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{"title"=>"A hierarchical modeling framework for multiple observer transect surveys", "type"=>"journal", "authors"=>[{"first_name"=>"Paul B.", "last_name"=>"Conn", "scopus_author_id"=>"7101653004"}, {"first_name"=>"Jeffrey L.", "last_name"=>"Laake", "scopus_author_id"=>"24381503900"}, {"first_name"=>"Devin S.", "last_name"=>"Johnson", "scopus_author_id"=>"7406828372"}], "year"=>2012, "source"=>"PLoS ONE", "identifiers"=>{"isbn"=>"1932-6203", "pmid"=>"22905121", "doi"=>"10.1371/journal.pone.0042294", "pui"=>"365400211", "issn"=>"19326203", "sgr"=>"84864723761", "scopus"=>"2-s2.0-84864723761"}, "id"=>"db8059db-37d9-3de1-a64c-c1b9d583de85", "abstract"=>"Ecologists often use multiple observer transect surveys to census animal populations. In addition to animal counts, these surveys produce sequences of detections and non-detections for each observer. When combined with additional data (i.e. covariates such as distance from the transect line), these sequences provide the additional information to estimate absolute abundance when detectability on the transect line is less than one. Although existing analysis approaches for such data have proven extremely useful, they have some limitations. For instance, it is difficult to extrapolate from observed areas to unobserved areas unless a rigorous sampling design is adhered to; it is also difficult to share information across spatial and temporal domains or to accommodate habitat-abundance relationships. In this paper, we introduce a hierarchical modeling framework for multiple observer line transects that removes these limitations. In particular, abundance intensities can be modeled as a function of habitat covariates, making it easier to extrapolate to unsampled areas. Our approach relies on a complete data representation of the state space, where unobserved animals and their covariates are modeled using a reversible jump Markov chain Monte Carlo algorithm. Observer detections are modeled via a bivariate normal distribution on the probit scale, with dependence induced by a distance-dependent correlation parameter. We illustrate performance of our approach with simulated data and on a known population of golf tees. In both cases, we show that our hierarchical modeling approach yields accurate inference about abundance and related parameters. In addition, we obtain accurate inference about population-level covariates (e.g. group size). We recommend that ecologists consider using hierarchical models when analyzing multiple-observer transect data, especially when it is difficult to rigorously follow pre-specified sampling designs. We provide a new R package, hierarchicalDS, to facilitate the building and fitting of these models.", "link"=>"http://www.mendeley.com/research/hierarchical-modeling-framework-multiple-observer-transect-surveys", "reader_count"=>65, "reader_count_by_academic_status"=>{"Unspecified"=>5, "Professor > Associate Professor"=>4, "Researcher"=>25, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>12, "Student > Master"=>12, "Other"=>1, "Student > Bachelor"=>2, "Lecturer"=>1, "Professor"=>1}, "reader_count_by_user_role"=>{"Unspecified"=>5, "Professor > Associate Professor"=>4, "Researcher"=>25, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>12, "Student > Master"=>12, "Other"=>1, "Student > Bachelor"=>2, "Lecturer"=>1, "Professor"=>1}, "reader_count_by_subject_area"=>{"Unspecified"=>7, "Environmental Science"=>18, "Mathematics"=>1, "Agricultural and Biological Sciences"=>37, "Medicine and Dentistry"=>1, "Social Sciences"=>1}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>1}, "Social Sciences"=>{"Social Sciences"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>37}, "Mathematics"=>{"Mathematics"=>1}, "Unspecified"=>{"Unspecified"=>7}, "Environmental Science"=>{"Environmental Science"=>18}}, "reader_count_by_country"=>{"Latvia"=>1, "Argentina"=>1, "United States"=>4, "Japan"=>1, "Brazil"=>1, "United Kingdom"=>1, "South Africa"=>1, "Mexico"=>1, "Spain"=>2, "India"=>2}, "group_count"=>1}

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• http://doi.org/10.1111/2041-210X.12518
• http://doi.org/10.7717/peerj.8226
• http://doi.org/10.1016/j.ecolind.2019.105546
• http://doi.org/10.1371/journal.pone.0136403
• http://doi.org/10.1093/jmammal/gyz017
• http://doi.org/10.1002/ece3.4824
• http://doi.org/10.1890/12-2124.1
• http://doi.org/10.1111/2041-210X.12105
• http://doi.org/10.1111/mms.12502
