Estimating koala density from incidental koala sightings in South‐East Queensland, Australia (1997–2013), using a self‐exciting spatio‐temporal point process model
The koala, Phascolarctos cinereus, is an iconic Australian wildlife species facing a rapid decline in South‐East Queensland (SEQLD). For conservation planning, the ability to estimate the size of koala populations is crucial. Systematic surveys are the most common approach to estimate koala populati...
Saved in:
Published in | Ecology and evolution Vol. 11; no. 20; pp. 13805 - 13814 |
---|---|
Main Authors | , , , , |
Format | Journal Article |
Language | English |
Published |
England
John Wiley & Sons, Inc
01.10.2021
John Wiley and Sons Inc Wiley |
Subjects | |
Online Access | Get full text |
Cover
Loading…
Abstract | The koala, Phascolarctos cinereus, is an iconic Australian wildlife species facing a rapid decline in South‐East Queensland (SEQLD). For conservation planning, the ability to estimate the size of koala populations is crucial. Systematic surveys are the most common approach to estimate koala populations but because of their cost they are often restricted to small geographic areas and are conducted infrequently. Public interest and participation in the collection of koala sighting data is increasing in popularity, but such data are generally not used for population estimation. We modeled monthly sightings of koalas reported by members of the public from 1997 to 2013 in SEQLD by developing a self‐exciting spatio‐temporal point process model. This allowed us to account for characteristics that are associated with koala presence (which vary over both space and time) while accounting for detection bias in the koala sighting process and addressing spatial clustering of observations. The density of koalas varied spatially due to the heterogeneous nature of koala habitat in SEQLD, with a mean density of 0.0019 koalas per km2 over the study period. The percentage of land areas with very low densities (0–0.0005 koalas per km2) remained similar throughout the study period representing, on average, 66% of the total study area. The approach described in this paper provides a useful starting point to allow greater use to be made of incidental koala sighting data. We propose that the model presented here could be used to combine systematic koala survey data (which is spatially restricted, but more precise) with koala sighting data (which is incidental and often biased by nature, but often collected over large geographical areas). Our approach could also be adopted for modeling the density of other wildlife species where data is collected in the same manner.
A self‐exciting spatio‐temporal point process model was developed to estimate koala density from observed koala sightings data while accounting for spatio‐temporal detection bias and clustering of observations. An example of koala densities between 1997 and 2006, estimated from koala sightings data is shown below. |
---|---|
AbstractList | Abstract The koala, Phascolarctos cinereus, is an iconic Australian wildlife species facing a rapid decline in South‐East Queensland (SEQLD). For conservation planning, the ability to estimate the size of koala populations is crucial. Systematic surveys are the most common approach to estimate koala populations but because of their cost they are often restricted to small geographic areas and are conducted infrequently. Public interest and participation in the collection of koala sighting data is increasing in popularity, but such data are generally not used for population estimation. We modeled monthly sightings of koalas reported by members of the public from 1997 to 2013 in SEQLD by developing a self‐exciting spatio‐temporal point process model. This allowed us to account for characteristics that are associated with koala presence (which vary over both space and time) while accounting for detection bias in the koala sighting process and addressing spatial clustering of observations. The density of koalas varied spatially due to the heterogeneous nature of koala habitat in SEQLD, with a mean density of 0.0019 koalas per km2 over the study period. The percentage of land areas with very low densities (0–0.0005 koalas per km2) remained similar throughout the study period representing, on average, 66% of the total study area. The approach described in this paper provides a useful starting point to allow greater use to be made of incidental koala sighting data. We propose that the model presented here could be used to combine systematic koala survey data (which is spatially restricted, but more precise) with koala sighting data (which is incidental and often biased by nature, but often collected over large geographical areas). Our approach could also be adopted for modeling the density of other wildlife species where data is collected in the same manner. The koala, Phascolarctos cinereus, is an iconic Australian wildlife species facing a rapid decline in South‐East Queensland (SEQLD). For conservation planning, the ability to estimate the size of koala populations is crucial. Systematic surveys are the most common approach to estimate koala