTE outputs
Marques, Tiago A.
Thomas, Len
University of St Andrews. School of Mathematics and Statistics
University of St Andrews. Scottish Oceans Institute
University of St Andrews. Centre for Research into Ecological & Environmental Modelling
University of St Andrews. Statistics
University of St Andrews. Marine Alliance for Science & Technology Scotland
GC Oceanography
QL Zoology
2015-10-30T16:10:10Z
2015-10-30T16:10:10Z
2015-10-30T16:10:10Z
2015-10-30
Report
Marques , T A & Thomas , L 2015 , A description of LATTE outputs . CREEM Technical Report , no. 2015-2 , University of St Andrews .
PURE: 226928716
PURE UUID: 6afbb49e-4793-4af4-a706-a7f4a8de0820
ORCID: /0000-0002-7436-067X/work/29591662
ORCID: /0000-0002-2581-1972/work/56861259
http://hdl.handle.net/10023/7720
eng
CREEM Technical Report
University of St Andrews
oai:research-repository.st-andrews.ac.uk:10023/21582023-04-18T09:36:45Zcom_10023_625com_10023_165com_10023_39com_10023_879com_10023_878col_10023_626col_10023_880
An update to the methods in Endangered Species Research 2011 paper "Estimating North Pacific right whale Eubalaena japonica density using passive acoustic cue counting"
Marques, Tiago A.
Munger, Lisa
Thomas, Len
Wiggins, Sean
Hildebrand, John
University of St Andrews. School of Mathematics and Statistics
University of St Andrews. Scottish Oceans Institute
University of St Andrews. Centre for Research into Ecological & Environmental Modelling
University of St Andrews. Statistics
University of St Andrews. Marine Alliance for Science & Technology Scotland
Distance sampling
QL Zoology
HA Statistics
2012-01-12T09:44:01Z
2012-01-12T09:44:01Z
2012-01-12T09:44:01Z
2012
Report
Marques , T A , Munger , L , Thomas , L , Wiggins , S & Hildebrand , J 2012 , An update to the methods in Endangered Species Research 2011 paper "Estimating North Pacific right whale Eubalaena japonica density using passive acoustic cue counting" . CREEM Technical Report , no. 2012-1 , University of St Andrews .
PURE: 16492980
PURE UUID: 17e7ff53-117e-4bce-807a-645b5dd5ddb3
ORCID: /0000-0002-7436-067X/work/29591706
ORCID: /0000-0002-2581-1972/work/56861289
http://hdl.handle.net/10023/2158
http://creem2.st-andrews.ac.uk/reports
eng
CREEM Technical Report
University of St Andrews
oai:research-repository.st-andrews.ac.uk:10023/6332019-04-01T11:10:17Zcom_10023_625com_10023_165com_10023_39col_10023_626
Accommodating availability bias on line transect surveys using hidden Markov models.
Borchers, David L.
Samara, Filipa I. P.
availability bias
surfacing pattern
hidden Markov model
line transect survey
Maximum likelihood methods are developed which accommodate intermittent animal availability of animals on line transect surveys. Existing 'availability bias' correction methods are shown to be inadequate in general. The new method is applied to an aerial survey of whales, using a hidden Markov model to characterise the availability process.
2009-01-14T15:33:25Z
2009-01-14T15:33:25Z
2009-01-14T15:33:25Z
2007
Report
CREEM technical report ; 2007-05
http://hdl.handle.net/10023/633
en
13 p.
