Spatial capture-recapture /:
"Space plays a vital role in virtually all ecological processes (Tilman and Kareiva, 1997; Hanski, 1999; Clobert et al., 2001). The spatial arrangement of habitat can influence movement patterns during dispersal, habitat selection, and survival. The distance between an organism and its competit...
Gespeichert in:
| Weitere Verfasser: | , , , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Boston :
Elsevier,
2013.
|
| Schlagworte: | |
| Online-Zugang: | Volltext Volltext |
| Zusammenfassung: | "Space plays a vital role in virtually all ecological processes (Tilman and Kareiva, 1997; Hanski, 1999; Clobert et al., 2001). The spatial arrangement of habitat can influence movement patterns during dispersal, habitat selection, and survival. The distance between an organism and its competitors and prey can influence activity patterns and foraging behavior. Further, understanding distribution and spatial variation in abundance is necessary in the conservation and management of populations"-- |
| Beschreibung: | 1 online resource |
| Bibliographie: | Includes bibliographical references and index. |
| ISBN: | 9780124071520 012407152X 0128100125 9780128100127 |
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| 245 | 0 | 0 | |a Spatial capture-recapture / |c edited by J. Andrew Royle, Richard B. Chandler, Rahel Sollmann, Beth Gardner. |
| 264 | 1 | |a Boston : |b Elsevier, |c 2013. | |
| 300 | |a 1 online resource | ||
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| 520 | 2 | |a "Space plays a vital role in virtually all ecological processes (Tilman and Kareiva, 1997; Hanski, 1999; Clobert et al., 2001). The spatial arrangement of habitat can influence movement patterns during dispersal, habitat selection, and survival. The distance between an organism and its competitors and prey can influence activity patterns and foraging behavior. Further, understanding distribution and spatial variation in abundance is necessary in the conservation and management of populations"-- |c Provided by publisher | |
| 504 | |a Includes bibliographical references and index. | ||
| 588 | 0 | |a Print version record. | |
| 505 | 0 | |a Half Title; Title Page; Copyright; Contents; Foreword; Preface; Acknowledgments; PART I: Background and Concepts; 1 Introduction; 1.1 The study of populations by capture-recapture; 1.2 Lions and Tigers and Bears, oh my: Genesis of Spatial; 1.2.1 Camera trapping; 1.2.2 DNA sampling; 1.2.3 Acoustic sampling; 1.2.4 Search-encounter methods; 1.3 Capture-Recapture for Modeling Encounter Probability; 1.3.1 Example: Fort Drum bear study; 1.3.2 Inadequacy of non-spatial capture-recapture; 1.4 Historical Context: a Brief Synopsis; 1.4.1 Buffering; 1.4.2 Temporary emigration. | |
| 505 | 8 | |a 1.5 Extension of Closed Population Models1.5.1 Toward spatial explicitness: Efford's formulation; 1.5.2 Abundance as the aggregation of a point process; 1.5.3 The activity center concept; 1.5.4 The state-space; 1.5.5 Abundance and density; 1.6 Characterization of SCR Models; 1.7 Summary and Outlook; 2 Statistical Models and SCR; 2.1 Random Variables and Probability Distributions; 2.1.1 Stochasticity in ecology; 2.1.2 Properties of probability distributions; 2.2 Common Probability Distributions; 2.2.1 The binomial distribution; 2.2.2 The Bernoulli distribution. | |
| 505 | 8 | |a 2.2.3 The multinomial and categorical distributions2.2.4 The Poisson distribution; 2.2.5 The uniform distribution; 2.2.6 Other distributions; 2.3 Statistical Inference and Parameter Estimation; 2.4 Joint, Marginal, and Conditional Distributions; 2.5 Hierarchical Models and Inference; 2.6 Characterization of SCR Models; 2.7 Summary and Outlook; 3 GLMs and Bayesian Analysis; 3.1 GLMs and GLMMs; 3.2 Bayesian Analysis; 3.2.1 Bayes' rule; 3.2.2 Principles of Bayesian inference; 3.2.3 Prior distributions; 3.2.4 Posterior inference; 3.2.5 Small sample inference. | |
