The Experts below are selected from a list of 105 Experts worldwide ranked by ideXlab platform
Luigi Boitani - One of the best experts on this subject based on the ideXlab platform.
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a Multiple Data Source approach to improve abundance estimates of small populations the brown bear in the apennines italy
Biological Conservation, 2012Co-Authors: Vincenzo Gervasi, Paolo Ciucci, John Boulanger, Ettore Randi, Luigi BoitaniAbstract:When dealing with small populations of elusive species, capture–recapture methods suffer from sampling and analytical limitations, making abundance assessment particularly challenging. We present an empirical and theoretical evaluation of Multiple Data Source sampling as a flexible and effective way to improve the performance of capture–recapture models for abundance estimation of small populations. We integrated three Data Sources to estimate the size of the relict Apennine brown bear (Ursus arctos marsicanus) population in central Italy, and supported our results with simulations to assess the robustness of Multiple Data Source capture–recapture models to violations of main assumptions. During May–August 2008, we non-invasively sampled bears using systematic hair traps on a grid of 41 5 × 5 km cells, moving trap locations between five sampling sessions. We also live-trapped, ear-tagged, and genotyped 17 bears (2004–2008), and integrated resights of marked bears and family units (July–September 2008) into a Multiple Data Source capture–recapture Dataset. Population size was estimated at 40 (95% CI = 37–52) bears, with a corresponding closure-corrected density of 32 bears/1000 km2 (95% CI = 28–36). Given the average capture probability we obtained with all Data Sources combined (pˆ=0.311), simulations suggested that the expected degree of correlation among Data Sources did not seriously affect model performance, with expected level of bias <5%. Our results refine previous simulation work on larger populations, cautioning on the combined effect of lack of independence and low capture probability in application of Multiple Data Source sampling to very small populations (N < 100).
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A Multiple Data Source approach to improve abundance estimates of small populations: The brown bear in the Apennines, Italy
Biological Conservation, 2012Co-Authors: Vincenzo Gervasi, Paolo Ciucci, John Boulanger, Ettore Randi, Luigi BoitaniAbstract:When dealing with small populations of elusive species, capture–recapture methods suffer from sampling and analytical limitations, making abundance assessment particularly challenging. We present an empirical and theoretical evaluation of Multiple Data Source sampling as a flexible and effective way to improve the performance of capture–recapture models for abundance estimation of small populations. We integrated three Data Sources to estimate the size of the relict Apennine brown bear (Ursus arctos marsicanus) population in central Italy, and supported our results with simulations to assess the robustness of Multiple Data Source capture–recapture models to violations of main assumptions. During May–August 2008, we non-invasively sampled bears using systematic hair traps on a grid of 41 5 × 5 km cells, moving trap locations between five sampling sessions. We also live-trapped, ear-tagged, and genotyped 17 bears (2004–2008), and integrated resights of marked bears and family units (July–September 2008) into a Multiple Data Source capture–recapture Dataset. Population size was estimated at 40 (95% CI = 37–52) bears, with a corresponding closure-corrected density of 32 bears/1000 km2 (95% CI = 28–36). Given the average capture probability we obtained with all Data Sources combined (pˆ=0.311), simulations suggested that the expected degree of correlation among Data Sources did not seriously affect model performance, with expected level of bias
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a preliminary estimate of the apennine brown bear population size based on hair snag sampling and Multiple Data Source mark recapture huggins models
Ursus, 2008Co-Authors: Vincenzo Gervasi, Paolo Ciucci, John Boulanger, Ettore Randi, Mario Posillico, Cinzia Sulli, Stefano Focardi, Luigi BoitaniAbstract:Abstract Although the brown bear (Ursus arctos) population in Abruzzo (central Apennines, Italy) suffered high mortality during the past 30 years and is potentially at high risk of extinction, no formal estimate of its abundance has been attempted. In 2004, the Italian Forest Service and Abruzzo National Park applied DNA-based techniques to hair-snag samples from the Apennine bear population. Even though sampling and theoretical limitations prevented estimating population size from being the objective of these first applications, we extracted the most we could out of the 2004 Data to produce the first estimate of population size. To overcome the limitations of the sampling strategies (systematic grid, opportunistic sampling at buckthorn [Rhamnus alpina] patches, incidental sampling during other field activities), we used a Multiple Data-Source approach and Huggins closed models implemented in program MARK. To account for model uncertainty, we averaged plausible models using Akaike weights and estimated an...