CREEM, University of St Andrews
oai:research-repository.st-andrews.ac.uk:10023/32162023-04-26T00:22:12Zcom_10023_625com_10023_165com_10023_39com_10023_879com_10023_878col_10023_626col_10023_880
Workshop on new developments in cetacean survey methods
Borchers, David Louis
Thomas, Len
Buckland, Stephen Terrence
Skaug, Hans
Barlow, Jay
University of St Andrews. School of Mathematics and Statistics
University of St Andrews. Marine Alliance for Science & Technology Scotland
University of St Andrews. Scottish Oceans Institute
University of St Andrews. Centre for Research into Ecological & Environmental Modelling
University of St Andrews. Statistics
University of St Andrews. St Andrews Sustainability Institute
QL Zoology
QA Mathematics
SDG 14 - Life Below Water
This report contains the slides from a workshop on New Developments in Cetacean Survey Methods held on 27th November 2011 at the 19th Biennial Conference on the Biology of Marine Mammals, Tampa, Florida. Review talks were given on Passive Acoustic Density Estimation (Len Thomas); Dealing with g(0)<1: Perception Bias (Stephen Buckland); Dealing with g(0)<1: Availability Bias (Hans Skaug); Dealing with Measurement Error (David Borchers); and Density Surface Modelling (Jay Barlow). The sessions were followed by a discussion, and this is summarized at the end of the report.
2012-10-23T15:01:05Z
2012-10-23T15:01:05Z
2012-10-23T15:01:05Z
2011
Report
Borchers , D L , Thomas , L , Buckland , S T , Skaug , H & Barlow , J 2011 , Workshop on new developments in cetacean survey methods . CREEM Technical Report , no. 2011-3 , University of St Andrews .
PURE: 31701032
PURE UUID: c522c1f4-3101-4bd5-a376-7fd9a00cc0a6
ORCID: /0000-0002-7436-067X/work/29591709
ORCID: /0000-0002-3944-0754/work/72842416
ORCID: /0000-0002-9939-709X/work/73700967
http://hdl.handle.net/10023/3216
eng
CREEM Technical Report
University of St Andrews
oai:research-repository.st-andrews.ac.uk:10023/6322019-04-01T11:10:17Zcom_10023_625com_10023_165com_10023_39col_10023_626
Investigation of towed hydrophone monitoring power for harbour porpoise on the SCANS II survey.
Borchers, David L.
Burt, M. Louise.
statistical power
monitoring
wildlife population assessment
distance sampling
trend estimation
We investigate the power of harbour porpoise monitoring programmes which use an index of relative abundance to detect change. Power depends on the variability in the constant of proportionality relating the index to absolute abundance, as well as on the variability in the index given this constant. We estimate both from the SCANS II data and from European Seabirds at Sea (ESAS) data. Estimates of the coefficient of variation of the constant of proportionality are large and this results in very low power. Because these estimates may be unrealistically large for well-designed monitoring programs, we feel it is inappropriate to draw strong conclusions about the power of future monitoring programmes based on them.
ESAS surveys are found to be more efficient in terms of effort required to achieve given power, than the SCANS II passive acoustic surveys. However, the comparison may not be a fair one, for the following reason. The estimated CV of the constant of proportionality is obtained from the ratio of the index of density and the corresponding SCANS II absolute density estimate; the ESAS index is likely to be more highly correlated with the SCANS II estimate than the acoustic index, because like the SCANS II estimate, it is based on visual detections. In addition, standardization of the passive acoustic survey methods could yield substantially higher efficiency.
We provide a table giving power as a function of the CV of the constant of proportionality and the CV of the index, given this constant - this can be used to compare methods if reliable estimates of these CVs are available.
2009-01-14T15:13:42Z
2009-01-14T15:13:42Z
2009-01-14T15:13:42Z
2007
Report
CREEM technical report ; 2007-04
http://hdl.handle.net/10023/632
en
8 p.