populations but because of their cost they are often restricted to small geographic areas and are conducted infrequently. Public interest and participation in the collection of koala sighting data is increasing in popularity, but such data are generally not used for population estimation. We modeled monthly sightings of koalas reported by members of the public from 1997 to 2013 in SEQLD by developing a self‐exciting spatio‐temporal point process model. This allowed us to account for characteristics that are associated with koala presence (which vary over both space and time) while accounting for detection bias in the koala sighting process and addressing spatial clustering of observations. The density of koalas varied spatially due to the heterogeneous nature of koala habitat in SEQLD, with a mean density of 0.0019 koalas per km2 over the study period. The percentage of land areas with very low densities (0–0.0005 koalas per km2) remained similar throughout the study period representing, on average, 66% of the total study area. The approach described in this paper provides a useful starting point to allow greater use to be made of incidental koala sighting data. We propose that the model presented here could be used to combine systematic koala survey data (which is spatially restricted, but more precise) with koala sighting data (which is incidental and often biased by nature, but often collected over large geographical areas). Our approach could also be adopted for modeling the density of other wildlife species where data is collected in the same manner. A self‐exciting spatio‐temporal point process model was developed to estimate koala density from observed koala sightings data while accounting for spatio‐temporal detection bias and clustering of observations. An example of koala densities between 1997 and 2006, estimated from koala sightings data is shown below. The koala, , is an iconic Australian wildlife species facing a rapid decline in South-East Queensland (SEQLD). For conservation planning, the ability to estimate the size of koala populations is crucial. Systematic surveys are the most common approach to estimate koala populations but because of their cost they are often restricted to small geographic areas and are conducted infrequently. Public interest and participation in the collection of koala sighting data is increasing in popularity, but such data are generally not used for population estimation. We modeled monthly sightings of koalas reported by members of the public from 1997 to 2013 in SEQLD by developing a self-exciting spatio-temporal point process model. This allowed us to account for characteristics that are associated with koala presence (which vary over both space and time) while accounting for detection bias in the koala sighting process and addressing spatial clustering of observations. The density of koalas varied spatially due to the heterogeneous nature of koala habitat in SEQLD, with a mean density of 0.0019 koalas per km over the study period. The percentage of land areas with very low densities (0-0.0005 koalas per km ) remained similar throughout the study period representing, on average, 66% of the total study area. The approach described in this paper provides a useful starting point to allow greater use to be made of incidental koala sighting data. We propose that the model presented here could be used to combine systematic koala survey data (which is spatially restricted, but more precise) with koala sighting data (which is incidental and often biased by nature, but often collected over large geographical areas). Our approach could also be adopted for modeling the density of other wildlife species where data is collected in the same manner. The koala, Phascolarctos cinereus, is an iconic Australian wildlife species facing a rapid decline in South-East Queensland (SEQLD). For conservation planning, the ability to estimate the size of koala populations is crucial. Systematic surveys are the most common approach to estimate koala populations but because of their cost they are often restricted to small geographic areas and are conducted infrequently. Public interest and participation in the collection of koala sighting data is increasing in popularity, but such data are generally not used for population estimation. We modeled monthly sightings of koalas reported by members of the public from 1997 to 2013 in SEQLD by developing a self-exciting spatio-temporal point process model. This allowed us to account for characteristics that are associated with koala presence (which vary over both space and time) while accounting for detection bias in the koala sighting process and addressing spatial clustering of observations. The density of koalas varied spatially due to the heterogeneous nature of koala habitat in SEQLD, with a mean density of 0.0019 koalas per km2 over the study period. The percentage of land areas with very low densities (0-0.0005 koalas per km2) remained similar throughout the study period representing, on average, 66% of the total study area. The approach described in this paper provides a useful starting point to allow greater use to be made of