| 505 | 8 | |a 3.3 Characterizing Posterior Distributions by MCMC Simulation3.3.1 What goes on under the MCMC hood; 3.3.2 Rules for constructing full conditional distributions; 3.3.3 Metropolis-Hastings algorithm; 3.4 Bayesian Analysis Using the BUGS Language; 3.4.1 Linear regression in WinBUGS; 3.5 Practical Bayesian Analysis and MCMC; 3.5.1 Choice of prior distributions; 3.5.2 Convergence and so forth; 3.5.3 Bayesian confidence intervals; 3.5.4 Estimating functions of parameters; 3.6 Poisson GLMs; 3.6.1 North American breeding bird survey data; 3.6.2 Poisson GLM in WinBUGS. | |
| 505 | 8 | |a 3.6.3 Constructing your own MCMC algorithm3.7 Poisson GLM with Random Effects; 3.8 Binomial GLMs; 3.8.1 Binomial regression; 3.8.2 North American waterfowl banding data; 3.9 Bayesian Model Checking and Selection; 3.9.1 Goodness-of-fit; 3.9.2 Model selection; 3.10 Summary and Outlook; 4 Closed Population Models; 4.1 The Simplest Closed Population Model: Model M0; 4.1.1 The core capture-recapture assumptions; 4.1.2 Conditional likelihood; 4.2 Data Augmentation; 4.2.1 DA links occupancy models and closed population models; 4.2.2 Model M0 in BUGS; 4.2.3 Remarks on data augmentation. | |
| 546 | |a English. | ||
| 650 | 0 | |a Spatial ecology |x Research. | |
| 650 | 0 | |a Spatial behavior in animals |x Research. | |
| 650 | 0 | |a Animal populations |x Mathematical models. | |
| 650 | 0 | |a Biological models. |0 http://id.loc.gov/authorities/subjects/sh85014180 | |
| 650 | 0 | |a Population density. |0 http://id.loc.gov/authorities/subjects/sh85104918 | |
| 650 | 1 | 2 | |a Animals |
| 650 | 1 | 2 | |a Models, Biological |
| 650 | 1 | 2 | |a Population Density |
| 650 | 6 | |a Écologie spatiale |x Recherche. | |
| 650 | 6 | |a Comportement spatial chez les animaux |x Recherche. | |
| 650 | 6 | |a Animaux |x Populations |x Modèles mathématiques. | |
| 650 | 6 | |a Modèles biologiques. | |
| 650 | 6 | |a Population |x Densité. | |
| 650 | 7 | |a population density. |2 aat | |
| 650 | 7 | |a SCIENCE |x Life Sciences |x Zoology |x General. |2 bisacsh | |
| 650 | 7 | |a Population density |2 fast | |
| 650 | 7 | |a Biological models |2 fast | |
| 650 | 7 | |a Animal populations |x Mathematical models |2 fast | |
| 700 | 1 | |a Royle, J. Andrew, |e editor. | |
| 700 | 1 | |a Chandler, Richard B., |e editor. | |
| 700 | 1 | |a Sollmann, Rahel, |e editor. | |
| 700 | 1 | |a Gardner, Beth, |e editor |1 http://www.wikidata.org/entity/Q88550011 | |
| 758 | |i has work: |a Spatial capture-recapture (Text) |1 https://id.oclc.org/worldcat/entity/E39PCFTcrCyCFfJJcjDQ8WV4hd |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
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Datensatz im Suchindex
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|---|---|
| _version_ | 1816882243932520448 |
| adam_text | |
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| author2 | Royle, J. Andrew Chandler, Richard B. Sollmann, Rahel Gardner, Beth |
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| author_facet | Royle, J. Andrew Chandler, Richard B. Sollmann, Rahel Gardner, Beth |
| building | Verbundindex |
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| callnumber-first | Q - Science |
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| callnumber-raw | QH541.15.S62 S58 2013eb |
| callnumber-search | QH541.15.S62 S58 2013eb |
| callnumber-sort | QH 3541.15 S62 S58 42013EB |
| callnumber-subject | QH - Natural History and Biology |
| collection | ZDB-4-EBA |