Shichao Zhang - One of the best experts on this subject based on the ideXlab platform.
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Group Pattern Discovery Systems for Multiple Data Sources
Encyclopedia of Data Warehousing and Mining, 2020Co-Authors: Shichao Zhang, Chengqi ZhangAbstract:Multiple Data Source mining is the process of identifying potentially useful patterns from different Data Sources, or Datasets (Zhang et al., 2003). Group pattern discovery systems for mining different Data Sources are based on local pattern-analysis strategy, mainly including logical systems for information enhancing, a pattern discovery system, and a post-pattern-analysis system.
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mining Multiple Data Sources local pattern analysis
Data Mining and Knowledge Discovery, 2006Co-Authors: Shichao Zhang, Mohammed J ZakiAbstract:Many large organizations process Data from Multiple Data Sources, such as the different branches of an interstate or international company. Also the Web has emerged as a large, distributed Data repository consisting of a variety of Data Sources and formats. Although the Data collected from the Web or Multiple local Datasets brings us opportunities in improving the quality of decisions, it generates significant challenges at the same time, for example, how to efficiently discover useful knowledge from different Data Sources and how to integrate them. We call this the Multiple Data Source (MDS) mining problem, and it has recently been recognized as an important research topic in the Data mining community. This problem is difficult to solve due to the fact that MDS mining involves the discovery of useful patterns in multidimensional spaces across diverse Sources; and putting all Data together from different Sources might amass a huge Database for centralized processing and might cause serious problems in Data privacy, Data inconsistency, Data conflict, and Data irrelevance. On the other hand, mining local patterns at different Data Sources and forwarding the local patterns (rather than the original raw Data) to a centralized place for global pattern analysis can provide a feasible way to deal with MDS problems (Zhang et al., 2004). Local knowledge/pattern sharing can alleviate the challenges of a centralized processing approach, and is an attractive approach since the local patterns may in any case be mined for knowledge discovery at each Data Source independently to discover local trends and for making local decisions. The above observations encourage the development of pattern discovery algorithms based on local patterns. Local pattern analysis is an in-place strategy specifically designed for mining Multiple Data Sources, providing a feasible way to generate globally interesting models from Data in multidimensional multi-Databases. With local pattern analysis, one can better understand the distribution and inconsistency of local Data
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Synthesizing high-frequency rules from different Data Sources
IEEE Transactions on Knowledge and Data Engineering, 2003Co-Authors: Xindong Wu, Shichao ZhangAbstract:Many large organizations have Multiple Data Sources, such as different branches of an interstate company. While putting all Data together from different Sources might amass a huge Database for centralized processing, mining association rules at different Data Sources and forwarding the rules (rather than the original raw Data) to the centralized company headquarter provides a feasible way to deal with Multiple Data Source problems. In the meanwhile, the association rules at each Data Source may be required for that Data Source in the first instance, so association analysis at each Data Source is also important and useful. However, the forwarded rules from different Data Sources may be too many for the centralized company headquarter to use. This paper presents a weighting model for synthesizing high-frequency association rules from different Data Sources. There are two reasons to focus on high-frequency rules. First, a centralized company headquarter is interested in high-frequency rules because they are supported by most of its branches for corporate profitability. Second, high-frequency rules have larger chances to become valid rules in the union of all Data Sources. In order to extract high-frequency rules efficiently, a procedure of rule selection is also constructed to enhance the weighting model by coping with low-frequency rules. Experimental results show that our proposed weighting model is efficient and effective.
Vincenzo Gervasi - One of the best experts on this subject based on the ideXlab platform.