CREEM, University of St Andrews
oai:research-repository.st-andrews.ac.uk:10023/185092024-03-28T00:47:09Zcom_10023_625com_10023_165com_10023_39com_10023_879com_10023_878col_10023_626col_10023_880
From here and now to there and then : practical recommendations for extrapolating cetacean density surface models to novel conditions
Bouchet, Philippe Jean-Francois
Miller, David Lawrence
Roberts, Jason
Mannocci, Laura
Harris, Catriona M
Thomas, Len
University of St Andrews. School of Mathematics and Statistics
University of St Andrews. Centre for Research into Ecological & Environmental Modelling
University of St Andrews. Applied Mathematics
University of St Andrews. Scottish Oceans Institute
University of St Andrews. School of Biology
University of St Andrews. Sea Mammal Research Unit
University of St Andrews. Statistics
University of St Andrews. Marine Alliance for Science & Technology Scotland
GC Oceanography
QH301 Biology
SDG 14 - Life Below Water
Density surface models (DSMs) are clearly established as a method of choice for the analysis of cetacean line transect survey data, and are increasingly used to inform risk assessments in remote marine areas subject to rising anthropogenic impacts (e.g. the high seas). However, despite persistent skepticism about the validity of extrapolated models, more and more DSMs are being applied well beyond the boundaries of the study regions where field sampling originally took place. This leads to potentially uncertain and error-prone model predictions that may mislead on-the-ground management interventions and undermine conservation decision-making. In addition, no consensus currently exists on the best way to define and measure extrapolation when it occurs, leaving users without the tools they require to audit models projected into novel conditions. Consequently, a transparent and consistent protocol for identifying scenarios under which extrapolation may be appropriate (or conversely, ill-advised) is urgently needed to better gauge how models behave outside the boundaries of sample data and to know how much faith can be placed in their outputs. This report aims to address this gap by synthesising recent advances in extrapolation detection, and presenting recommendations for a minimum standard for measuring extrapolation in novel environmental space. Such guidelines are essential to promoting transparency, replicability, and quality control, and will help marine scientists, managers and policy agencies to (i) better interpret density surfaces and their associated uncertainty; (ii) refine model development and selection approaches; and (iii) optimise the allocation of future survey effort by identifying priority knowledge gaps, e.g. by delineating areas where model predictions are the least supported by data. Our review is accompanied by supplementary R code offering a user-friendly framework for quantifying, summarising and visualising various forms of extrapolation in multivariate environmental space a priori (ahead of model fitting). We illustrate its application with case studies designed to revisit previously published predictions of sperm whale (Physeter macrocephalus) and beaked whale (Ziphiidae spp.) densities in the Northwest Atlantic, and evaluate them in light of several extrapolation metrics. Very early in their training, ecologists are given strong warnings against extrapolating, as model predictions made in data-deficient contexts rely heavily on assumptions that may not hold outside the range of sampled conditions. Navigating the ‘uncharted waters’ of extrapolation, however, is critical to scientific progress, and will be best achieved with a clear understanding of the mechanics, benefits, and limitations of extrapolated models.
2019-09-19T10:30:02Z
2019-09-19T10:30:02Z
2019-09-19T10:30:02Z
2019-09-04
Report
Bouchet , P J-F , Miller , D L , Roberts , J , Mannocci , L , Harris , C M & Thomas , L 2019 , From here and now to there and then : practical recommendations for extrapolating cetacean density surface models to novel conditions . CREEM Technical Report , no. 2019-1 , University of St Andrews .
ORCID: /0000-0002-7436-067X/work/61369990
ORCID: /0000-0001-9198-2414/work/61370031
ORCID: /0000-0002-2144-2049/work/61979019
https://hdl.handle.net/10023/18509
eng
CREEM Technical Report
University of St Andrews
oai:research-repository.st-andrews.ac.uk:10023/20482022-04-15T10:30:13Zcom_10023_625com_10023_165com_10023_39com_10023_879com_10023_878col_10023_626col_10023_880
Complex Region Spatial Smoother (CReSS)
Scott Hayward, Lindesay Alexandra Sarah
MacKenzie, Monique Lea
Donovan, Carl Robert
Walker, Cameron
Ashe, Erin
University of St Andrews. School of Biology
University of St Andrews. School of Mathematics and Statistics
University of St Andrews. Statistics
University of St Andrews. Centre for Research into Ecological & Environmental Modelling
University of St Andrews. Scottish Oceans Institute
Geodesic distance
Local radial basis
Thin plate splines
Model averaging
QA Mathematics
Conventional smoothing over complicated coastal and island regions may result in errors across boundaries, due to the use of Euclidean distances to represent inter-point similarity. The new Complex Region Spatial Smoother (CReSS) method presented here, uses estimated geodesic distances, model averaging and a local radial basis function to provide improved smoothing over complex domains. CReSS is compared, via simulation, to recent related smoothing techniques, Thin Plate Splines (TPS, Harder and Desmarais, 1972), geodesic low rank TPS [Wang and Ranalli, 2007] and the Soap film smoother [Wood et al., 2008]. The GLTPS method cannot be used in areas with islands and SOAP can be hard to parameterize. CReSS is comparable with, if not better than, all considered methods on a range of simulations. Supplementary materials for this article are available online.
