scholarly journals Developing and assessing a density surface model in a Bayesian hierarchical framework with a focus on uncertainty: insights from simulations and an application to fin whales (Balaenoptera physalus)

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e8226
Author(s):  
Douglas B. Sigourney ◽  
Samuel Chavez-Rosales ◽  
Paul B. Conn ◽  
Lance Garrison ◽  
Elizabeth Josephson ◽  
...  
Keyword(s):  
Distance Sampling ◽  
Habitat Model ◽  
Line Transect ◽  
Surface Model ◽  
Two Stage ◽  
Balaenoptera Physalus ◽  
Density Estimates ◽  
Bayesian Hierarchical ◽  
Density Surface ◽  
Fin Whales

Density surface models (DSMs) are an important tool in the conservation and management of cetaceans. Most previous applications of DSMs have adopted a two-step approach to model fitting (hereafter referred to as the Two-Stage Method), whereby detection probabilities are first estimated using distance sampling detection functions and subsequently used as an offset when fitting a density-habitat model. Although variance propagation techniques have recently become available for the Two-Stage Method, most previous applications have not propagated detection probability uncertainty into final density estimates. In this paper, we describe an alternative approach for fitting DSMs based on Bayesian hierarchical inference (hereafter referred to as the Bayesian Method), which is a natural framework for simultaneously propagating multiple sources of uncertainty into final estimates. Our framework includes (1) a mark-recapture distance sampling observation model that can accommodate two team line transect data, (2) an informed prior for the probability a group of animals is at the surface and available for detection (i.e. surface availability) (3) a density-habitat model incorporating spatial smoothers and (4) a flexible compound Poisson-gamma model for count data that incorporates overdispersion and zero-inflation. We evaluate our method and compare its performance to the Two-Stage Method with simulations and an application to line transect data of fin whales (Balaenoptera physalus) off the east coast of the USA. Simulations showed that both methods had low bias (<1.5%) and confidence interval coverage close to the nominal 95% rate when variance was propagated from the first step. Results from the fin whale analysis showed that density estimates and predicted distribution patterns were largely similar among methods; however, the coefficient of variation of the final abundance estimate more than doubled (0.14 vs 0.31) when detection variance was correctly propagated into final estimates. An analysis of the variance components demonstrated that overall detectability as well as surface availability contributed substantial amounts of variance in the final abundance estimates whereas uncertainty in mean group size contributed a negligible amount. Our method provides a Bayesian alternative to DSMs that incorporates much of the flexibility available in the Two-Stage Method. In addition, these results demonstrate the degree to which uncertainty can be underestimated if certain components of a DSM are assumed fixed.

scholarly journals Development of a species distribution model for fin whales (Balaenoptera physalus) within a Bayesian hierarchical framework: Implications for uncertainty

2018 ◽  
Author(s):  
Douglas B Sigourney ◽  
Samuel Chavez-Rosales ◽  
Paul Conn ◽  
Lance Garrison ◽  
Elizabeth Josephson ◽  
...  
Keyword(s):  
Species Distribution ◽  
Species Distribution Model ◽  
Distance Sampling ◽  
Distribution Model ◽  
Balaenoptera Physalus ◽  
Density Estimates ◽  
Bayesian Hierarchical ◽  
Hierarchical Framework ◽  
Final Density ◽  
Fin Whales