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a Multiple Data Source approach to improve abundance estimates of small populations the brown bear in the apennines italy
Biological Conservation, 2012Co-Authors: Vincenzo Gervasi, Paolo Ciucci, John Boulanger, Ettore Randi, Luigi BoitaniAbstract:When dealing with small populations of elusive species, capture–recapture methods suffer from sampling and analytical limitations, making abundance assessment particularly challenging. We present an empirical and theoretical evaluation of Multiple Data Source sampling as a flexible and effective way to improve the performance of capture–recapture models for abundance estimation of small populations. We integrated three Data Sources to estimate the size of the relict Apennine brown bear (Ursus arctos marsicanus) population in central Italy, and supported our results with simulations to assess the robustness of Multiple Data Source capture–recapture models to violations of main assumptions. During May–August 2008, we non-invasively sampled bears using systematic hair traps on a grid of 41 5 × 5 km cells, moving trap locations between five sampling sessions. We also live-trapped, ear-tagged, and genotyped 17 bears (2004–2008), and integrated resights of marked bears and family units (July–September 2008) into a Multiple Data Source capture–recapture Dataset. Population size was estimated at 40 (95% CI = 37–52) bears, with a corresponding closure-corrected density of 32 bears/1000 km2 (95% CI = 28–36). Given the average capture probability we obtained with all Data Sources combined (pˆ=0.311), simulations suggested that the expected degree of correlation among Data Sources did not seriously affect model performance, with expected level of bias <5%. Our results refine previous simulation work on larger populations, cautioning on the combined effect of lack of independence and low capture probability in application of Multiple Data Source sampling to very small populations (N < 100).
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A Multiple Data Source approach to improve abundance estimates of small populations: The brown bear in the Apennines, Italy
Biological Conservation, 2012Co-Authors: Vincenzo Gervasi, Paolo Ciucci, John Boulanger, Ettore Randi, Luigi BoitaniAbstract:When dealing with small populations of elusive species, capture–recapture methods suffer from sampling and analytical limitations, making abundance assessment particularly challenging. We present an empirical and theoretical evaluation of Multiple Data Source sampling as a flexible and effective way to improve the performance of capture–recapture models for abundance estimation of small populations. We integrated three Data Sources to estimate the size of the relict Apennine brown bear (Ursus arctos marsicanus) population in central Italy, and supported our results with simulations to assess the robustness of Multiple Data Source capture–recapture models to violations of main assumptions. During May–August 2008, we non-invasively sampled bears using systematic hair traps on a grid of 41 5 × 5 km cells, moving trap locations between five sampling sessions. We also live-trapped, ear-tagged, and genotyped 17 bears (2004–2008), and integrated resights of marked bears and family units (July–September 2008) into a Multiple Data Source capture–recapture Dataset. Population size was estimated at 40 (95% CI = 37–52) bears, with a corresponding closure-corrected density of 32 bears/1000 km2 (95% CI = 28–36). Given the average capture probability we obtained with all Data Sources combined (pˆ=0.311), simulations suggested that the expected degree of correlation among Data Sources did not seriously affect model performance, with expected level of bias
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a preliminary estimate of the apennine brown bear population size based on hair snag sampling and Multiple Data Source mark recapture huggins models
Ursus, 2008Co-Authors: Vincenzo Gervasi, Paolo Ciucci, John Boulanger, Ettore Randi, Mario Posillico, Cinzia Sulli, Stefano Focardi, Luigi BoitaniAbstract:Abstract Although the brown bear (Ursus arctos) population in Abruzzo (central Apennines, Italy) suffered high mortality during the past 30 years and is potentially at high risk of extinction, no formal estimate of its abundance has been attempted. In 2004, the Italian Forest Service and Abruzzo National Park applied DNA-based techniques to hair-snag samples from the Apennine bear population. Even though sampling and theoretical limitations prevented estimating population size from being the objective of these first applications, we extracted the most we could out of the 2004 Data to produce the first estimate of population size. To overcome the limitations of the sampling strategies (systematic grid, opportunistic sampling at buckthorn [Rhamnus alpina] patches, incidental sampling during other field activities), we used a Multiple Data-Source approach and Huggins closed models implemented in program MARK. To account for model uncertainty, we averaged plausible models using Akaike weights and estimated an...