| contents | Half Title; Title Page; Copyright; Contents; Foreword; Preface; Acknowledgments; PART I: Background and Concepts; 1 Introduction; 1.1 The study of populations by capture-recapture; 1.2 Lions and Tigers and Bears, oh my: Genesis of Spatial; 1.2.1 Camera trapping; 1.2.2 DNA sampling; 1.2.3 Acoustic sampling; 1.2.4 Search-encounter methods; 1.3 Capture-Recapture for Modeling Encounter Probability; 1.3.1 Example: Fort Drum bear study; 1.3.2 Inadequacy of non-spatial capture-recapture; 1.4 Historical Context: a Brief Synopsis; 1.4.1 Buffering; 1.4.2 Temporary emigration. 1.5 Extension of Closed Population Models1.5.1 Toward spatial explicitness: Efford's formulation; 1.5.2 Abundance as the aggregation of a point process; 1.5.3 The activity center concept; 1.5.4 The state-space; 1.5.5 Abundance and density; 1.6 Characterization of SCR Models; 1.7 Summary and Outlook; 2 Statistical Models and SCR; 2.1 Random Variables and Probability Distributions; 2.1.1 Stochasticity in ecology; 2.1.2 Properties of probability distributions; 2.2 Common Probability Distributions; 2.2.1 The binomial distribution; 2.2.2 The Bernoulli distribution. 2.2.3 The multinomial and categorical distributions2.2.4 The Poisson distribution; 2.2.5 The uniform distribution; 2.2.6 Other distributions; 2.3 Statistical Inference and Parameter Estimation; 2.4 Joint, Marginal, and Conditional Distributions; 2.5 Hierarchical Models and Inference; 2.6 Characterization of SCR Models; 2.7 Summary and Outlook; 3 GLMs and Bayesian Analysis; 3.1 GLMs and GLMMs; 3.2 Bayesian Analysis; 3.2.1 Bayes' rule; 3.2.2 Principles of Bayesian inference; 3.2.3 Prior distributions; 3.2.4 Posterior inference; 3.2.5 Small sample inference. 3.3 Characterizing Posterior Distributions by MCMC Simulation3.3.1 What goes on under the MCMC hood; 3.3.2 Rules for constructing full conditional distributions; 3.3.3 Metropolis-Hastings algorithm; 3.4 Bayesian Analysis Using the BUGS Language; 3.4.1 Linear regression in WinBUGS; 3.5 Practical Bayesian Analysis and MCMC; 3.5.1 Choice of prior distributions; 3.5.2 Convergence and so forth; 3.5.3 Bayesian confidence intervals; 3.5.4 Estimating functions of parameters; 3.6 Poisson GLMs; 3.6.1 North American breeding bird survey data; 3.6.2 Poisson GLM in WinBUGS. 3.6.3 Constructing your own MCMC algorithm3.7 Poisson GLM with Random Effects; 3.8 Binomial GLMs; 3.8.1 Binomial regression; 3.8.2 North American waterfowl banding data; 3.9 Bayesian Model Checking and Selection; 3.9.1 Goodness-of-fit; 3.9.2 Model selection; 3.10 Summary and Outlook; 4 Closed Population Models; 4.1 The Simplest Closed Population Model: Model M0; 4.1.1 The core capture-recapture assumptions; 4.1.2 Conditional likelihood; 4.2 Data Augmentation; 4.2.1 DA links occupancy models and closed population models; 4.2.2 Model M0 in BUGS; 4.2.3 Remarks on data augmentation. |
| ctrlnum | (OCoLC)857769680 |
| dewey-full | 591.56/6 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 591 - Specific topics in natural history of animals |
| dewey-raw | 591.56/6 |
| dewey-search | 591.56/6 |
| dewey-sort | 3591.56 16 |
| dewey-tens | 590 - Animals |
| discipline | Biologie |
| format | Electronic eBook |
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Chandler, Rahel Sollmann, Beth Gardner.</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Boston :</subfield><subfield code="b">Elsevier,</subfield><subfield code="c">2013.</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 online resource</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">computer</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">online resource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="347" ind1=" " ind2=" "><subfield code="a">text file</subfield></datafield><datafield tag="520" ind1="2" ind2=" "><subfield code="a">"Space plays a vital role in virtually all ecological processes (Tilman and Kareiva, 1997; Hanski, 1999; Clobert et al., 2001). 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| id | ZDB-4-EBA-ocn857769680 |