• http://doi.org/10.1016/j.ecolmodel.2017.05.003
• http://doi.org/10.1002/jwmg.21541
• http://doi.org/10.1007/s13253-014-0167-0
• http://doi.org/10.1111/2041-210X.12127
• http://doi.org/10.1002/ece3.3284
• http://doi.org/10.1111/2041-210X.12333
• http://doi.org/10.1016/j.dsr2.2019.104699
• http://doi.org/10.1111/2041-210X.13026

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• {"files"=>["https://ndownloader.figshare.com/files/594660"], "description"=>"<p>Parameters and data used in the hierarchical model for distance data.</p>", "links"=>[], "tags"=>["Computational biology", "ecology", "mathematics"], "article_id"=>265152, "categories"=>["Mathematics", "Biological Sciences", "Ecology"], "users"=>["Paul B. Conn", "Jeffrey L. Laake", "Devin S. Johnson"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0042294.t001", "stats"=>{"downloads"=>1, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Parameter_and_data_definitions_/265152", "title"=>"Parameter and data definitions.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-08-08 01:25:52"}
• {"files"=>["https://ndownloader.figshare.com/files/594181"], "description"=>"<p>Individual nodes indicate a parameter or vector of parameters, and arrows represent conditional dependence. Notation is defined in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0042294#pone-0042294-t001\" target=\"_blank\">Table 1</a>.</p>", "links"=>[], "tags"=>["acyclic", "graph", "hierarchical"], "article_id"=>264675, "categories"=>["Mathematics", "Biological Sciences", "Ecology"], "users"=>["Paul B. Conn", "Jeffrey L. Laake", "Devin S. Johnson"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0042294.g001", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Directed_acyclic_graph_DAG_of_the_areal_hierarchical_model_for_distance_data_/264675", "title"=>"Directed, acyclic graph (DAG) of the (areal) hierarchical model for distance data.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-08-08 01:17:55"}
• {"files"=>["https://ndownloader.figshare.com/files/594283"], "description"=>"<p>Each symbol represents a different group of golf tees, with dark symbols representing yellow tees and gray symbols representing green tees. Groups that were observed by at least one observer are indicated by solid symbols, while open symbols indicate groups that were never observed. Squares represent tee groups that were exposed above surrounding grass, while triangles represent unexposed groups. Group sizes are indicated by the proportional size of each symbol, with the smallest symbols representing groups of 1 animal, and the largest symbols representing a group of 8 individuals. Transect lines are represented by solid black lines, with dotted lines giving survey area boundaries and demarcating the areas surveyed by each transect. The red line serves as the strata boundary (points north comprise the northern stratum).</p>", "links"=>[], "tags"=>["tee"], "article_id"=>264775, "categories"=>["Mathematics", "Biological Sciences", "Ecology"], "users"=>["Paul B. Conn", "Jeffrey L. Laake", "Devin S. Johnson"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0042294.g003", "stats"=>{"downloads"=>2, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Representation_of_golf_tee_population_/264775", "title"=>"Representation of golf tee population.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-08-08 01:19:35"}
• {"files"=>["https://ndownloader.figshare.com/files/594544"], "description"=>"<p>Kernel density estimates of posterior distributions are in black, while true values are represented by red vertical lines, and estimates from a conventional mark-recapture distance sampling analysis (see Laake and Borchers <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0042294#pone.0042294-Laake1\" target=\"_blank\">[7]</a>) are presented in blue.</p>", "links"=>[], "tags"=>["distributions", "abundance", "tees"], "article_id"=>265039, "categories"=>["Mathematics", "Biological Sciences", "Ecology"], "users"=>["Paul B. Conn", "Jeffrey L. Laake", "Devin S. Johnson"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0042294.g007", "stats"=>{"downloads"=>2, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Posterior_distributions_for_the_abundance_number_of_groups_of_golf_tees_of_different_types_/265039", "title"=>"Posterior distributions for the abundance (number of groups) of golf tees of different types.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-08-08 01:23:59"}