Species distribution models (SDMs) have proven to be an integral tool in the conservation and management of cetaceans. Many applications have adopted a two-step approach where a detection function is estimated using conventional distance sampling in the first step and subsequently used as an offset to a density-habitat model in the second step. A drawback to this approach, hereafter referred to as the conventional species distribution model (CSDM), is the difficulty in propagating the uncertainty from the first step to the final density estimates. We describe a Bayesian hierarchical species distribution model (BHSDM) which has the advantage of simultaneously propagating multiple sources of uncertainty. Our framework includes 1) a mark-recapture distance sampling observation model that can accommodate two team line transect data, 2) an informed prior for surface availability 3) spatial smoothers using spline-like bases and 4) a compound Poisson-gamma likelihood which is a special case of the Tweedie distribution. We compare our approach to the CSDM method using a simulation study and a case study of fin whales (Balaenoptera physalus) off the East Coast of the USA. Simulations showed that the BHSDM method produced estimates with lower precision but with confidence interval coverage closer to the nominal 95% rate (94% for the BSHDM vs 85% for the CSDM). Results from the fin whale analysis showed that density estimates and predicted distribution patterns were largely similar among methods. Abundance estimates were also similar though modestly higher for the CSDM (4700, CV=0.13) than the BHSDM (4526, CV=0.26). Estimated sampling error differed substantially among the two methods where the average CV for density estimates from BHSDM method was approximately 3.5 times greater than estimates from the CSDM method. Successful wildlife management hinges on the ability to properly quantify uncertainty. Underestimates of uncertainty can result in ill-informed management decisions. Our results highlight the additional sampling uncertainty that is propagated in a hierarchical framework. Future applications of SDMs should consider techniques that allow all sources of error to be fully represented in final density predictions.

scholarly journals Development of a species distribution model for fin whales (Balaenoptera physalus) within a Bayesian hierarchical framework: Implications for uncertainty

2018 ◽  
Author(s):  
Douglas B Sigourney ◽  
Samuel Chavez-Rosales ◽  
Paul Conn ◽  
Lance Garrison ◽  
Elizabeth Josephson ◽  
...  
Keyword(s):  
Species Distribution ◽  
Species Distribution Model ◽  
Distance Sampling ◽  
Distribution Model ◽  
Balaenoptera Physalus ◽  
Density Estimates ◽  
Bayesian Hierarchical ◽  
Hierarchical Framework ◽  
Final Density ◽  
Fin Whales

Species distribution models (SDMs) have proven to be an integral tool in the conservation and management of cetaceans. Many applications have adopted a two-step approach where a detection function is estimated using conventional distance sampling in the first step and subsequently used as an offset to a density-habitat model in the second step. A drawback to this approach, hereafter referred to as the conventional species distribution model (CSDM), is the difficulty in propagating the uncertainty from the first step to the final density estimates. We describe a Bayesian hierarchical species distribution model (BHSDM) which has the advantage of simultaneously propagating multiple sources of uncertainty. Our framework includes 1) a mark-recapture distance sampling observation model that can accommodate two team line transect data, 2) an informed prior for surface availability 3) spatial smoothers using spline-like bases and 4) a compound Poisson-gamma likelihood which is a special case of the Tweedie distribution. We compare our approach to the CSDM method using a simulation study and a case study of fin whales (Balaenoptera physalus) off the East Coast of the USA. Simulations showed that the BHSDM method produced estimates with lower precision but with confidence interval coverage closer to the nominal 95% rate (94% for the BSHDM vs 85% for the CSDM). Results from the fin whale analysis showed that density estimates and predicted distribution patterns were largely similar among methods. Abundance estimates were also similar though modestly higher for the CSDM (4700, CV=0.13) than the BHSDM (4526, CV=0.26). Estimated sampling error differed substantially among the two methods where the average CV for density estimates from BHSDM method was approximately 3.5 times greater than estimates from the CSDM method. Successful wildlife management hinges on the ability to properly quantify uncertainty. Underestimates of uncertainty can result in ill-informed management decisions. Our results highlight the additional sampling uncertainty that is propagated in a hierarchical framework. Future applications of SDMs should consider techniques that allow all sources of error to be fully represented in final density predictions.

scholarly journals Variance Propagation for Density Surface Models

2021 ◽  
Author(s):  
Mark V. Bravington ◽  
David L. Miller ◽  
Sharon L. Hedley
Keyword(s):  
Random Effect ◽  
Distance Sampling ◽  
Line Transect ◽  
Line Transect Survey ◽  
Surface Models ◽  
On Line ◽  
Density Surface ◽  
Two Stages ◽  
Variance Estimates ◽  
Spatially Varying