| illustrated | Not Illustrated |
| indexdate | 2024-11-27T13:25:31Z |
| institution | BVB |
| isbn | 9780124071520 012407152X 0128100125 9780128100127 |
| language | English |
| oclc_num | 857769680 |
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| physical | 1 online resource |
| psigel | ZDB-4-EBA |
| publishDate | 2013 |
| publishDateSearch | 2013 |
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| publisher | Elsevier, |
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| spelling | Spatial capture-recapture / edited by J. Andrew Royle, Richard B. Chandler, Rahel Sollmann, Beth Gardner. Boston : Elsevier, 2013. 1 online resource text txt rdacontent computer c rdamedia online resource cr rdacarrier text file "Space plays a vital role in virtually all ecological processes (Tilman and Kareiva, 1997; Hanski, 1999; Clobert et al., 2001). The spatial arrangement of habitat can influence movement patterns during dispersal, habitat selection, and survival. The distance between an organism and its competitors and prey can influence activity patterns and foraging behavior. Further, understanding distribution and spatial variation in abundance is necessary in the conservation and management of populations"-- Provided by publisher Includes bibliographical references and index. Print version record. Half Title; Title Page; Copyright; Contents; Foreword; Preface; Acknowledgments; PART I: Background and Concepts; 1 Introduction; 1.1 The study of populations by capture-recapture; 1.2 Lions and Tigers and Bears, oh my: Genesis of Spatial; 1.2.1 Camera trapping; 1.2.2 DNA sampling; 1.2.3 Acoustic sampling; 1.2.4 Search-encounter methods; 1.3 Capture-Recapture for Modeling Encounter Probability; 1.3.1 Example: Fort Drum bear study; 1.3.2 Inadequacy of non-spatial capture-recapture; 1.4 Historical Context: a Brief Synopsis; 1.4.1 Buffering; 1.4.2 Temporary emigration. 1.5 Extension of Closed Population Models1.5.1 Toward spatial explicitness: Efford's formulation; 1.5.2 Abundance as the aggregation of a point process; 1.5.3 The activity center concept; 1.5.4 The state-space; 1.5.5 Abundance and density; 1.6 Characterization of SCR Models; 1.7 Summary and Outlook; 2 Statistical Models and SCR; 2.1 Random Variables and Probability Distributions; 2.1.1 Stochasticity in ecology; 2.1.2 Properties of probability distributions; 2.2 Common Probability Distributions; 2.2.1 The binomial distribution; 2.2.2 The Bernoulli distribution. 2.2.3 The multinomial and categorical distributions2.2.4 The Poisson distribution; 2.2.5 The uniform distribution; 2.2.6 Other distributions; 2.3 Statistical Inference and Parameter Estimation; 2.4 Joint, Marginal, and Conditional Distributions; 2.5 Hierarchical Models and Inference; 2.6 Characterization of SCR Models; 2.7 Summary and Outlook; 3 GLMs and Bayesian Analysis; 3.1 GLMs and GLMMs; 3.2 Bayesian Analysis; 3.2.1 Bayes' rule; 3.2.2 Principles of Bayesian inference; 3.2.3 Prior distributions; 3.2.4 Posterior inference; 3.2.5 Small sample inference. 3.3 Characterizing Posterior Distributions by MCMC Simulation3.3.1 What goes on under the MCMC hood; 3.3.2 Rules for constructing full conditional distributions; 3.3.3 Metropolis-Hastings algorithm; 3.4 Bayesian Analysis Using the BUGS Language; 3.4.1 Linear regression in WinBUGS; 3.5 Practical Bayesian Analysis and MCMC; 3.5.1 Choice of prior distributions; 3.5.2 Convergence and so forth; 3.5.3 Bayesian confidence intervals; 3.5.4 Estimating functions of parameters; 3.6 Poisson GLMs; 3.6.1 North American breeding bird survey data; 3.6.2 Poisson GLM in WinBUGS. 