Paolo Ciucci - One of the best experts on this subject based on the ideXlab platform.
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a Multiple Data Source approach to improve abundance estimates of small populations the brown bear in the apennines italy
Biological Conservation, 2012Co-Authors: Vincenzo Gervasi, Paolo Ciucci, John Boulanger, Ettore Randi, Luigi BoitaniAbstract:When dealing with small populations of elusive species, capture–recapture methods suffer from sampling and analytical limitations, making abundance assessment particularly challenging. We present an empirical and theoretical evaluation of Multiple Data Source sampling as a flexible and effective way to improve the performance of capture–recapture models for abundance estimation of small populations. We integrated three Data Sources to estimate the size of the relict Apennine brown bear (Ursus arctos marsicanus) population in central Italy, and supported our results with simulations to assess the robustness of Multiple Data Source capture–recapture models to violations of main assumptions. During May–August 2008, we non-invasively sampled bears using systematic hair traps on a grid of 41 5 × 5 km cells, moving trap locations between five sampling sessions. We also live-trapped, ear-tagged, and genotyped 17 bears (2004–2008), and integrated resights of marked bears and family units (July–September 2008) into a Multiple Data Source capture–recapture Dataset. Population size was estimated at 40 (95% CI = 37–52) bears, with a corresponding closure-corrected density of 32 bears/1000 km2 (95% CI = 28–36). Given the average capture probability we obtained with all Data Sources combined (pˆ=0.311), simulations suggested that the expected degree of correlation among Data Sources did not seriously affect model performance, with expected level of bias <5%. Our results refine previous simulation work on larger populations, cautioning on the combined effect of lack of independence and low capture probability in application of Multiple Data Source sampling to very small populations (N < 100).
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A Multiple Data Source approach to improve abundance estimates of small populations: The brown bear in the Apennines, Italy
Biological Conservation, 2012Co-Authors: Vincenzo Gervasi, Paolo Ciucci, John Boulanger, Ettore Randi, Luigi BoitaniAbstract:When dealing with small populations of elusive species, capture–recapture methods suffer from sampling and analytical limitations, making abundance assessment particularly challenging. We present an empirical and theoretical evaluation of Multiple Data Source sampling as a flexible and effective way to improve the performance of capture–recapture models for abundance estimation of small populations. We integrated three Data Sources to estimate the size of the relict Apennine brown bear (Ursus arctos marsicanus) population in central Italy, and supported our results with simulations to assess the robustness of Multiple Data Source capture–recapture models to violations of main assumptions. During May–August 2008, we non-invasively sampled bears using systematic hair traps on a grid of 41 5 × 5 km cells, moving trap locations between five sampling sessions. We also live-trapped, ear-tagged, and genotyped 17 bears (2004–2008), and integrated resights of marked bears and family units (July–September 2008) into a Multiple Data Source capture–recapture Dataset. Population size was estimated at 40 (95% CI = 37–52) bears, with a corresponding closure-corrected density of 32 bears/1000 km2 (95% CI = 28–36). Given the average capture probability we obtained with all Data Sources combined (pˆ=0.311), simulations suggested that the expected degree of correlation among Data Sources did not seriously affect model performance, with expected level of bias
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a preliminary estimate of the apennine brown bear population size based on hair snag sampling and Multiple Data Source mark recapture huggins models
Ursus, 2008Co-Authors: Vincenzo Gervasi, Paolo Ciucci, John Boulanger, Ettore Randi, Mario Posillico, Cinzia Sulli, Stefano Focardi, Luigi BoitaniAbstract:Abstract Although the brown bear (Ursus arctos) population in Abruzzo (central Apennines, Italy) suffered high mortality during the past 30 years and is potentially at high risk of extinction, no formal estimate of its abundance has been attempted. In 2004, the Italian Forest Service and Abruzzo National Park applied DNA-based techniques to hair-snag samples from the Apennine bear population. Even though sampling and theoretical limitations prevented estimating population size from being the objective of these first applications, we extracted the most we could out of the 2004 Data to produce the first estimate of population size. To overcome the limitations of the sampling strategies (systematic grid, opportunistic sampling at buckthorn [Rhamnus alpina] patches, incidental sampling during other field activities), we used a Multiple Data-Source approach and Huggins closed models implemented in program MARK. To account for model uncertainty, we averaged plausible models using Akaike weights and estimated an...