AbstractSpatially explicit estimates of population density, together with appropriate estimates of uncertainty, are required in many management contexts. Density surface models (DSMs) are a two-stage approach for estimating spatially varying density from distance sampling data. First, detection probabilities—perhaps depending on covariates—are estimated based on details of individual encounters; next, local densities are estimated using a GAM, by fitting local encounter rates to location and/or spatially varying covariates while allowing for the estimated detectabilities. One criticism of DSMs has been that uncertainty from the two stages is not usually propagated correctly into the final variance estimates. We show how to reformulate a DSM so that the uncertainty in detection probability from the distance sampling stage (regardless of its complexity) is captured as an extra random effect in the GAM stage. In effect, we refit an approximation to the detection function model at the same time as fitting the spatial model. This allows straightforward computation of the overall variance via exactly the same software already needed to fit the GAM. A further extension allows for spatial variation in group size, which can be an important covariate for detectability as well as directly affecting abundance. We illustrate these models using point transect survey data of Island Scrub-Jays on Santa Cruz Island, CA, and harbour porpoise from the SCANS-II line transect survey of European waters. Supplementary materials accompanying this paper appear on-line.

scholarly journals Distribution and abundance of fin whales (Balaenoptera physalus) in the Northeast and Central Atlantic as inferred from the North Atlantic Sightings Surveys 1987-2001

2013 ◽  
Vol 7 ◽  
pp. 49 ◽  
Author(s):  
Gísli A Víkingsson ◽  
Daniel G Pike ◽  
Geneviève Desportes ◽  
Nils Øien ◽  
Thorvaldur Gunnlaugsson ◽  
...  
Keyword(s):  
North Atlantic ◽  
Large Scale ◽  
High Growth ◽  
Line Transect ◽  
Annual Growth Rate ◽  
Survey Period ◽  
Balaenoptera Physalus ◽  
The North ◽  
Fin Whales ◽  
Eastern North Atlantic

North Atlantic Sightings Surveys (NASS) is a series of large scale international cetacean line transect surveys, conducted in 1987, 1989, 1995 and 2001, that covered a large part of the central and eastern North Atlantic. Target species were fin (Balaenoptera physalus), common minke (B. acutorostrata), pilot (Globicephala melas) and sei (B. borealis) whales. Here we present new estimates of abundance for fin whales from the 2 most recent surveys and analysis of trends throughout the survey period. Fin whales were found in highest densities in the Irminger Sea between Iceland and Greenland. Abundance of fin whales in the survey area of the Icelandic and Faroese vessels (Central North Atlantic) was estimated as 19,672 (95% C.I. 12,083-28,986) animals in 1995 and 24,887 (95% C.I. 18,186-30,214) in 2001. The estimates are negatively biased because of whales diving during the passage of vessels, and whales being missed by observers, but these and other potential biases are likely small for this species. The abundance of fin whales increased significantly over the survey period. For all areas combined the estimated annual growth rate was 4%. An estimated annual increase of 10% in the area between Iceland and Greenland was responsible for most of this overall increase in numbers of fin whales in the area. Although high, the estimated rates of increase are not out of bounds of biological plausibility and can thus be viewed as recovery of a depleted population. However, the apparent pattern of population growth and the whaling history in the area indicate that fin whales made a significant recovery during the first half of the 20th century and that the recent observed high growth rates cannot be explained solely by recovery after overexploitation. 

scholarly journals Using encounter rates as surrogates for density estimates makes monitoring of heavily-traded grey parrots achievable across Africa

Oryx ◽  
2015 ◽  
Vol 50 (4) ◽  
pp. 617-625 ◽  
Author(s):  
Stuart J. Marsden ◽  
Emmanuel Loqueh ◽  
Jean Michel Takuo ◽  
John A. Hart ◽  
Robert Abani ◽  
...  
Keyword(s):  
Local Density ◽  
Conservation Status ◽  
Distance Sampling ◽  
Central Africa ◽  
High Mobility ◽  
Encounter Rate ◽  
Line Transect ◽  
African Countries ◽  
Density Estimates ◽  
Encounter Rates

