Imperfect detection is a form of sampling error that can bias measurements of species occurrence and is widely known to bias measures of local(α)and regional(γ)diversity.However,it is less well known how imperfect d...Imperfect detection is a form of sampling error that can bias measurements of species occurrence and is widely known to bias measures of local(α)and regional(γ)diversity.However,it is less well known how imperfect detection affects estimates ofβdiversity,the variation in species composition among sites,especially when incorporating species traits and evolutionary histories.Using a decade of avian monitoring data collected across36 subtropical islands,occupancy model was applied to correct imperfect detection and to quantify its impact on taxonomic,functional,and phylogeneticβdiversity in relation to island area and isolation.To assess the broader generality of these patterns,simulations were conducted incorporating multiple ecological and sampling drivers,including survey design and community structure.Both empirical and simulated analyses revealed that imperfect detection consistently led to overestimates of taxonomic,functional,and phylogeneticβdiversity,primarily due to the under-detection of shared species,which,in turn,obscured diversity relationships with island attributes.In the empirical dataset,the extent of overestimation increased with greater differences in island area,whereas simulations demonstrated that repeated surveys per site effectively reduced this bias.Collectively,these findings establish a general framework explaining how imperfect detection systematically biases all facets ofβdiversity by altering observed species composition.This mechanism offers broad applicability across various biological taxa and ecological systems,enhancing the accuracy of biodiversity measurements,particularly functional and phylogenetic diversity.Given the importance ofβdiversity in understanding spatial and temporal community turnover,it is imperative to prioritize its accurate quantification.展开更多
Background:The reliability of long-term population estimates is crucial for conservation and management purposes.Most previous studies assume that count indices are proportionally related to abundance;however,this ass...Background:The reliability of long-term population estimates is crucial for conservation and management purposes.Most previous studies assume that count indices are proportionally related to abundance;however,this assumption may not hold when detection varies spatially and temporally.We examined seasonal variations in abundance of three bird species(Cabot’s Tragopan Tragopan caboti,Silver Pheasant Lophura nycthemera,and Whitenecklaced Partridge Arborophila gingica) along an elevational gradient,using N-mixture models that take into account imperfect detection in our bird data.Methods:Camera-trapping was used to monitor temporal activity patterns of these species at Guangdong Nanling National Nature Reserve from December 2013 to November 2017(4 seasons per year).For abundance analysis(N-mixture modeling),we divided a year into 4 seasons,i.e.3 months per season,and performed the analysis by season.Elevation was incorporated into the N-mixture model as a covariate that may affect abundance.We compared the N-mixture model with a null model(no covariate model) and selected the better model based on AIC values to make an inference.Results:From 24 sampling sites,we obtained 6786 photographs of 8482 individuals of 44 bird species and 26 mammal species.Silver Pheasant was photographed much more frequently and showed higher temporal activity frequency than White-necklaced Partridge or Cabot’s Tragopan.Silver Pheasant was camera-captured most frequently in summer,and other two species in winters.All three species had two daytime activity peaks:between 6:00 a.m.and 10:00 a.m.,and between 5:00 p.m.and 7:00 p.m.,respectively.Our estimated abundance and detection probability from the N-mixture model were variable by season.In particular,all three species showed greater abundance in summer than in winter,and estimated abundance patterns of all three species were more similar with observed cameratrapping counts in summers.Moreover,in winter,elevation had a positive impact on abundance of Silver Pheasant and Cabot’s Tragopan,but not on White-necklaced Partridge.Conclusions:Our results demonstrate that the N-mixture model performed well in the estimation of temporal popu lation abundance at local fixed permanent plots in mountain habitat in southern China,based on the modeling of repeated camera-trapping counts.The seasonal differences in abundance of the three endemic bird species and the strong effect of elevation on abundance of two species in winter were only indicative of variations in spatio-tempora distribution within species and between species.In identifying suitable habitat for endemic pheasants,the positive elevational effect also suggests that more attention should be paid to conservation of areas with higher elevation in the Nanling Mountains.展开更多