incidental koala sighting data. We propose that the model presented here could be used to combine systematic koala survey data (which is spatially restricted, but more precise) with koala sighting data (which is incidental and often biased by nature, but often collected over large geographical areas). Our approach could also be adopted for modeling the density of other wildlife species where data is collected in the same manner. The koala, Phascolarctos cinereus , is an iconic Australian wildlife species facing a rapid decline in South‐East Queensland (SEQLD). For conservation planning, the ability to estimate the size of koala populations is crucial. Systematic surveys are the most common approach to estimate koala populations but because of their cost they are often restricted to small geographic areas and are conducted infrequently. Public interest and participation in the collection of koala sighting data is increasing in popularity, but such data are generally not used for population estimation. We modeled monthly sightings of koalas reported by members of the public from 1997 to 2013 in SEQLD by developing a self‐exciting spatio‐temporal point process model. This allowed us to account for characteristics that are associated with koala presence (which vary over both space and time) while accounting for detection bias in the koala sighting process and addressing spatial clustering of observations. The density of koalas varied spatially due to the heterogeneous nature of koala habitat in SEQLD, with a mean density of 0.0019 koalas per km 2 over the study period. The percentage of land areas with very low densities (0–0.0005 koalas per km 2 ) remained similar throughout the study period representing, on average, 66% of the total study area. The approach described in this paper provides a useful starting point to allow greater use to be made of incidental koala sighting data. We propose that the model presented here could be used to combine systematic koala survey data (which is spatially restricted, but more precise) with koala sighting data (which is incidental and often biased by nature, but often collected over large geographical areas). Our approach could also be adopted for modeling the density of other wildlife species where data is collected in the same manner. The koala, Phascolarctos cinereus , is an iconic Australian wildlife species facing a rapid decline in South‐East Queensland (SEQLD). For conservation planning, the ability to estimate the size of koala populations is crucial. Systematic surveys are the most common approach to estimate koala populations but because of their cost they are often restricted to small geographic areas and are conducted infrequently. Public interest and participation in the collection of koala sighting data is increasing in popularity, but such data are generally not used for population estimation. We modeled monthly sightings of koalas reported by members of the public from 1997 to 2013 in SEQLD by developing a self‐exciting spatio‐temporal point process model. This allowed us to account for characteristics that are associated with koala presence (which vary over both space and time) while accounting for detection bias in the koala sighting process and addressing spatial clustering of observations. The density of koalas varied spatially due to the heterogeneous nature of koala habitat in SEQLD, with a mean density of 0.0019 koalas per km 2 over the study period. The percentage of land areas with very low densities (0–0.0005 koalas per km 2 ) remained similar throughout the study period representing, on average, 66% of the total study area. The approach described in this paper provides a useful starting point to allow greater use to be made of incidental koala sighting data. We propose that the model presented here could be used to combine systematic koala survey data (which is spatially restricted, but more precise) with koala sighting data (which is incidental and often biased by nature, but often collected over large geographical areas). Our approach could also be adopted for modeling the density of other wildlife species where data is collected in the same manner. A self‐exciting spatio‐temporal point process model was developed to estimate koala density from observed koala sightings data while accounting for spatio‐temporal detection bias and clustering of observations. An example of koala densities between 1997 and 2006, estimated from koala sightings data is shown below. |
Author | Allavena, Rachel Stevenson, Mark Dissanayake, Ravi Bandara Giorgi, Emanuele Henning, Joerg |
AuthorAffiliation | 2 Lancaster Medical School Lancaster University Lancaster UK 3 Faculty of Veterinary and Agricultural Sciences University of Melbourne Parkville Vic. Australia 1 School of Veterinary Science The University of Queensland Gatton Qld Australia |
AuthorAffiliation_xml | – name: 2 Lancaster Medical School Lancaster University Lancaster UK – name: 1 School of Veterinary Science The University of Queensland Gatton Qld Australia – name: 3 Faculty of Veterinary and Agricultural Sciences University of Melbourne Parkville Vic. Australia |