AbstractEstimating population sizes in the heavily traded grey parrots of West and Central Africa would provide insights into conservation status and sustainability of harvests. Ideally, density estimates would be derived from a standardized method such as distance sampling, but survey efforts are hampered by the extensive ranges, patchy distribution, variable abundance, cryptic habits and high mobility of the parrots as well as by logistical difficulties and limited resources. We carried out line transect distance sampling alongside a simpler encounter rate method at 10 sites across five West and Central African countries. Density estimates were variable across sites, from 0–0.5 individuals km−2 in Côte d'Ivoire and central Democratic Republic of the Congo to c. 30 km−2 in Cameroon and > 70 km−2 on the island of Príncipe. Most significantly, we identified the relationship between densities estimated from distance sampling and simple encounter rates, which has important applications in monitoring grey parrots: (1) to convert records of parrot groups encountered in a day's activities by anti-poaching patrols within protected areas into indicative density estimates, (2) to confirm low density in areas where parrots are so rare that distance sampling is not feasible, and (3) to provide a link between anecdotal records and local density estimates. Encounter rates of less than one parrot group per day of walking are a reality in most forests within the species’ ranges. Densities in these areas are expected to be one individual km−2 or lower, and local harvest should be disallowed on this basis.

Double-observer evaluation of pronghorn aerial line-transect surveys

2016 ◽  
Vol 43 (6) ◽  
pp. 474 ◽  
Author(s):  
Timothy J. Smyser ◽  
Richard J. Guenzel ◽  
Christopher N. Jacques ◽  
Edward O. Garton
Keyword(s):  
Cluster Size ◽  
Distance Sampling ◽  
Landscape Composition ◽  
Probability Of Detection ◽  
Line Transect ◽  
Front Seat ◽  
Density Estimates ◽  
Seat Position ◽  
Analysis Techniques ◽  
Double Observer

Context Distance sampling is used to estimate abundance for several taxa, including pronghorn (Antilocapra americana). Comparisons between population estimates derived from quadrat sampling and distance sampling suggest that distance sampling underestimates pronghorn density, likely owing to violations of the critical assumption of distance sampling that all pronghorn within the innermost distance band (A band; nearest to the aircraft) are detected. Aims We sought to rigorously test the assumption that all pronghorn clusters are detected within the innermost distance band by applying a double-observer approach to an established pronghorn aerial-survey protocol. Additionally, we evaluated potential effects of cluster size, landscape composition and seat position (front seat versus rear) on the probability of detection. Methods We conducted aerial line-transect distance-sampling surveys using independent, paired observers and modelled the probability of detection with mark–recapture distance-sampling (MRDS) analysis techniques that explicitly estimate the probability of detection for pronghorn clusters in the innermost distance band. We compared density estimates produced by the MRDS analysis with those produced by multiple-covariate distance sampling (MCDS), a method that assumes complete detection for clusters on the transect line. Key results We identified violations of the assumption that all clusters within the innermost distance band were detected, which would contribute to proportional biases in density estimates for analysis techniques that assume complete detection. The frequency of missed clusters was modest from the front-seat position, with 45 of the 47 (96%) clusters in the A band detected. In contrast, the frequency of missed clusters was more substantial for the rear position, from which 37 of 47 (79%) clusters in the A band were detected. Further, our analysis showed that cluster size and landscape composition were important factors for pronghorn sightability. Conclusions When implementing standard survey methodologies, pronghorn aerial-line transect surveys underestimated population densities. A double-observer survey configuration allowed us to quantify and correct for the bias caused by the failure of observers to detect all pronghorn clusters within the innermost distance band. Implications Population monitoring programs should incorporate double-observer validation trials to quantify the extent of bias owing to undetected clusters within the innermost distance band realised under typical survey conditions. Wildlife managers can improve the precision of pronghorn aerial line-transect surveys by incorporating cluster size and measures of landscape composition and complexity into detection models without incurring additional survey costs.