• {"files"=>["https://ndownloader.figshare.com/files/594332"], "description"=>"<p>Bar plots representing the probability mass for group size in the golf tee experiment. Empirical distributions correspond to the actual distribution of group size used in the experiment, while posterior distributions represent estimated posterior predictive distributions obtained after analyzing data with our hierarchical model.</p>", "links"=>[], "tags"=>["posterior", "predictive", "distributions", "tee"], "article_id"=>264824, "categories"=>["Mathematics", "Biological Sciences", "Ecology"], "users"=>["Paul B. Conn", "Jeffrey L. Laake", "Devin S. Johnson"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0042294.g004", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Empirical_and_posterior_predictive_distributions_of_golf_tee_group_size_/264824", "title"=>"Empirical and posterior predictive distributions of golf tee group size.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-08-08 01:20:24"}
• {"files"=>["https://ndownloader.figshare.com/files/594489"], "description"=>"<p>Detection functions for each species are based on mean group sizes for each species (4 and 2, respectively), and are made for observer 2 (who had an intermediate detective ability).</p>", "links"=>[], "tags"=>["detection", "functions", "simulated"], "article_id"=>264980, "categories"=>["Mathematics", "Biological Sciences", "Ecology"], "users"=>["Paul B. Conn", "Jeffrey L. Laake", "Devin S. Johnson"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0042294.g006", "stats"=>{"downloads"=>3, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_True_and_estimated_detection_functions_for_simulated_data_/264980", "title"=>"True and estimated detection functions for simulated data.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-08-08 01:23:00"}
• {"files"=>["https://ndownloader.figshare.com/files/594230"], "description"=>"<p>True abundance is indicated in red, with posterior means and estimated 95% credible intervals for abundance indicated by circles and brackets, respectively. Panel (A) gives results for the simulation with linearly increasing abundance, while panel (B) gives results for the simulation with a quadratic relationship between abundance and a habitat covariate.</p>", "links"=>[], "tags"=>["simulated"], "article_id"=>264725, "categories"=>["Mathematics", "Biological Sciences", "Ecology"], "users"=>["Paul B. Conn", "Jeffrey L. Laake", "Devin S. Johnson"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0042294.g002", "stats"=>{"downloads"=>0, "page_views"=>32, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_True_and_estimated_population_size_for_simulated_data_/264725", "title"=>"True and estimated population size for simulated data.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-08-08 01:18:45"}
• {"files"=>["https://ndownloader.figshare.com/files/594616"], "description"=>"<p>The top panel gives detection probability curves for the set of covariates that maximize observer dependence (observer = 2, group size = 1, exposure = 0, species = “green”). “Individual” specifies detection probability for observer 2 only; “Conditional” gives the probability of detection for observer 2 given that the group was detected by observer 1; “Duplicate” gives the probability of detection by both observers; “Pooled” gives the probability of detection by at least one observer. The bottom panel represents dependence, as summarized by the parameter (see <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0042294#pone.0042294-Buckland3\" target=\"_blank\">[9]</a>) for the same set of covariates.</p>", "links"=>[], "tags"=>["detection", "probability", "dependence", "covariates", "maximizing"], "article_id"=>265111, "categories"=>["Mathematics", "Biological Sciences", "Ecology"], "users"=>["Paul B. Conn", "Jeffrey L. Laake", "Devin S. Johnson"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0042294.g008", "stats"=>{"downloads"=>1, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Implied_detection_probability_and_observer_dependence_for_covariates_maximizing_dependence_/265111", "title"=>"Implied detection probability and observer dependence for covariates maximizing dependence.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-08-08 01:25:11"}