Author_xml | – sequence: 1 givenname: Ravi Bandara orcidid: 0000-0001-8059-9436 surname: Dissanayake fullname: Dissanayake, Ravi Bandara email: r.dissanayake@uq.net.au organization: The University of Queensland – sequence: 2 givenname: Emanuele surname: Giorgi fullname: Giorgi, Emanuele organization: Lancaster University – sequence: 3 givenname: Mark surname: Stevenson fullname: Stevenson, Mark organization: University of Melbourne – sequence: 4 givenname: Rachel surname: Allavena fullname: Allavena, Rachel organization: The University of Queensland – sequence: 5 givenname: Joerg orcidid: 0000-0002-0282-1318 surname: Henning fullname: Henning, Joerg organization: The University of Queensland |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/34707819$$D View this record in MEDLINE/PubMed |
BookMark | eNp1ktFqFDEUhgep2Fp74QtIwJsWum2SSSaTm0JZVi0URNTrkGTO7madmYzJjLp3fQShj9G36pOY2V1LK5ibhHO-85-Tn_My22t9C1n2muAzgjE9Bwv5WYlL-iw7oJjxiRC83Hv03s-OYlzhdApMGRYvsv2cCSxKIg-yu1nsXaN71y7QN69rjSpoo-vXaB58g1xrXQr0ut5lo1ssRzimFPrsh355f_N7pmOPPg2QKmvdVqfocoh90LXT6JhIKe5vbikm-ckpGuLYKMlAPU-F8Mu6TevYpRF8ivTQdD6Vos67tkdd8BZiRI2voH6VPZ_rOsLR7j7Mvr6bfZl-mFx_fH81vbyeWI4lnXBCmDRCWkbBmHnBhCU5WMwFprKwnGlg0hoBBqypCig4FpYKkxc4l2BwfphdbXUrr1eqC8mfsFZeO7UJ-LBQOvTO1qAKAoC1KSSIghFNS25sVWhKc8Zkjm3SuthqdYNpoLLJy_S7J6JPM61bqoX_oUpOOS7HYY53AsF_HyD2qnHRQp2MBj9ERXkpRC5KThL69h905YfQJqtGisqSJihRJ1vKBh9jgPnDMASrcaPUuFFq3KjEvnk8_QP5d38ScL4Ffroa1v9XUrPpLN9I_gHLG9wh |
CitedBy_id | crossref_primary_10_1111_csp2_12874 |
Cites_doi | 10.1071/AM12046 10.1111/j.1365-2664.2007.01407.x 10.1111/ddi.12400 10.1002/joc.1276 10.1046/j.1523-1739.2000.99383.x 10.1007/s00265-009-0761-2 10.1071/WR10156 10.1071/WR08079 10.1016/S0006-3207(01)00233-6 10.1002/ece3.1094 10.1111/geb.12216 10.1071/AM12023 10.1016/j.tree.2010.05.001 10.1016/j.gecco.2021.e01662 10.1071/WR97028 10.7882/AZ.2011.029 10.1038/s41598-019-39917-5 10.1071/WR02031 10.1071/WR19148 10.2193/0022-541X(2006)70[367:MTPORU]2.0.CO;2 10.1071/WR13054 10.1007/s10592-015-0784-3 10.1111/j.1365-2664.2009.01737.x 10.1016/j.biocon.2006.03.021 10.1111/j.1541-0420.2009.01304.x 10.1038/s41598-019-46376-5 10.1071/AM15019 10.1071/AM18006 |
ContentType | Journal Article |
Copyright | 2021 The Authors. published by John Wiley & Sons Ltd. 2021 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. 2021. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
Copyright_xml | – notice: 2021 The Authors. published by John Wiley & Sons Ltd. – notice: 2021 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. – notice: 2021. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
DBID | 24P WIN NPM AAYXX CITATION 3V. 7SN 7SS 7ST 7X2 8FD 8FE 8FH 8FK ABUWG AFKRA ATCPS AZQEC BBNVY BENPR BHPHI C1K CCPQU DWQXO FR3 GNUQQ HCIFZ LK8 M0K M7P P64 PIMPY PQEST PQQKQ PQUKI PRINS RC3 SOI 7X8 5PM DOA |
DOI | 10.1002/ece3.8082 |
DatabaseName | Wiley Open Access Wiley Online Library Free Content PubMed CrossRef ProQuest Central (Corporate) Ecology Abstracts Entomology Abstracts (Full archive) Environment Abstracts Agricultural Science Collection Technology Research Database ProQuest SciTech Collection ProQuest Natural Science Collection ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest Central Agricultural & Environmental Science Collection ProQuest Central Essentials Biological Science Collection ProQuest Central Natural Science Collection Environmental Sciences and Pollution Management ProQuest One Community College ProQuest Central Korea Engineering Research Database ProQuest Central Student SciTech Premium Collection Biological Sciences Agriculture Science Database Biological Science Database Biotechnology and BioEngineering Abstracts Publicly Available Content (ProQuest) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Academic ProQuest One Academic UKI Edition ProQuest Central China Genetics Abstracts Environment Abstracts MEDLINE - Academic PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals |
DatabaseTitle | PubMed CrossRef Agricultural Science Database Publicly Available Content Database ProQuest Central Student Technology Research Database ProQuest Central Essentials ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest Natural Science Collection ProQuest Central China Environmental Sciences and Pollution Management ProQuest Central Genetics Abstracts Natural Science Collection ProQuest Central Korea Agricultural & Environmental Science Collection Biological Science Collection ProQuest Biological Science Collection ProQuest One Academic Eastern Edition Agricultural Science Collection Biological Science Database ProQuest SciTech Collection Ecology Abstracts Biotechnology and BioEngineering Abstracts Entomology Abstracts ProQuest One Academic UKI Edition Engineering Research Database ProQuest One Academic Environment Abstracts ProQuest Central (Alumni) MEDLINE - Academic |
DatabaseTitleList | PubMed MEDLINE - Academic Agricultural Science Database CrossRef |
Database_xml | – sequence: 1 dbid: DOA name: Directory of Open Access Journals(OpenAccess) url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: 24P name: Wiley Open Access url: https://authorservices.wiley.com/open-science/open-access/browse-journals.html sourceTypes: Publisher – sequence: 3 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 4 dbid: BENPR name: ProQuest Central url: https://www.proquest.com/central sourceTypes: Aggregation Database |