基金supported by the National Key Research and Development Program of China(2024YFF1307602)National Natural Science Foundation of China(32371590,32311520284,32030066,32101268,32101278,32071545)。
文摘Imperfect detection is a form of sampling error that can bias measurements of species occurrence and is widely known to bias measures of local(α)and regional(γ)diversity.However,it is less well known how imperfect detection affects estimates ofβdiversity,the variation in species composition among sites,especially when incorporating species traits and evolutionary histories.Using a decade of avian monitoring data collected across36 subtropical islands,occupancy model was applied to correct imperfect detection and to quantify its impact on taxonomic,functional,and phylogeneticβdiversity in relation to island area and isolation.To assess the broader generality of these patterns,simulations were conducted incorporating multiple ecological and sampling drivers,including survey design and community structure.Both empirical and simulated analyses revealed that imperfect detection consistently led to overestimates of taxonomic,functional,and phylogeneticβdiversity,primarily due to the under-detection of shared species,which,in turn,obscured diversity relationships with island attributes.In the empirical dataset,the extent of overestimation increased with greater differences in island area,whereas simulations demonstrated that repeated surveys per site effectively reduced this bias.Collectively,these findings establish a general framework explaining how imperfect detection systematically biases all facets ofβdiversity by altering observed species composition.This mechanism offers broad applicability across various biological taxa and ecological systems,enhancing the accuracy of biodiversity measurements,particularly functional and phylogenetic diversity.Given the importance ofβdiversity in understanding spatial and temporal community turnover,it is imperative to prioritize its accurate quantification.
基金supported by Guangdong Science and Technology Plan Project(2013B02031005)Guangdong Academy of Science(GDAS)Special Project of Science and Technology Development(2017GDASCX-0107,2018 GDASCX-0107)+1 种基金Guangdong Forestry Special Project(0877-16GZTP01D060,1210-1741YDZB0401)Special Fund of Guangdong Nature Reserve(RYCG12-14,GDHS15SGFX07060,Cabot’s Tragopan monitoring)
文摘Background:The reliability of long-term population estimates is crucial for conservation and management purposes.Most previous studies assume that count indices are proportionally related to abundance;however,this assumption may not hold when detection varies spatially and temporally.We examined seasonal variations in abundance of three bird species(Cabot’s Tragopan Tragopan caboti,Silver Pheasant Lophura nycthemera,and Whitenecklaced Partridge Arborophila gingica) along an elevational gradient,using N-mixture models that take into account imperfect detection in our bird data.Methods:Camera-trapping was used to monitor temporal activity patterns of these species at Guangdong Nanling National Nature Reserve from December 2013 to November 2017(4 seasons per year).For abundance analysis(N-mixture modeling),we divided a year into 4 seasons,i.e.3 months per season,and performed the analysis by season.Elevation was incorporated into the N-mixture model as a covariate that may affect abundance.We compared the N-mixture model with a null model(no covariate model) and selected the better model based on AIC values to make an inference.Results:From 24 sampling sites,we obtained 6786 photographs of 8482 individuals of 44 bird species and 26 mammal species.Silver Pheasant was photographed much more frequently and showed higher temporal activity frequency than White-necklaced Partridge or Cabot’s Tragopan.Silver Pheasant was camera-captured most frequently in summer,and other two species in winters.All three species had two daytime activity peaks:between 6:00 a.m.and 10:00 a.m.,and between 5:00 p.m.and 7:00 p.m.,respectively.Our estimated abundance and detection probability from the N-mixture model were variable by season.In particular,all three species showed greater abundance in summer than in winter,and estimated abundance patterns of all three species were more similar with observed cameratrapping counts in summers.Moreover,in winter,elevation had a positive impact on abundance of Silver Pheasant and Cabot’s Tragopan,but not on White-necklaced Partridge.Conclusions:Our results demonstrate that the N-mixture model performed well in the estimation of temporal popu lation abundance at local fixed permanent plots in mountain habitat in southern China,based on the modeling of repeated camera-trapping counts.The seasonal differences in abundance of the three endemic bird species and the strong effect of elevation on abundance of two species in winter were only indicative of variations in spatio-tempora distribution within species and between species.In identifying suitable habitat for endemic pheasants,the positive elevational effect also suggests that more attention should be paid to conservation of areas with higher elevation in the Nanling Mountains.