scholarly journals Estimated Abundances of Cetacean Species in the Northeast Atlantic from Two Multiyear Surveys Conducted by Norwegian Vessels between 2002–2013

2020 ◽  
Vol 11 ◽  
Author(s):  
Deanna Leonard ◽  
Nils Øien
Keyword(s):  
Barents Sea ◽  
Distance Sampling ◽  
Humpback Whales ◽  
Line Transect ◽  
Physeter Macrocephalus ◽  
Sperm Whales ◽  
Independent Observer ◽  
Northeast Atlantic ◽  
Management Procedure ◽  
Fin Whales

Two shipboard line-transect surveys of the Northeast Atlantic were conducted between 2002–2007 and 2008–2013 to meet the ongoing requirements of the Revised Management Procedure (RMP) for common minke whales (Balaenoptera acutorostrata acutorostrata) developed by the International Whaling Commission’s Scientific Committee. Here we present estimated abundances for non-target species for which there were sufficient sightings, including fin whales (Balaenoptera physalus), humpback whales (Megaptera novaeangliae), sperm whales (Physeter macrocephalus), killer whales (Orcinus orca), harbour porpoises (Phocoena phocoena), and dolphins of genus Lagenorhynchus. The 2 surveys were conducted using a multiyear mosaic survey design with 2 independent observer platforms operating in passing mode, each with 2 observers. The abundances of Lagenorhynchus spp. from the 2002–2007 survey were estimated using single-platform standard distance sampling methods because of uncertainty in identifying duplicate sightings. All other estimates were derived using mark-recapture distance sampling techniques applied to a combined-platform dataset of observations, correcting for perception bias. Most notably, we find that the abundance of humpback whales, similar in both survey periods, has doubled since the 1990s with the most striking changes occurring in the Barents Sea. We also show that the pattern in distribution and abundance of fin whales and sperm whales is consistent with our earlier surveys, and that abundances of small odontocete species, which were not estimated in earlier surveys, show stable distributions with some variation in their estimates. Our estimates do not account for distributional shifts between years or correct for biases due to availability or responsive movement.

scholarly journals Fin whales of the Great Bear Rainforest: Balaenoptera physalus velifera in a Canadian Pacific fjord system

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0256815
Author(s):  
Eric M. Keen ◽  
James Pilkington ◽  
Éadin O’Mahony ◽  
Kim-Ly Thompson ◽  
Benjamin Hendricks ◽  
...  
Keyword(s):  
Line Transect ◽  
Balaenoptera Physalus ◽  
Fin Whale ◽  
Resident Population ◽  
Fjord System ◽  
Commercial Whaling ◽  
Dynamics Of Population ◽  
Oceanic Species ◽  
Fin Whales ◽  
Peak Abundance

Fin whales (Balaenoptera physalus) are widely considered an offshore and oceanic species, but certain populations also use coastal areas and semi-enclosed seas. Based upon fifteen years of study, we report that Canadian Pacific fin whales (B. p. velifera) have returned to the Kitimat Fjord System (KFS) in the Great Bear Rainforest, and have established a seasonally resident population in its intracoastal waters. This is the only fjord system along this coast or elsewhere in which fin whales are known to occur regularly with strong site fidelity. The KFS was also the only Canadian Pacific fjord system in which fin whales were commonly found and killed during commercial whaling, pointing to its long-term importance. Traditional knowledge, whaling records, and citizen science databases suggest that fin whales were extirpated from this area prior to their return in 2005–2006. Visual surveys and mark-recapture analysis documented their repopulation of the area, with 100–120 whales using the fjord system in recent years, as well as the establishment of a seasonally resident population with annual return rates higher than 70%. Line transect surveys identified the central and outer channels of the KFS as the primary fin whale habitat, with the greatest densities occurring in Squally Channel and Caamaño Sound. Fin whales were observed in the KFS in most months of the year. Vessel- and shore-based surveys (27,311 km and 6,572 hours of effort, respectively) indicated regular fin whale presence (2,542 detections), including mother-calf pairs, from June to October and peak abundance in late August–early September. Seasonal patterns were variable year-to-year, and several lines of evidence indicated that fin whales arrived and departed from the KFS repeatedly throughout the summer and fall. Additionally, we report on the population’s social network and morphometrics. These findings offer insights into the dynamics of population recovery in an area where several marine shipping projects are proposed. The fin whales of the Great Bear Rainforest represent a rare exception to general patterns in this species’ natural history, and we highlight the importance of their conservation.