2011-11-18T15:29:37Z
2011-11-18T15:29:37Z
2011-11-18T15:29:37Z
2011
Report
Scott Hayward , L A S , MacKenzie , M L , Donovan , C R , Walker , C & Ashe , E 2011 , Complex Region Spatial Smoother (CReSS) . CREEM Technical Report , no. 2011-2 , University of St Andrews .
PURE: 15725189
PURE UUID: 662af2ed-5dcd-40fb-b511-a34589876c09
ORCID: /0000-0002-1465-5193/work/68647706
ORCID: /0000-0003-3402-533X/work/73700897
ORCID: /0000-0002-8505-6585/work/74509974
http://hdl.handle.net/10023/2048
eng
CREEM Technical Report
University of St Andrews
oai:research-repository.st-andrews.ac.uk:10023/173282023-04-18T09:37:47Zcom_10023_625com_10023_165com_10023_39com_10023_879com_10023_878col_10023_626col_10023_880
Statistical analysis of SAMBAH survey and associated data
Thomas, Len
Burt, M Louise
University of St Andrews. Statistics
University of St Andrews. School of Mathematics and Statistics
University of St Andrews. Marine Alliance for Science & Technology Scotland
University of St Andrews. Centre for Research into Ecological & Environmental Modelling
University of St Andrews. Scottish Oceans Institute
Passive acoustic monitoring
Passive acoustic density estimation
Population abundance
Harbour porpoise
SAMBAH (Static Acoustic Monitoring of the Baltic Sea Harbour Porpoise) was an international project involving all EU countries around the Baltic Sea, funded by those countries and by the EU Life program (Project Number LIFE08 NAT/S/000261). It ran from 1/1/2010 to 30/9/2015. One major goal of the project was to estimate the abundance of Baltic Sea harbour porpoise, by designing and implementing a large-scale multi-year passive acoustic survey. CREEM was contracted to collaborate on the survey design, and provide statistical analysis of resulting data. A number of internal reports were produced and circulated to the project team, detailing aspects of the analysis. In this CREEM technical report, we collate the most recent version of each of these internal reports as a means of making them publicly available.
2019-03-20T10:30:48Z
2019-03-20T10:30:48Z
2019-03-20T10:30:48Z
2016-03-01
Report
Thomas , L & Burt , M L 2016 , Statistical analysis of SAMBAH survey and associated data . CREEM Technical Report , no. 2016-1 , University of St Andrews .
PURE: 258224564
PURE UUID: 73e93541-808d-435d-8321-c3349a36a3ff
ORCID: /0000-0002-7436-067X/work/55643791
http://hdl.handle.net/10023/17328
eng
CREEM Technical Report
University of St Andrews
oai:research-repository.st-andrews.ac.uk:10023/199092024-03-24T00:46:50Zcom_10023_625com_10023_165com_10023_39com_10023_879com_10023_878col_10023_626col_10023_880
Simulating cetacean responses to sonar exposure within a Bayesian hierarchical modelling framework : technical report
Bouchet, Phil
Harris, Catriona M
Thomas, Len
University of St Andrews. School of Mathematics and Statistics
University of St Andrews. Centre for Research into Ecological & Environmental Modelling
University of St Andrews. School of Biology
GC Oceanography
QA Mathematics
QH301 Biology
In this report, we present a framework for simulating responses of cetaceans to various military sonar exposure contexts using Bayesian hierarchical modelling. This work was motivated by the need to assess the utility of different types of animal-attached biotelemetry tags in improving our understanding of dose–response relationships. Specifically, we used a Monte Carlo approach to conduct a sensitivity analysis of the effects of uncertainty in acoustic dose measurements (i.e. received sound levels) on the probability of behavioural response. Accompanying R code is available and fully described in a sister document.