3.6.3 Constructing your own MCMC algorithm3.7 Poisson GLM with Random Effects; 3.8 Binomial GLMs; 3.8.1 Binomial regression; 3.8.2 North American waterfowl banding data; 3.9 Bayesian Model Checking and Selection; 3.9.1 Goodness-of-fit; 3.9.2 Model selection; 3.10 Summary and Outlook; 4 Closed Population Models; 4.1 The Simplest Closed Population Model: Model M0; 4.1.1 The core capture-recapture assumptions; 4.1.2 Conditional likelihood; 4.2 Data Augmentation; 4.2.1 DA links occupancy models and closed population models; 4.2.2 Model M0 in BUGS; 4.2.3 Remarks on data augmentation. English. Spatial ecology Research. Spatial behavior in animals Research. Animal populations Mathematical models. Biological models. http://id.loc.gov/authorities/subjects/sh85014180 Population density. http://id.loc.gov/authorities/subjects/sh85104918 Animals Models, Biological Population Density Écologie spatiale Recherche. Comportement spatial chez les animaux Recherche. Animaux Populations Modèles mathématiques. Modèles biologiques. Population Densité. population density. aat SCIENCE Life Sciences Zoology General. bisacsh Population density fast Biological models fast Animal populations Mathematical models fast Royle, J. Andrew, editor. Chandler, Richard B., editor. Sollmann, Rahel, editor. Gardner, Beth, editor http://www.wikidata.org/entity/Q88550011 has work: Spatial capture-recapture (Text) https://id.oclc.org/worldcat/entity/E39PCFTcrCyCFfJJcjDQ8WV4hd https://id.oclc.org/worldcat/ontology/hasWork Print version: Spatial capture-recapture 9780124059399 (DLC) 2013030684 (OCoLC)855779243 FWS01 ZDB-4-EBA FWS_PDA_EBA https://www.sciencedirect.com/science/book/9780124059399 Volltext FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=501310 Volltext |
| spellingShingle | Spatial capture-recapture / Half Title; Title Page; Copyright; Contents; Foreword; Preface; Acknowledgments; PART I: Background and Concepts; 1 Introduction; 1.1 The study of populations by capture-recapture; 1.2 Lions and Tigers and Bears, oh my: Genesis of Spatial; 1.2.1 Camera trapping; 1.2.2 DNA sampling; 1.2.3 Acoustic sampling; 1.2.4 Search-encounter methods; 1.3 Capture-Recapture for Modeling Encounter Probability; 1.3.1 Example: Fort Drum bear study; 1.3.2 Inadequacy of non-spatial capture-recapture; 1.4 Historical Context: a Brief Synopsis; 1.4.1 Buffering; 1.4.2 Temporary emigration. 1.5 Extension of Closed Population Models1.5.1 Toward spatial explicitness: Efford's formulation; 1.5.2 Abundance as the aggregation of a point process; 1.5.3 The activity center concept; 1.5.4 The state-space; 1.5.5 Abundance and density; 1.6 Characterization of SCR Models; 1.7 Summary and Outlook; 2 Statistical Models and SCR; 2.1 Random Variables and Probability Distributions; 2.1.1 Stochasticity in ecology; 2.1.2 Properties of probability distributions; 2.2 Common Probability Distributions; 2.2.1 The binomial distribution; 2.2.2 The Bernoulli distribution. 2.2.3 The multinomial and categorical distributions2.2.4 The Poisson distribution; 2.2.5 The uniform distribution; 2.2.6 Other distributions; 2.3 Statistical Inference and Parameter Estimation; 2.4 Joint, Marginal, and Conditional Distributions; 2.5 Hierarchical Models and Inference; 2.6 Characterization of SCR Models; 2.7 Summary and Outlook; 3 GLMs and Bayesian Analysis; 3.1 GLMs and GLMMs; 3.2 Bayesian Analysis; 3.2.1 Bayes' rule; 3.2.2 Principles of Bayesian inference; 3.2.3 Prior distributions; 3.2.4 Posterior inference; 3.2.5 Small sample inference. 