Detection and Plant Monitoring Programs: Lessons from an Intensive Survey of Asclepias meadii with Five Observers
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{"title"=>"Detection and Plant Monitoring Programs: Lessons from an Intensive Survey of Asclepias meadii with Five Observers", "type"=>"journal", "authors"=>[{"first_name"=>"Helen M.", "last_name"=>"Alexander", "scopus_author_id"=>"7202105268"}, {"first_name"=>"Aaron W.", "last_name"=>"Reed", "scopus_author_id"=>"11239466000"}, {"first_name"=>"W. Dean", "last_name"=>"Kettle", "scopus_author_id"=>"6603132878"}, {"first_name"=>"Norman A.", "last_name"=>"Slade", "scopus_author_id"=>"7005512393"}, {"first_name"=>"Sarah A.", "last_name"=>"Bodbyl Roels", "scopus_author_id"=>"36007075200"}, {"first_name"=>"Cathy D.", "last_name"=>"Collins", "scopus_author_id"=>"34067518400"}, {"first_name"=>"Vaughn", "last_name"=>"Salisbury", "scopus_author_id"=>"8933924300"}], "year"=>2012, "source"=>"PLoS ONE", "identifiers"=>{"doi"=>"10.1371/journal.pone.0052762", "sgr"=>"84871460950", "isbn"=>"1932-6203", "pmid"=>"23285179", "issn"=>"19326203", "scopus"=>"2-s2.0-84871460950", "pui"=>"366319249"}, "id"=>"f088115b-d0da-3b4a-9225-e1d3f8c42a12", "abstract"=>"Monitoring programs, where numbers of individuals are followed through time, are central to conservation. Although incomplete detection is expected with wildlife surveys, this topic is rarely considered with plants. However, if plants are missed in surveys, raw count data can lead to biased estimates of population abundance and vital rates. To illustrate, we had five independent observers survey patches of the rare plant Asclepias meadii at two prairie sites. We analyzed data with two mark-recapture approaches. Using the program CAPTURE, the estimated number of patches equaled the detected number for a burned site, but exceeded detected numbers by 28% for an unburned site. Analyses of detected patches using Huggins models revealed important effects of observer, patch state (flowering/nonflowering), and patch size (number of stems) on probabilities of detection. Although some results were expected (i.e. greater detection of flowering than nonflowering patches), the importance of our approach is the ability to quantify the magnitude of detection problems. We also evaluated the degree to which increased observer numbers improved detection: smaller groups (3-4 observers) generally found 90 - 99% of the patches found by all five people, but pairs of observers or single observers had high error and detection depended on which individuals were involved. We conclude that an intensive study at the start of a long-term monitoring study provides essential information about probabilities of detection and what factors cause plants to be missed. This information can guide development of monitoring programs. Citation: Alexander HM, Reed AW, Kettle WD, Slade NA, Bodbyl Roels SA, et al. (2012) Detection and Plant Monitoring Programs: Lessons from an Intensive Survey of Asclepias meadii with Five Observers. PLoS ONE 7(12): e52762. doi:10.1371/journal.pone.0052762", "link"=>"http://www.mendeley.com/research/detection-plant-monitoring-programs-lessons-intensive-survey-asclepias-meadii-five-observers", "reader_count"=>25, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>1, "Researcher"=>7, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>4, "Student > Postgraduate"=>1, "Other"=>5, "Student > Master"=>4, "Student > Bachelor"=>1, "Lecturer > Senior Lecturer"=>1}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>1, "Researcher"=>7, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>4, "Student > Postgraduate"=>1, "Other"=>5, "Student > Master"=>4, "Student > Bachelor"=>1, "Lecturer > Senior Lecturer"=>1}, "reader_count_by_subject_area"=>{"Unspecified"=>2, "Environmental Science"=>6, "Agricultural and Biological Sciences"=>12, "Medicine and Dentistry"=>2, "Earth and Planetary Sciences"=>3}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>2}, "Earth and Planetary Sciences"=>{"Earth and Planetary Sciences"=>3}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>12}, "Unspecified"=>{"Unspecified"=>2}, "Environmental Science"=>{"Environmental Science"=>6}}, "reader_count_by_country"=>{"Canada"=>1, "United States"=>1, "Australia"=>1, "Spain"=>1}, "group_count"=>3}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/521794"], "description"=>"<p>Numbers of observers per group range from 1 – 5; probabilities shown are <i>p<sub>1</sub>, p<sub>2</sub>, p<sub>3,</sub> p<sub>4,</sub></i> and <i>p<sub>5</sub></i>, defined as the probability that at least one observer in a group of defined size will detect patches; see text). For each group size, probabilities are indicated for four categories (all vs. only nonflowering patches, burned vs. unburned site). For group size 5, a single detection probability was calculated for each category (see <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0052762#pone.0052762.e001\" target=\"_blank\">equation 1</a>). For group sizes 2–4, probabilities of detection are indicated for all combinations of the number of observers (10 combinations for 2 and 3 observers, 5 combinations for 4 observers; see text). For group size 1, five values are shown, corresponding to the observer-specific detection probabilities for the five observers in the actual study. Bars are SE of a common variance.</p>", "links"=>[], "tags"=>["detection", "patches", "observers"], "article_id"=>192285, "categories"=>["Plant Biology"], "users"=>["Helen M. Alexander", "Aaron W. Reed", "W. Dean Kettle", "Norman A. Slade", "Sarah A. Bodbyl Roels", "Cathy D. Collins", "Vaughn Salisbury"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0052762.g002", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Probability_of_detection_of_patches_depending_on_the_number_of_observers_per_group_/192285", "title"=>"Probability of detection of patches depending on the number of observers per group.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-12-20 00:38:05"}