2020-05-11T09:30:45Z
2020-05-11T09:30:45Z
2020-05-11T09:30:45Z
2020-04-21
Report
Bouchet , P , Harris , C M & Thomas , L 2020 , Simulating cetacean responses to sonar exposure within a Bayesian hierarchical modelling framework : technical report . CREEM Technical Report , no. 2020-01 , University of St Andrews .
ORCID: /0000-0002-7436-067X/work/72842153
ORCID: /0000-0001-9198-2414/work/72842595
ORCID: /0000-0002-2144-2049/work/72842869
https://hdl.handle.net/10023/19909
eng
CREEM Technical Report
University of St Andrews
oai:research-repository.st-andrews.ac.uk:10023/75232022-04-11T11:30:08Zcom_10023_625com_10023_165com_10023_39com_10023_879com_10023_878col_10023_626col_10023_880
Procedure description : using AUTEC’s hydrophones surrounding a DTAGed whale to obtain localizations
Marques, Tiago A.
Shaeffer, Jessica
Moretti, David
Thomas, Len
University of St Andrews. School of Mathematics and Statistics
University of St Andrews. Scottish Oceans Institute
University of St Andrews. Centre for Research into Ecological & Environmental Modelling
University of St Andrews. Statistics
University of St Andrews. Marine Alliance for Science & Technology Scotland
GC Oceanography
QL Zoology
2015-09-22T10:40:01Z
2015-09-22T10:40:01Z
2015-09-22T10:40:01Z
2015
Report
Marques , T A , Shaeffer , J , Moretti , D & Thomas , L 2015 , Procedure description : using AUTEC’s hydrophones surrounding a DTAGed whale to obtain localizations . CREEM Technical Report , no. 2015-1 , University of St Andrews .
PURE: 218408517
PURE UUID: 92e55068-27c1-4d91-87d4-2f048d7f4608
ORCID: /0000-0002-7436-067X/work/29591672
ORCID: /0000-0002-2581-1972/work/56861278
http://hdl.handle.net/10023/7523
eng
CREEM Technical Report
University of St Andrews
oai:research-repository.st-andrews.ac.uk:10023/7842019-04-01T11:10:18Zcom_10023_625com_10023_165com_10023_39col_10023_626
Comparison of aerial survey methods for estimating abundance of common scoters
Rexstad, Eric
Buckland, Stephen T.
During the month of March, four survey methods were applied to the SPA at Camarthen Bay. WWT staff carried out visual aerial surveys using distance sampling methodology (Camphuysen et al. 2004). Visual shore-based counts were also conducted. Distance measures were not consistently taken by these observers, nor was survey effort equal among the four surveys. Because they are intended to be complete counts without replication within a day, it is not possible to estimate precision of these counts, or assess bias, making comparison with other survey results difficult. Digital still data were collected and processed by APEM Ltd. Digital video imagery were captured and processed by HiDef. This report revision includes 29 March survey data from HiDef not available at the time of the release of our 17 July report.
2009-11-13T15:07:47Z
2009-11-13T15:07:47Z
2009-11-13T15:07:47Z
2009
Report
CREEM technical report ; 2009-01
http://hdl.handle.net/10023/784
en
5 p.