3.3 Characterizing Posterior Distributions by MCMC Simulation3.3.1 What goes on under the MCMC hood; 3.3.2 Rules for constructing full conditional distributions; 3.3.3 Metropolis-Hastings algorithm; 3.4 Bayesian Analysis Using the BUGS Language; 3.4.1 Linear regression in WinBUGS; 3.5 Practical Bayesian Analysis and MCMC; 3.5.1 Choice of prior distributions; 3.5.2 Convergence and so forth; 3.5.3 Bayesian confidence intervals; 3.5.4 Estimating functions of parameters; 3.6 Poisson GLMs; 3.6.1 North American breeding bird survey data; 3.6.2 Poisson GLM in WinBUGS. 3.6.3 Constructing your own MCMC algorithm3.7 Poisson GLM with Random Effects; 3.8 Binomial GLMs; 3.8.1 Binomial regression; 3.8.2 North American waterfowl banding data; 3.9 Bayesian Model Checking and Selection; 3.9.1 Goodness-of-fit; 3.9.2 Model selection; 3.10 Summary and Outlook; 4 Closed Population Models; 4.1 The Simplest Closed Population Model: Model M0; 4.1.1 The core capture-recapture assumptions; 4.1.2 Conditional likelihood; 4.2 Data Augmentation; 4.2.1 DA links occupancy models and closed population models; 4.2.2 Model M0 in BUGS; 4.2.3 Remarks on data augmentation. Spatial ecology Research. Spatial behavior in animals Research. Animal populations Mathematical models. Biological models. http://id.loc.gov/authorities/subjects/sh85014180 Population density. http://id.loc.gov/authorities/subjects/sh85104918 Animals Models, Biological Population Density Écologie spatiale Recherche. Comportement spatial chez les animaux Recherche. Animaux Populations Modèles mathématiques. Modèles biologiques. Population Densité. population density. aat SCIENCE Life Sciences Zoology General. bisacsh Population density fast Biological models fast Animal populations Mathematical models fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh85014180 http://id.loc.gov/authorities/subjects/sh85104918 |
| title | Spatial capture-recapture / |
| title_auth | Spatial capture-recapture / |
| title_exact_search | Spatial capture-recapture / |
| title_full | Spatial capture-recapture / edited by J. Andrew Royle, Richard B. Chandler, Rahel Sollmann, Beth Gardner. |
| title_fullStr | Spatial capture-recapture / edited by J. Andrew Royle, Richard B. Chandler, Rahel Sollmann, Beth Gardner. |
| title_full_unstemmed | Spatial capture-recapture / edited by J. Andrew Royle, Richard B. Chandler, Rahel Sollmann, Beth Gardner. |
| title_short | Spatial capture-recapture / |
| title_sort | spatial capture recapture |
| topic | Spatial ecology Research. Spatial behavior in animals Research. Animal populations Mathematical models. Biological models. http://id.loc.gov/authorities/subjects/sh85014180 Population density. http://id.loc.gov/authorities/subjects/sh85104918 Animals Models, Biological Population Density Écologie spatiale Recherche. Comportement spatial chez les animaux Recherche. Animaux Populations Modèles mathématiques. Modèles biologiques. Population Densité. population density. aat SCIENCE Life Sciences Zoology General. bisacsh Population density fast Biological models fast Animal populations Mathematical models fast |
| topic_facet | Spatial ecology Research. Spatial behavior in animals Research. Animal populations Mathematical models. Biological models. Population density. Animals Models, Biological Population Density Écologie spatiale Recherche. Comportement spatial chez les animaux Recherche. Animaux Populations Modèles mathématiques. Modèles biologiques. Population Densité. population density. SCIENCE Life Sciences Zoology General. Population density Biological models Animal populations Mathematical models |
| url | https://www.sciencedirect.com/science/book/9780124059399 https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=501310 |
| work_keys_str_mv | AT roylejandrew spatialcapturerecapture AT chandlerrichardb spatialcapturerecapture AT sollmannrahel spatialcapturerecapture AT gardnerbeth spatialcapturerecapture |