DeliveryMethod | fulltext_linktorsrc |
Discipline | Ecology Geography |
DocumentTitleAlternate | DISSANAYAKE et al |
EISSN | 2045-7758 |
EndPage | 13814 |
ExternalDocumentID | oai_doaj_org_article_61ee0ab69e7641a285bcd6a22344930c 10_1002_ece3_8082 34707819 ECE38082 |
Genre | article Journal Article |
GeographicLocations | Queensland Australia Australia |
GeographicLocations_xml | – name: Queensland Australia – name: Australia |
GroupedDBID | 0R~ 1OC 24P 53G 5VS 7X2 8-0 8-1 8FE 8FH AAFWJ AAHBH AAHHS AAZKR ACCFJ ACGFO ACPRK ACXQS ADBBV ADKYN ADRAZ ADZMN ADZOD AEEZP AENEX AEQDE AFKRA AFPKN AFRAH AIAGR AIWBW AJBDE ALAGY ALMA_UNASSIGNED_HOLDINGS ALUQN AOIJS ATCPS AVUZU BAWUL BBNVY BCNDV BENPR BHPHI CCPQU D-8 D-9 DIK EBS ECGQY EJD GODZA GROUPED_DOAJ GX1 HCIFZ HYE IAO IEP KQ8 LK8 M0K M48 M7P M~E OK1 PIMPY PROAC RNS ROL RPM SUPJJ WIN ITC NPM AAYXX CITATION 3V. 7SN 7SS 7ST 8FD 8FK ABUWG AZQEC C1K DWQXO FR3 GNUQQ P64 PQEST PQQKQ PQUKI PRINS RC3 SOI 7X8 5PM |
ID | FETCH-LOGICAL-c5092-51149b79c42ebbf647c13ec0570296c54ae49cb7ebecbd6e6507c27b36039eb03 |
IEDL.DBID | RPM |
ISSN | 2045-7758 |
IngestDate | Tue Oct 22 15:06:26 EDT 2024 Tue Sep 17 21:22:56 EDT 2024 Sat Oct 26 05:48:51 EDT 2024 Thu Oct 10 16:39:14 EDT 2024 Fri Dec 06 02:04:00 EST 2024 Sat Nov 02 12:31:22 EDT 2024 Sat Aug 24 00:59:51 EDT 2024 |
IsDoiOpenAccess | true |
IsOpenAccess | true |
IsPeerReviewed | true |
IsScholarly | true |
Issue | 20 |
Keywords | citizen science modeling koala Queensland population |
Language | English |
License | Attribution 2021 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
LinkModel | DirectLink |
MergedId | FETCHMERGED-LOGICAL-c5092-51149b79c42ebbf647c13ec0570296c54ae49cb7ebecbd6e6507c27b36039eb03 |
Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ORCID | 0000-0001-8059-9436 0000-0002-0282-1318 |
OpenAccessLink | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8525080/ |
PMID | 34707819 |
PQID | 2582982513 |
PQPubID | 2034651 |
PageCount | 10 |
ParticipantIDs | doaj_primary_oai_doaj_org_article_61ee0ab69e7641a285bcd6a22344930c pubmedcentral_primary_oai_pubmedcentral_nih_gov_8525080 proquest_miscellaneous_2587737851 proquest_journals_2582982513 crossref_primary_10_1002_ece3_8082 pubmed_primary_34707819 wiley_primary_10_1002_ece3_8082_ECE38082 |
PublicationCentury | 2000 |
PublicationDate | October 2021 |
PublicationDateYYYYMMDD | 2021-10-01 |
PublicationDate_xml | – month: 10 year: 2021 text: October 2021 |
PublicationDecade | 2020 |
PublicationPlace | England |
PublicationPlace_xml | – name: England – name: Bognor Regis – name: Hoboken |
PublicationTitle | Ecology and evolution |
PublicationTitleAlternate | Ecol Evol |
PublicationYear | 2021 |
Publisher | John Wiley & Sons, Inc John Wiley and Sons Inc Wiley |
Publisher_xml | – name: John Wiley & Sons, Inc – name: John Wiley and Sons Inc – name: Wiley |
References | 2006; 70 2019; 9 2009; 63 2015; 17 2021; 28 2006; 132 2007 2011; 35 2014; 41 2011; 38 2016; 38 2014; 23 1998; 25 2005; 25 2010; 66 2009; 36 2004; 31 2014; 4 2010; 25 2010; 47 2000; 14 2019; 41 2013; 35 2002; 106 2020; 48 2014; 36 2008; 45 2016 2015 2016; 22 e_1_2_9_30_1 e_1_2_9_31_1 e_1_2_9_11_1 e_1_2_9_10_1 e_1_2_9_13_1 e_1_2_9_32_1 e_1_2_9_12_1 e_1_2_9_15_1 e_1_2_9_14_1 e_1_2_9_17_1 e_1_2_9_16_1 e_1_2_9_19_1 e_1_2_9_18_1 e_1_2_9_20_1 e_1_2_9_22_1 e_1_2_9_21_1 Preece H. J. (e_1_2_9_26_1) 2007 e_1_2_9_24_1 e_1_2_9_23_1 e_1_2_9_8_1 e_1_2_9_7_1 Rhodes J. R. (e_1_2_9_27_1) 2015 e_1_2_9_6_1 e_1_2_9_5_1 e_1_2_9_3_1 e_1_2_9_2_1 Baddeley A. (e_1_2_9_4_1) 2016 e_1_2_9_9_1 e_1_2_9_25_1 e_1_2_9_28_1 e_1_2_9_29_1 |
References_xml | – volume: 66 start-page: 347 issue: 2 year: 2010 end-page: 354 article-title: Partial‐likelihood analysis of spatio‐temporal point‐process data publication-title: Biometrics – volume: 25 start-page: 663 issue: 6 year: 1998 end-page: 668 article-title: The spatial and temporal distribution of koala faecal pellets publication-title: Wildlife Research – volume: 38 start-page: 29 issue: 1 year: 2016 end-page: 43 article-title: Interpreting patterns of population change in koalas from long‐term datasets in Coffs Harbour on the north coast of New South Wales publication-title: Australian Mammalogy – volume: 22 start-page: 249 issue: 3 year: 2016 end-page: 262 article-title: Use of expert knowledge to elicit population trends for the koala ( ) publication-title: Diversity and Distributions – volume: 36 start-page: 262 issue: 3 year: 2009 end-page: 273 article-title: Combining a map‐based public survey with an estimation of site occupancy to determine the recent and changing distribution of the koala in New South Wales publication-title: Wildlife Research – volume: 132 start-page: 153 issue: 2 year: 2006 end-page: 165 article-title: The importance of forest area and configuration relative to local habitat factors for conserving forest mammals: A case study of koalas in Queensland, Australia publication-title: Biological Conservation – volume: 9 start-page: 3208 issue: 1 year: 2019 article-title: Automated detection of koalas using low‐level aerial surveillance and machine learning publication-title: Scientific Reports – year: 2007 – volume: 28 year: 2021 article-title: Predicting koala ( ) distribution from incidental sighting data in South‐East Queensland, Australia publication-title: Global Ecology and Conservation – volume: 45 start-page: 549 issue: 2 year: 2008 end-page: 557 article-title: Regional variation in habitat‐occupancy thresholds: A warning for conservation planning publication-title: Journal of Applied Ecology – volume: 41 start-page: 22 issue: 1 year: 2014 article-title: Extinction in Eden: Identifying the role of climate change in the decline of the koala in south‐eastern NSW publication-title: Wildlife Research – volume: 17 start-page: 337 issue: 2 year: 2015 end-page: 353 article-title: Genome‐wide SNP loci reveal novel insights into koala ( ) population variability across its range publication-title: Conservation Genetics – volume: 47 start-page: 5 issue: 1 year: 2010 end-page: 14 article-title: Distance software: Design and analysis of distance sampling surveys for estimating population size publication-title: Journal of Applied Ecology – volume: 23 start-page: 1472 issue: 12 year: 2014 end-page: 1484 article-title: Accounting for imperfect detection and survey bias in statistical analysis of presence‐only data publication-title: Global Ecology and Biogeography – volume: 4 start-page: 2103 issue: 11 year: 2014 end-page: 2114 article-title: Distribution models for koalas in South Australia using citizen science‐collected data publication-title: Ecology and Evolution – volume: 14 start-page: 619 issue: 3 year: 2000 end-page: 628 article-title: Overview, critical assessment, and conservation implications of koala distribution and abundance publication-title: Conservation Biology – year: 2016 – volume: 31 start-page: 109 issue: 2 year: 2004 article-title: Determining the distribution and abundance of a regional koala population in south‐east Queensland for conservation management publication-title: Wildlife Research – volume: 25 start-page: 479 issue: 8 year: 2010 end-page: 486 article-title: Ecological models supporting environmental decision making: A strategy for the future publication-title: Trends in Ecology & Evolution – volume: 38 start-page: 122 issue: 2 year: 2011 article-title: Modelling climate‐change‐induced shifts in the distribution of the koala publication-title: Wildlife Research – volume: 48 start-page: 105 year: 2020 article-title: Comparison of three methods of estimating the population size of an arboreal mammal in a fragmented rural landscape publication-title: Wildlife Research – volume: 35 start-page: 774 year: 2011 end-page: 780 article-title: The Spot Assessment Technique: A tool for determining localised levels of habitat use by koalas publication-title: Australian Zoologist – volume: 35 start-page: 160 issue: 2 year: 2013 end-page: 165 article-title: Koala habitat use and population density: Using field data to test the assumptions of ecological models publication-title: Australian Mammalogy – volume: 9 issue: 1 year: 2019 article-title: The value of long‐term citizen science data for monitoring koala populations publication-title: Scientific Reports – volume: 63 start-page: 1181 issue: 8 year: 2009 end-page: 1188 article-title: Spatiotemporal dynamics of habitat use by koalas: The checkerboard model publication-title: Behavioral Ecology and Sociobiology – volume: 25 start-page: 1965 issue: 15 year: 2005 end-page: 1978 article-title: Very high resolution interpolated climate surfaces for global land areas publication-title: International Journal of Climatology – volume: 106 start-page: 101 issue: 1 year: 2002 end-page: 113 article-title: Modelling mammalian extinction and forecasting recovery: Koalas at Iluka (NSW, Australia) publication-title: Biological Conservation – volume: 70 start-page: 367 issue: 2 year: 2006 end-page: 374 article-title: Modeling the probability of resource use: The effect of, and dealing with, detecting a species imperfectly publication-title: Journal of Wildlife Management – volume: 41 start-page: 157 issue: 1 year: 2019 article-title: Are koalas detected more effectively by systematic spotlighting or diurnal searches? publication-title: Australian Mammalogy – year: 2015 – volume: 36 start-page: 45 issue: 1 year: 2014 end-page: 54 article-title: Ecology and movement of urban koalas adjacent to linear infrastructure in coastal south‐east Queensland publication-title: Australian Mammalogy – ident: e_1_2_9_7_1 doi: 10.1071/AM12046 – ident: e_1_2_9_28_1 doi: 10.1111/j.1365-2664.2007.01407.x – ident: e_1_2_9_3_1 doi: 10.1111/ddi.12400 – ident: e_1_2_9_16_1 doi: 10.1002/joc.1276 – ident: e_1_2_9_24_1 doi: 10.1046/j.1523-1739.2000.99383.x – volume-title: South East Queensland koala population modelling study year: 2015 ident: e_1_2_9_27_1 contributor: fullname: Rhodes J. R. – ident: e_1_2_9_14_1 doi: 10.1007/s00265-009-0761-2 – ident: e_1_2_9_2_1 doi: 10.1071/WR10156 – ident: e_1_2_9_18_1 doi: 10.1071/WR08079 – ident: e_1_2_9_19_1 doi: 10.1016/S0006-3207(01)00233-6 – ident: e_1_2_9_30_1 doi: 10.1002/ece3.1094 – ident: e_1_2_9_12_1 doi: 10.1111/geb.12216 – ident: e_1_2_9_13_1 doi: 10.1071/AM12023 – ident: e_1_2_9_29_1 doi: 10.1016/j.tree.2010.05.001 – volume-title: Spatial point patterns: Methodology and applications with R (Interdisciplinary statistics) year: 2016 ident: e_1_2_9_4_1 contributor: fullname: Baddeley A. – ident: e_1_2_9_11_1 doi: 10.1016/j.gecco.2021.e01662 – ident: e_1_2_9_15_1 doi: 10.1071/WR97028 – ident: e_1_2_9_25_1 doi: 10.7882/AZ.2011.029 – ident: e_1_2_9_5_1 doi: 10.1038/s41598-019-39917-5 – ident: e_1_2_9_9_1 doi: 10.1071/WR02031 – ident: e_1_2_9_6_1 doi: 10.1071/WR19148 – ident: e_1_2_9_22_1 doi: 10.2193/0022-541X(2006)70[367:MTPORU]2.0.CO;2 – ident: e_1_2_9_21_1 doi: 10.1071/WR13054 – ident: e_1_2_9_17_1 doi: 10.1007/s10592-015-0784-3 – ident: e_1_2_9_31_1 doi: 10.1111/j.1365-2664.2009.01737.x – ident: e_1_2_9_23_1 doi: 10.1016/j.biocon.2006.03.021 – ident: e_1_2_9_8_1 doi: 10.1111/j.1541-0420.2009.01304.x – ident: e_1_2_9_10_1 doi: 10.1038/s41598-019-46376-5 – ident: e_1_2_9_20_1 doi: 10.1071/AM15019 – ident: e_1_2_9_32_1 doi: 10.1071/AM18006 – volume-title: Paper presented at the MODSIM 2007: International Congress on Modelling and Simulation year: 2007 ident: e_1_2_9_26_1 contributor: fullname: Preece H. J. |
SSID | ssj0000602407 |
Score | 2.304394 |
Snippet | The koala, Phascolarctos cinereus, is an iconic Australian wildlife species facing a rapid decline in South‐East Queensland (SEQLD). For conservation planning,... The koala, , is an iconic Australian wildlife species facing a rapid decline in South-East Queensland (SEQLD). For conservation planning, the ability to... The koala, Phascolarctos cinereus , is an iconic Australian wildlife species facing a rapid decline in South‐East Queensland (SEQLD). For conservation... The koala, Phascolarctos cinereus, is an iconic Australian wildlife species facing a rapid decline in South-East Queensland (SEQLD). For conservation planning,... Abstract The koala, Phascolarctos cinereus, is an iconic Australian wildlife species facing a rapid decline in South‐East Queensland (SEQLD). For conservation... |
SourceID | doaj pubmedcentral proquest crossref pubmed wiley |
SourceType | Open Website Open Access Repository Aggregation Database Index Database Publisher |
StartPage | 13805 |
SubjectTerms | citizen science Clustering Data collection Datasets Environmental protection Geography koala modeling Original Research Phascolarctos cinereus Polls & surveys population Population decline Population density Populations Public concern Public participation Queensland Wildlife Wildlife conservation Wildlife habitats |
SummonAdditionalLinks | – databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1baxQxFA5SEHwR745WieJDhY7NJplcHrVMKT4IgoW-DbmcqYvL7OJOoX3rTyj4M_xX_SXmZGaXXVR88W1I5pLJOZnzncyXL4S8MTpY3cpYKsNiKbXzpXMulgGcM22K4Boy2-KTOj6RH0-r042tvpATNsgDDx13oCYAzHllQSs5cdxUPkTlUlST0goW8teX8Y1kavgGo3aXXkkJMX4AAcQ7wwzfCkBZp_9P4PJ3juQmds3B5-geuTuiRvp-aO19cgu6B-R2nRWnLx-Sn3UaqAg9uzP6be5mjkbkpfeXFFePUJxOj3nZ41i7xIwcp8hTFc2b6N1cXddu2dPPOa9FtuM-XU-D0D3kidxc_UhhXLzdp8iVP6PpNjBr04VwEab50cvMzk4lo97VjC7m066ni2ExAs277jwiJ0f1l8PjctyFoQwJTKRMNWVM1msbJAfvWyV1mAgICecxblWopANpg9foDT4qSJBPB669UExY8Ew8JjvdvIOnhLKotY1RADNOatZa2fIYdZswW2AwqQryemWaZjGIbTSDrDJv0H4N2q8gH9Bo6xNQHzsXJK9pRq9p_uU1BdldmbwZB-2y4ZXhFpfyioK8Wlen4Yb_UFwH8_N8jtZCJ5xakCeDh6xbImSWTrIF0Vu-s9XU7Zpu-jVLehv8u2xYQfayl_397Zv6sBZ48Ox_dMNzcocjRyeTE3fJTv_9HF4kkNX7l3k8_QJyHCql priority: 102 providerName: Directory of Open Access Journals – databaseName: ProQuest Central dbid: BENPR link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV3LbhMxFLUgFaIbBOU1UJBBLIrUoY7t-LFCtJqqYlEBolJ3I78mjYhm0iaVyK6fgMRn8Ff9EnwdJxDx2I3G8_Do3js-vj4-F6FXSjotG-5LoYgvuTS2NMb40gVjVBNHcBkS2-JYHJ3w96eD05xwm2Za5fKfmH7UvnOQI9-jA0U17LNkbyfnJVSNgtXVXELjJtqgfaZUD23sV8cfPq2yLESAhpdcSgoRuhdcYG8UUXRtIEp6_X8DmX9yJX_HsGkQOryL7mT0iN8tzH0P3QjtFrpVJeXp-Ra6nUuan83vox9VDF6Ao-0Qf-nM2GAPXPXZHMOOEgwpdp-2QubWKczSIW0em3AqrHd99a0y0xn-mOa6wIDcxavUCN4B7sj11fc4tLPXuxj480McHxPGTbwxfHWj9OppYmzHM1kDa4wn3aid4cligwJOlXgeoJPD6vPBUZkrM5QuAow4e42zKG2ldpwGaxvBpeuz4CL2I1QLN-AmcO2sBA-xXoQIA6Wj0jJBmA6WsIeo13ZteIww8VJq71kgynBJGs0b6r1sIo5zJPQHBXq5NFM9WQhw1AupZVqDLWuwZYH2wYCrC0AzO53oLoZ1DsFa9EMgxgodpOB9Q9XAOi9MxEeca0ZcgbaX5q9zIE_rX25XoBer5hiCsK5i2tBdpmukZDJi1wI9WnjLqieMJzklXSC55kdrXV1vaUdnSeZbwYqzIgXaSR7376-vq4OKwcGT_3_BU7RJgZGTqIjbqDe7uAzPIqSa2ec5bn4CtcMntw priority: 102 providerName: ProQuest – databaseName: Scholars Portal Open Access Journals dbid: M48 link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV3datRAFD7UiuiNaP2LVhnFiwpNnc1MMpkLES0pRVAQXOhdmL9sF5dk3U2he9dHEHwM36pP4pxJsnSxerdkkmySc07OdybffAfgdS6MFBW3cZZTG3OhdKyUsrFxSuWVz-DCBbbFl-x4zD-dpCdbMPTY7B_g8trSDvtJjRezg_Mfq_c-4N_1AqJvnXHsIPe57AbcTHxCRGbX5x7ldy9kFPISg67Q1SM2slEQ7b8Oaf5NmLwKZEMmOroHd3sIST50Nr8PW67egVtFkJ9e7cDtvq_56eoB_C58BCMmrSfke6NmilgkrLcrgstKCM6z27Aesh9dYqmOc-d-iITuepcXPwu1bMnXUPAiDXKfrOdHyB4SSC4vfvn8zt7sEyTRT4g_jZtV_kB3bqbhr5eBtu239EJYMzJvpnVL5t0qBRLa8TyE8VHx7fA47tszxMajDF_C-lJKaiENT5zWVcaFGTFnPACkicxMypXj0miBbqJt5jwWFCYRmmWUSacpewTbdVO7J0CoFUJayxzNFRe0krxKrBWVB3OGulEawavBTOW8U-EoO73lpERblmjLCD6iAdc7oHB22NAsJmUfh2U2co4qnUknMj5SSZ5qYzPlQRLnklETwe5g_nJwxjJJ80TiGl8Wwcv1sI9D_LiiatechX2EYMID2Aged96yvhLGg6aSjEBs-NHGpW6O1NPToPWd42fnnEawFzzu33dfFocFwx9P_38Hz-BOgrScwEfche12ceaee1zV6hchav4Aqkwn_Q