CREEM, University of St Andrews
oai:research-repository.st-andrews.ac.uk:10023/22412023-04-26T00:22:10Zcom_10023_625com_10023_165com_10023_39com_10023_879com_10023_878col_10023_626col_10023_880
A critical review of the literature on population modelling
Cabrelli, Abigail
Harwood, John
Matthiopoulos, Jason
New, Leslie Frances
Thomas, Len
University of St Andrews. Statistics
University of St Andrews. Centre for Research into Ecological & Environmental Modelling
University of St Andrews. School of Biology
University of St Andrews. Scottish Oceans Institute
University of St Andrews. St Andrews Sustainability Institute
University of St Andrews. Marine Alliance for Science & Technology Scotland
Population dynamics models
Acoustic disturbance
Biological significance
Matrix population models
State-space model
QH Natural history
QA Mathematics
SDG 3 - Good Health and Well-being
SDG 14 - Life Below Water
The 2005 report of the National Research Council’s ‘Committee on Characterizing Biologically Significant Marine Mammal Behavior’ proposed a framework, which they called PCAD - Population Consequences of Acoustic Disturbance, that uses a series of transfer functions to link behavioural responses to sound with life functions, vital rates, and population change. The Committee suggested that the best understood transfer functions are those linking vital rates to population change. One of the main aims of this report is to document that understanding. However, we also show how the existing frameworks for modelling the dynamics of marine mammal populations can be extended to include the effects of behavioural responses on vital rates. In Chapter 1 we introduce the central concept of the rate of increase (lambda) of a population, which we believe is the most useful measure of the effects of behavioural responses on the dynamics of a population. If the value of lambda exceeds one, then thepopulation will increase over time; if it is less than one it will decrease. We show how changes in lambda provide a measure of the impact of human activities (such as exploitation, conservation, or disturbance) on a population. We also introduce structured population models, which take account of the fact that all individuals in a population are not identical, and show how the dynamics of different parts of a population can be modelled using a population projection matrix. The mathematical properties of this projection matrix can be used to determine the sensitivity of lambda to small changes in vital rates. Finally, we provide a very brief introduction to the concept of stochasticity, and the use of lambda to predict when (and if) a population might be driven to extinction. Chapter 2 describes how lambda also provides a measure of the Darwinian fitness of the individual members of a population. An individual’s fitness, the contribution it will make to future generations, depends to a large extent on its body condition and on the risks of mortality to which it is exposed. Both of these could be affected by behaviour responses to sound. We also explain current theories about the relationship between an individual’s feeding behaviour and the abundance and distribution of prey, and how this can affect body condition. Chapter 3 provides a more detailed description of how elasticity analysis can be used to investigate the impact of changes in vital rates on lambda . Elasticity analysis is a useful tool for detecting which vital rates are most important in determining the dynamics of a population. However, its value is limited because it does not take account of random variations (stochasticity) and, in theory, it can only predict the effect of small changes in vital rates. Chapter 4 describes the fundamental concept of density dependence: the way in which vital rates change with population size or the availability of resources, such as prey. Not only is density dependence an essential prerequisite for population stability and sustainable use, but the form it takes will also determine how a population responds to behavioural changes. This is because behaviour, and particularly the effect of behavioural change on body condition, plays a central role in many of the mechanistic models of density dependence. Chapters 5 and 6 explore the way in which additional complexities, such as social structure and the way in which populations are distributed in space, can affect the dynamics of populations. Models that account for these complexities behave in a much less predictable way than the relatively simple structured models that form the core of Chapters 1-4. So far, the models of population dynamics that we have reviewed have been deterministic. That is, they have assumed that the only way in which vital rates can vary is in response to a change in abundance, via density dependent mechanisms. In Chapters 7 and 8 we investigate the effect of random variation (stochasticity) on population dynamics. We distinguish the effects of demographic stochasticity, chance variations in the number of animals that die or give birth in a time interval that occur even if vital rates do not vary over time, and environmental stochasticity, which is the result of variations in vital rates across years. Variation in abundance may also occur as a result of environmental change and changes in the ecological community of which a population is a part. The effect