scholarly journals Getting the Most From Surveys: How Method Selection and Method Modification Impact Butterfly Survey Data

2021 ◽  
Author(s):  
Katherine C Kral-O’Brien ◽  
Adrienne K Antonsen ◽  
Torre J Hovick ◽  
Ryan F Limb ◽  
Jason P Harmon
Keyword(s):  
Species Richness ◽  
New Species ◽  
Count Data ◽  
Distance Sampling ◽  
Line Transect ◽  
Similar Species ◽  
Method Selection ◽  
M 10 ◽  
Sampling Procedures

Abstract Many methods are used to survey butterfly populations, with line transect and area surveys being prominent. Observers are typically limited to search within 5 or 10 m from the line, while observers are unrestricted in larger specified search regions in area surveys. Although methods differ slightly, the selection is often based on producing defendable data for conservation, maximizing data quality, and minimizing effort. To guide method selection, we compared butterfly surveys using 1) line versus area methods and 2) varying width transects (5 m, 10 m, or unrestricted) using count data from surveys in North Dakota from 2015 to 2018. Between line and area surveys, we detected more individuals with area surveys, even when accounting for effort. However, both methods accumulated new species at similar rates. When comparing transect methodology, we detected nearly 60% more individuals and nine more species when transect width increased from 5 m to unrestricted, despite similar effort across methodology. Overall, we found line surveys slightly less efficient at detecting individuals, but they collected similar species richness to area surveys when accounting for effort. Additionally, line surveys allow the use of unrestricted-width transects with distance sampling procedures, which were more effective at detecting species and individuals while providing a means to correct count data over the same transect length. Methods that reduce effort and accurately depict communities are especially important for conservation when long-term datasets are unavailable.

scholarly journals Evaluating Alternative Flight Plans in Thermal Drone Wildlife Surveys—Simulation Study

2021 ◽  
Vol 13 (6) ◽  
pp. 1102
Author(s):  
Julia Witczuk ◽  
Stanisław Pagacz
Keyword(s):  
Computer Simulations ◽  
Survey Design ◽  
Distance Sampling ◽  
Line Of Sight ◽  
Density Estimates ◽  
Accuracy And Precision ◽  
Survey Costs ◽  
Random Survey ◽  
Density Population

The rapidly developing technology of unmanned aerial vehicles (drones) extends to the availability of aerial surveys for wildlife research and management. However, regulations limiting drone operations to visual line of sight (VLOS) seriously affect the design of surveys, as flight paths must be concentrated within small sampling blocks. Such a design is inferior to spatially unrestricted randomized designs available if operations beyond visual line of sight (BVLOS) are allowed. We used computer simulations to assess whether the VLOS rule affects the accuracy and precision of wildlife density estimates derived from drone collected data. We tested two alternative flight plans (VLOS vs. BVLOS) in simulated surveys of low-, medium- and high-density populations of a hypothetical ungulate species with three levels of effort (one to three repetitions). The population density was estimated using the ratio estimate and distance sampling method. The observed differences in the accuracy and precision of estimates from the VLOS and BVLOS surveys were relatively small and negligible. Only in the case of the low-density population (2 ind./100 ha) surveyed once was the VLOS design inferior to BVLOS, delivering biased and less precise estimates. These results show that while the VLOS regulations complicate survey logistics and interfere with random survey design, the quality of derived estimates does not have to be compromised. We advise testing alternative survey variants with the aid of computer simulations to achieve reliable estimates while minimizing survey costs.

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