priority: 102 providerName: Scholars Portal – databaseName: Wiley Open Access dbid: 24P link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV3NatVAFB5qRXAj_hutMoqLCo2dOzOZH1xpSSkuRMFCd2H-cnvxklyaFOyujyD4GL5Vn8Q5k9zoRQV3ITNJJpw5Od-cfOcbhF4q6bSsuc-FIj7n0tjcGONzF4xRdYzgMiS2xQdxdMzfnxQnW-jNuhZm0IeYEm7gGel7DQ5ubLf_SzQ0uMBeqxjBrqHrEdYI2L6A8o9TgoUIkO-CcmlQXI8oslBrZSFC96erN-JRku3_G9b8kzL5O5RNsejwNro1gkj8drD6HbQVmrvoRpkEqC_uoR9l9FtAos0cf2nN0mAPNPX-AkMxCYbsuk9VkGNrBwt0yJjHJpz21Lu6_Faarsef0jIXyI97eMqK4F2gjVxdfo9Rnb3aw0Cdn-N4m7Cs44Xhq1ukR3eJrB3PjPJXS7xqF02PV0NtAk6b8NxHx4fl54OjfNyUIXcRW8SFa1xAaSu14zRYWwsu3YwFF2EfoVq4gpvAtbMSJof1IkQEKB2VlgnCdLCEPUDbTduERwgTL6X2ngWiDJek1rym3ss6QjhHwqzI0Iu1aarVoL1RDSrLtAL7VWC_DL0Do00dQC47nWjP5tXofZWYhUCMFTpIwWeGqsI6L0yERpxrRlyGdtYmr0Yf7ipaKKqhspdl6PnUHL0PfqmYJrTnqY-UTEbYmqGHwwyZRsJ4UlLSGZIbc2djqJstzeI0KXwr-NmsSIZ20yz799tX5UHJ4ODx_3d9gm5SIOYkRuIO2u7PzsPTiKx6-yx50E_ZOCRG priority: 102 providerName: Wiley-Blackwell |
Title | Estimating koala density from incidental koala sightings in South‐East Queensland, Australia (1997–2013), using a self‐exciting spatio‐temporal point process model |
URI | https://onlinelibrary.wiley.com/doi/abs/10.1002%2Fece3.8082 https://www.ncbi.nlm.nih.gov/pubmed/34707819 https://www.proquest.com/docview/2582982513 https://search.proquest.com/docview/2587737851 https://pubmed.ncbi.nlm.nih.gov/PMC8525080 https://doaj.org/article/61ee0ab69e7641a285bcd6a22344930c |
Volume | 11 |
hasFullText | 1 |
inHoldings | 1 |
isFullTextHit | |
isPrint | |
link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3datswFD40HRu9Gfufty5oYxcd1IkiyZZ1uQaXMmjJxgq9M_pzGpbaoUlhvesjDPYYe6s-ySTZDg3bbnZjjOUfmXPk8x35O58A3mdcC14yE6cZNjHjUsVSShNrK2VWugjObWBbnKRHp-zTWXK2BUlXCxNI-1rNBtX8YlDNzgO3cnGhhx1PbDg5Hmf-X1yGhz3oufB7J0VvPr9etot3KkKYDK22dJC5WLcDDygL8jZiIwwFtf6_Qcw_mZJ3EWwIQYeP4GGLHdHHpo-PYctWT-B-HnSnr5_Cr9wNVw9Aqyn6Vsu5RMaz01fXyNeQID-pbkLxY9u69Hm5nyh3TSgspXd78yOXyxX6HLJbz3ncR-vJELTn2SK3Nz9dMKcf9pFnzE-Ru42dl-5C-13PwqOXgaPtjrSqV3O0qGfVCi2akgQU1t55BqeH-dfxUdyuxRBrBylcvuryJqG40IxYpcqUcT2iVju0h4lIdcKkZUIr7n1CmdQ64Mc14YqmmAqrMH0O21Vd2ZeAsOFcGEMtzqSzRilYSYzhpUNuGttREsG7zjTFopHcKBpxZVJ4UxbelBEceKOtT_Aq2eFAfTktWl8p0pG1WKpUWJ6ykSRZorRJpUNEjAmKdQS7ncmLduguC5JkRPiCXhrB23WzG3T-T4qsbH0VzuGccodWI3jReMi6J52HRcA3fGejq5stzs-DsHfr1xHsBS_799sX-TinfufVfz_kNewQT88JvMRd2F5dXtk3Dl-tVB96hE36cO8gP5l86YdZCrc9Zlk_jLTf-pQvvg |
link.rule.ids | 230,314,727,780,784,864,885,2102,2221,11562,21388,24318,27924,27925,33744,33745,43805,46052,46476,50814,50923,53791,53793,74302 |
linkProvider | National Library of Medicine |
linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV1bb9MwFLagExovCMYtMMAgHoa0MNd24vgJsSlTgVEB2qS9Rb6lq1Yl3dpJ9G0_AYmfwb_aL8HHdQsVl7cozsXROSf-fPz5Owi9LISRouY2zQtiUy6UTpVSNjVOqaL2I7hwgW3Rz3tH_P1xdhwTbpNIq1z8E8OP2rYGcuQ7NCuohH2W7M34LIWqUbC6GktoXEdroJyeddDabtn_9GWZZSE5aHiJhaQQoTvOOPa6IAVdGYiCXv_fQOafXMnfMWwYhPZvo1sRPeK3c3PfQddcs4FulEF5eraB1mNJ85PZXfSj9MELcLQZ4NNWjRS2wFWfzjDsKMGQYrdhK2RsncAsHdLmvgmHwnpXl99KNZniz2GuCwzIbbxMjeAt4I5cXX73Qzt7tY2BPz_A_jFuVPsb3VczDK-eBMa2PxM1sEZ43A6bKR7PNyjgUInnHjraLw_3emmszJAaDzD87NXPoqQW0nDqtK5zLkyXOeOxH6EyNxlXjkujBXiItrnzMFAYKjTLCZNOE3YfdZq2cQ8RJlYIaS1zpFBckFrymlorao_jDHHdLEEvFmaqxnMBjmoutUwrsGUFtkzQLhhweQFoZocT7fmgiiFY5V3niNK5dCLnXUWLTBubK4-POJeMmARtLsxfxUCeVL_cLkHPl80-BGFdRTWuvQjXCMGEx64JejD3lmVPGA9ySjJBYsWPVrq62tIMT4LMdwErzgVJ0FbwuH9_fVXulQwOHv3_C56h9d7hx4Pq4F3_w2N0kwI7J9ASN1Fnen7hnnh4NdVPYwz9BKuhKp8 |
linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV1bb9MwFLagE5cXBINBYIBBPAxpoa7txPETYiPVuKgaiEl7i3xLV1ElZe0k-rafgMTP4F_tl-DjuoWKy1sUp02ic07O5-PP30HoWSGMFDW3aV4Qm3KhdKqUsqlxShW1z-DCBbbFID844m-Ps-PIf5pGWuXymxg-1LY1UCPv0qygEvZZsm4daRGHr_svJ19S6CAFK62xncZltOGzIqEdtLFXDg4_riouJAc9L7GUFyK064xjLwpS0LWkFLT7_wY4_-RN_o5nQ0Lq30Q3IpLErxamv4UuuWYTXSmDCvV8E12L7c1P5rfRj9IHMkDTZog_t2qssAXe-myOYXcJhnK7Ddsi4-gUZuxQQvdDODTZuzj_VqrpDH8I815gQ-7iVZkE7wCP5OL8u0_z7PkuBi79EPu_cePa_9B9NaNw62lgb_szUQ9rjCftqJnhyWKzAg5dee6go375af8gjV0aUuPBhp_J-hmV1EIaTp3Wdc6F6TFnPA4kVOYm48pxabQAb9E2dx4SCkOFZjlh0mnCtlCnaRt3D2FihZDWMkcKxQWpJa-ptaL2mM4Q18sS9HRppmqyEOOoFrLLtAJbVmDLBO2BAVcXgH52ONGeDqsYjlXec44onUsnct5TtMi0sbnyWIlzyYhJ0PbS_FUM6mn1ywUT9GQ17MMR1lhU49qzcI0QTHgcm6C7C29ZPQnjQVpJJkis-dHao66PNKOTIPldwOpzQRK0Ezzu329flfslg4P7_3-Dx-iqD5_q_ZvBuwfoOgWiTmAobqPO7PTMPfRIa6YfxRD6CVhtLsw |
openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Estimating+koala+density+from+incidental+koala+sightings+in+South%E2%80%90East+Queensland%2C+Australia+%281997%E2%80%932013%29%2C+using+a+self%E2%80%90exciting+spatio%E2%80%90temporal+point+process+model&rft.jtitle=Ecology+and+evolution&rft.au=Ravi+Bandara+Dissanayake&rft.au=Giorgi%2C+Emanuele&rft.au=Stevenson%2C+Mark&rft.au=Allavena%2C+Rachel&rft.date=2021-10-01&rft.pub=John+Wiley+%26+Sons%2C+Inc&rft.eissn=2045-7758&rft.volume=11&rft.issue=20&rft.spage=13805&rft.epage=13814&rft_id=info:doi/10.1002%2Fece3.8082&rft.externalDBID=HAS_PDF_LINK |
thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2045-7758&client=summon |
thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2045-7758&client=summon |
thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2045-7758&client=summon |