of all these sources of variation is to reduce the realised growth rate of a population, and therefore its risk of extinction. In Chapter 9 we consider how the basic population modelling framework described in Chapters 1-8 might be extended to take account of the life functions identified by the NRC Committee. We suggest that these life functions are useful for defining the context in which behavioural responses might affect vital rates, but that they do not need to be modelled explicitly. Removing vital functions from the PCAD framework results in a much simpler structure, which is compatible with existing population modelling frameworks. However, these will have to be extended to allow population states, like body condition, that vary continuously to be modelled. Chapter 10 describes how changes in lambda can be detected. The simple analytical frameworks that are available for this are all vulnerable to the effects of variability that we introduced in Chapter 7. However, there is a framework (state-space and hidden Markov process modelling) that can account for the effects of this variability, and we recommend its use for detecting trends. The additional benefit of this approach is that its use results in a detailed model of the dynamics of the population that is under investigation. Chapter 11 reviews the different model structures that can be used to describe the dynamics of a population, and explains when different forms of population models (e.g. discrete vs. continuous time, deterministic vs. stochastic) are most appropriate. We also discuss how these different frameworks can be extended to account for continuous population states, as recommended in Chapter 8. The final focus is on how state-space models can be fitted to time series of abundance estimates and information on vital rates. Chapter 12 looks at the relevance of the different modelling approaches described in the previous chapters for analysing the potential effects of behavioural responses to sound on population dynamics, particularly the kinds of sounds that may be generated by the oil and gas industry. We conclude that lambda , the population rate of increase, and its variation provides a useful measure of these effects. We also believe that the models used for this purpose will certainly have to account for the effects of variability and density dependence. They will probably also have to account for the effects of social structure and the way in which populations use space. The state-space modelling framework outlined in Chapter 11 can, in principle, be extended to capture all of these features although work on this is still in its infancy.
2012-01-31T16:31:02Z
2012-01-31T16:31:02Z
2012-01-31T16:31:02Z
2009
Report
Cabrelli , A , Harwood , J , Matthiopoulos , J , New , L F & Thomas , L 2009 , A critical review of the literature on population modelling . CREEM Technical Report , no. 2009-2 , University of St Andrews .
PURE: 16729282
PURE UUID: 574d8523-aba0-4bbe-ba90-521b27052e7c
ORCID: /0000-0002-7436-067X/work/29591720
http://hdl.handle.net/10023/2241
eng
CREEM Technical Report
University of St Andrews
oai:research-repository.st-andrews.ac.uk:10023/16522023-04-18T09:36:24Zcom_10023_625com_10023_165com_10023_39com_10023_879com_10023_878col_10023_626col_10023_880
Density estimation implications of increasing ambient noise on beaked whale click detection and classification
Marques, Tiago A.
Ward, Jessica
Jarvis, Susan
Moretti, David
Morrissey, Ronald
DiMarzio, Nancy
Thomas, Len
University of St Andrews. School of Mathematics and Statistics
University of St Andrews. Scottish Oceans Institute
University of St Andrews. Centre for Research into Ecological & Environmental Modelling
University of St Andrews. Marine Alliance for Science & Technology Scotland
QC Physics
GC Oceanography
Acoustic based density estimates are being increasingly used. Usually density estimation methods require one to evaluate the effective survey area of the acoustic sensors, or equivalently estimate the mean detection probability of detecting the animals or cues of interest. This is often done based on an estimated detection function, the probability of detecting an object of interest as a function of covariates, usually distance and additional covariates. If the actual survey data and the data used to estimate a detection function are not collected simultaneously, as in Marques et al. (2009), the estimated detection function might not correspond to the detection process that generated the survey data. This would lead to biaseddensity estimates. Here we evaluate the influence of ambient noise in the detection and classification of beaked whale clicks at the Atlantic Undersea Test and Evaluation Center (AUTEC) hydrophones, to assess if the density estimates reported in Marques et al. (2009) might have been biased. To do so we contaminated a data set with increasing levels of ambient noise, and then estimated the detection function accounting for the noise level as an additional covariate. The results obtained suggest that for the particular results obtained at AUTEC’s deep water hydrophones the influence of ambient noise on the beaked whale’s click detection probability might have been minor, and hence unlikely to have had an impact on density estimates. However, we do not exclude the possibility that the results could be different under other scenarios.