  • {"files"=>["https://ndownloader.figshare.com/files/282290"], "description"=>"<div><p>Monitoring programs, where numbers of individuals are followed through time, are central to conservation. Although incomplete detection is expected with wildlife surveys, this topic is rarely considered with plants. However, if plants are missed in surveys, raw count data can lead to biased estimates of population abundance and vital rates. To illustrate, we had five independent observers survey patches of the rare plant <em>Asclepias meadii</em> at two prairie sites. We analyzed data with two mark-recapture approaches. Using the program CAPTURE, the estimated number of patches equaled the detected number for a burned site, but exceeded detected numbers by 28% for an unburned site. Analyses of detected patches using Huggins models revealed important effects of observer, patch state (flowering/nonflowering), and patch size (number of stems) on probabilities of detection. Although some results were expected (i.e. greater detection of flowering than nonflowering patches), the importance of our approach is the ability to quantify the magnitude of detection problems. We also evaluated the degree to which increased observer numbers improved detection: smaller groups (3–4 observers) generally found 90 – 99% of the patches found by all five people, but pairs of observers or single observers had high error and detection depended on which individuals were involved. We conclude that an intensive study at the start of a long-term monitoring study provides essential information about probabilities of detection and what factors cause plants to be missed. This information can guide development of monitoring programs.</p> </div>", "links"=>[], "tags"=>["detection", "monitoring", "lessons", "intensive", "observers"], "article_id"=>115654, "categories"=>["Cell Biology"], "users"=>["Helen M. Alexander", "Aaron W. Reed", "W. Dean Kettle", "Norman A. Slade", "Sarah A. Bodbyl Roels", "Cathy D. Collins", "Vaughn Salisbury"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0052762", "stats"=>{"downloads"=>0, "page_views"=>30, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Detection_and_Plant_Monitoring_Programs_Lessons_from_an_Intensive_Survey_of_Asclepias_meadii_with_Five_Observers__/115654", "title"=>"Detection and Plant Monitoring Programs: Lessons from an Intensive Survey of <em>Asclepias meadii</em> with Five Observers", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-12-20 01:34:14"}
  • {"files"=>["https://ndownloader.figshare.com/files/521848"], "description"=>"<p>The four best fitting models are shown (M1–M4), with model 1 having the lowest AIC<sub>c</sub> value and thus the best fit. Dev describes the fit of the model, k is the number of parameters and <i>w<sub>i</sub></i> refers to the weighting factor. A dash indicates whether a model included a term for differentiating probability of detection depending on patch state (flowering vs. nonflowering), patch size (number of stems), patch distance (dist; near or far from observer), site (burned vs. unburned), and observer (obs; individuals A–E). Interaction terms are noted.</p>", "links"=>[], "tags"=>["huggins"], "article_id"=>192339, "categories"=>["Plant Biology"], "users"=>["Helen M. Alexander", "Aaron W. Reed", "W. Dean Kettle", "Norman A. Slade", "Sarah A. Bodbyl Roels", "Cathy D. Collins", "Vaughn Salisbury"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0052762.t001", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparison_of_Huggins_models_/192339", "title"=>"Comparison of Huggins models.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-12-20 00:38:59"}
  • {"files"=>["https://ndownloader.figshare.com/files/521731"], "description"=>"<p>Probabilities were calculated for flowering (filled symbols) and nonflowering (open symbols) patches with 1 (triangle), 2 (circle), 3 (square) and >4 (diamond) stems per patch for a) burned and b) unburned prairie sites. Symbols are offset so that SE values can be examined. Lines connect values for the same patch state and size for different observers.</p>", "links"=>[], "tags"=>["detection", "observers", "calculated", "huggins"], "article_id"=>192220, "categories"=>["Plant Biology"], "users"=>["Helen M. Alexander", "Aaron W. Reed", "W. Dean Kettle", "Norman A. Slade", "Sarah A. Bodbyl Roels", "Cathy D. Collins", "Vaughn Salisbury"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0052762.g001", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Probabilities_of_detection_for_observers_A_8211_E_calculated_from_a_Huggins_model_/192220", "title"=>"Probabilities of detection for observers A – E calculated from a Huggins model.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-12-20 00:37:00"}

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