• {"files"=>["https://ndownloader.figshare.com/files/311381"], "description"=>"<div><p>Ecologists often use multiple observer transect surveys to census animal populations. In addition to animal counts, these surveys produce sequences of detections and non-detections for each observer. When combined with additional data (i.e. covariates such as distance from the transect line), these sequences provide the additional information to estimate absolute abundance when detectability on the transect line is less than one. Although existing analysis approaches for such data have proven extremely useful, they have some limitations. For instance, it is difficult to extrapolate from observed areas to unobserved areas unless a rigorous sampling design is adhered to; it is also difficult to share information across spatial and temporal domains or to accommodate habitat-abundance relationships. In this paper, we introduce a hierarchical modeling framework for multiple observer line transects that removes these limitations. In particular, abundance intensities can be modeled as a function of habitat covariates, making it easier to extrapolate to unsampled areas. Our approach relies on a complete data representation of the state space, where unobserved animals and their covariates are modeled using a reversible jump Markov chain Monte Carlo algorithm. Observer detections are modeled via a bivariate normal distribution on the probit scale, with dependence induced by a distance-dependent correlation parameter. We illustrate performance of our approach with simulated data and on a known population of golf tees. In both cases, we show that our hierarchical modeling approach yields accurate inference about abundance and related parameters. In addition, we obtain accurate inference about population-level covariates (e.g. group size). We recommend that ecologists consider using hierarchical models when analyzing multiple-observer transect data, especially when it is difficult to rigorously follow pre-specified sampling designs. We provide a new R package, hierarchicalDS, to facilitate the building and fitting of these models.</p> </div>", "links"=>[], "tags"=>["hierarchical", "modeling", "transect", "surveys"], "article_id"=>121432, "categories"=>["Mathematics", "Biological Sciences", "Ecology"], "users"=>["Paul B. Conn", "Jeffrey L. Laake", "Devin S. Johnson"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0042294", "stats"=>{"downloads"=>18, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/A_Hierarchical_Modeling_Framework_for_Multiple_Observer_Transect_Surveys/121432", "title"=>"A Hierarchical Modeling Framework for Multiple Observer Transect Surveys", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-08-08 00:23:52"}
• {"files"=>["https://ndownloader.figshare.com/files/594391"], "description"=>"<p>Kernel density estimates of marginal posterior distributions are indicated in black, with true values used to simulate data indicated by red, vertical lines. Parameters indexed by “Cov” give covariate parameters, “Det” give detection parameters, “Hab” give habitat parameters, and “N” gives abundance. The first panel (“cor”) gives an estimate of the observer dependence parameter. Species specific parameters are indexed by “sp1” (for species one) or “sp2” (species two).</p>", "links"=>[], "tags"=>["posterior", "distributions", "simulated"], "article_id"=>264890, "categories"=>["Mathematics", "Biological Sciences", "Ecology"], "users"=>["Paul B. Conn", "Jeffrey L. Laake", "Devin S. Johnson"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0042294.g005", "stats"=>{"downloads"=>1, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_True_values_and_estimated_posterior_distributions_for_simulated_data_/264890", "title"=>"True values and estimated posterior distributions for simulated data.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-08-08 01:21:30"}
• {"files"=>["https://ndownloader.figshare.com/files/594681"], "description"=>"<p>Choices, alternative(s), and advantages of the modeling choices we made when analyzing double observer line transect data.</p>", "links"=>[], "tags"=>["choices"], "article_id"=>265182, "categories"=>["Mathematics", "Biological Sciences", "Ecology"], "users"=>["Paul B. Conn", "Jeffrey L. Laake", "Devin S. Johnson"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0042294.t002", "stats"=>{"downloads"=>0, "page_views"=>35, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Modeling_choices_and_justification_/265182", "title"=>"Modeling choices and justification.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-08-08 01:26:22"}

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Relative Metric

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F1000Prime