2011-01-05T16:33:05Z
2011-01-05T16:33:05Z
2011-01-05T16:33:05Z
2010
Report
Marques , T A , Ward , J , Jarvis , S , Moretti , D , Morrissey , R , DiMarzio , N & Thomas , L 2010 , Density estimation implications of increasing ambient noise on beaked whale click detection and classification . CREEM Technical Report , no. 2010-1 , University of St Andrews .
PURE: 5009510
PURE UUID: d6e8bd6d-2980-414d-a238-520d0b6be8dd
ORCID: /0000-0002-7436-067X/work/29591716
ORCID: /0000-0002-2581-1972/work/56861270
http://hdl.handle.net/10023/1652
http://creem2.st-andrews.ac.uk/reports/
eng
CREEM Technical Report
University of St Andrews
oai:research-repository.st-andrews.ac.uk:10023/6312019-04-01T11:10:18Zcom_10023_625com_10023_165com_10023_39col_10023_626
Methods for estimating sperm whale abundance from passive acoustic line transect surveys.
Borchers, David L.
Brewer, Ciara
Matthews, Justin
distance sampling
passive acoustics
cetacean
2009-01-14T14:58:47Z
2009-01-14T14:58:47Z
2009-01-14T14:58:47Z
2007
Report
CREEM technical report ; 2007-03
http://hdl.handle.net/10023/631
en
16 p.
CREEM, University of St Andrews
oai:research-repository.st-andrews.ac.uk:10023/20082024-02-28T00:47:04Zcom_10023_625com_10023_165com_10023_39com_10023_879com_10023_878col_10023_626col_10023_880
Comparing pre- and post-construction distributions of long-tailed ducks Clangula hyemalis in and around the Nysted offshore wind farm, Denmark : a quasi-designed experiment accounting for imperfect detection, local surface features and autocorrelation
Petersen, Ib Krag
MacKenzie, Monique Lea
Rexstad, Eric
Wisz, Mary S.
Fox, Anthony D.
University of St Andrews. Centre for Research into Ecological & Environmental Modelling
University of St Andrews. School of Mathematics and Statistics
Aerial surveys
Distance sampling
Environmental impact assessment
Feeding desities
Generalized additive models
Parametric bootstrap
Spatially-adaptive model
Generalized estimating equations
Seaducks
Spatial autocorrelation
Temporal autocorrelation
QA Mathematics
QH Natural history
QL Zoology
SDG 1 - No Poverty
We report a novel technique to model abundance patterns of wintering seaducks in relation to the construction of an offshore wind farm (OWF) based on seven years of aerial survey transect data. Distance sampling was used to estimate seaduck densities adjusted for covariates affecting detection probabilities. A generalized additive model (GAM) generated seaduck densities in sampling units in relation to spatially explicit covariates, using bootstrapping to account for uncertainties in both processes. Generalized estimating equations generated precision measures for the GAM robust to spatial and temporal autocorrelation. Comparison of pre- and post-construction model generated surfaces showed significant reductions in long-tailed duck numbers only within the OWF (despite the fact that the model was uninformed about the OWF location), although the absolute numbers involved were trivial in a flyway population context. This method provides quantification of distributional effects on organisms over a gradient in space and time that offers an alternative to Before-After/Control-Impact designs in environmental impact assessment.
2011-10-03T08:26:16Z
2011-10-03T08:26:16Z
2011-10-03T08:26:16Z
2011
Report
Petersen , I K , MacKenzie , M L , Rexstad , E , Wisz , M S & Fox , A D 2011 , Comparing pre- and post-construction distributions of long-tailed ducks Clangula hyemalis in and around the Nysted offshore wind farm, Denmark : a quasi-designed experiment accounting for imperfect detection, local surface features and autocorrelation . CREEM Technical Report , no. 2011-1 , University of St Andrews .
ORCID: /0000-0002-4323-8161/work/29574870
ORCID: /0000-0002-8505-6585/work/74509961
https://hdl.handle.net/10023/2008
eng
CREEM Technical Report
University of St Andrews