John Boulanger - One of the best experts on this subject based on the ideXlab platform.
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a Multiple Data Source approach to improve abundance estimates of small populations the brown bear in the apennines italy
Biological Conservation, 2012Co-Authors: Vincenzo Gervasi, Paolo Ciucci, John Boulanger, Ettore Randi, Luigi BoitaniAbstract:When dealing with small populations of elusive species, capture–recapture methods suffer from sampling and analytical limitations, making abundance assessment particularly challenging. We present an empirical and theoretical evaluation of Multiple Data Source sampling as a flexible and effective way to improve the performance of capture–recapture models for abundance estimation of small populations. We integrated three Data Sources to estimate the size of the relict Apennine brown bear (Ursus arctos marsicanus) population in central Italy, and supported our results with simulations to assess the robustness of Multiple Data Source capture–recapture models to violations of main assumptions. During May–August 2008, we non-invasively sampled bears using systematic hair traps on a grid of 41 5 × 5 km cells, moving trap locations between five sampling sessions. We also live-trapped, ear-tagged, and genotyped 17 bears (2004–2008), and integrated resights of marked bears and family units (July–September 2008) into a Multiple Data Source capture–recapture Dataset. Population size was estimated at 40 (95% CI = 37–52) bears, with a corresponding closure-corrected density of 32 bears/1000 km2 (95% CI = 28–36). Given the average capture probability we obtained with all Data Sources combined (pˆ=0.311), simulations suggested that the expected degree of correlation among Data Sources did not seriously affect model performance, with expected level of bias <5%. Our results refine previous simulation work on larger populations, cautioning on the combined effect of lack of independence and low capture probability in application of Multiple Data Source sampling to very small populations (N < 100).
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A Multiple Data Source approach to improve abundance estimates of small populations: The brown bear in the Apennines, Italy
Biological Conservation, 2012Co-Authors: Vincenzo Gervasi, Paolo Ciucci, John Boulanger, Ettore Randi, Luigi BoitaniAbstract:When dealing with small populations of elusive species, capture–recapture methods suffer from sampling and analytical limitations, making abundance assessment particularly challenging. We present an empirical and theoretical evaluation of Multiple Data Source sampling as a flexible and effective way to improve the performance of capture–recapture models for abundance estimation of small populations. We integrated three Data Sources to estimate the size of the relict Apennine brown bear (Ursus arctos marsicanus) population in central Italy, and supported our results with simulations to assess the robustness of Multiple Data Source capture–recapture models to violations of main assumptions. During May–August 2008, we non-invasively sampled bears using systematic hair traps on a grid of 41 5 × 5 km cells, moving trap locations between five sampling sessions. We also live-trapped, ear-tagged, and genotyped 17 bears (2004–2008), and integrated resights of marked bears and family units (July–September 2008) into a Multiple Data Source capture–recapture Dataset. Population size was estimated at 40 (95% CI = 37–52) bears, with a corresponding closure-corrected density of 32 bears/1000 km2 (95% CI = 28–36). Given the average capture probability we obtained with all Data Sources combined (pˆ=0.311), simulations suggested that the expected degree of correlation among Data Sources did not seriously affect model performance, with expected level of bias
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a preliminary estimate of the apennine brown bear population size based on hair snag sampling and Multiple Data Source mark recapture huggins models
Ursus, 2008Co-Authors: Vincenzo Gervasi, Paolo Ciucci, John Boulanger, Ettore Randi, Mario Posillico, Cinzia Sulli, Stefano Focardi, Luigi BoitaniAbstract:Abstract Although the brown bear (Ursus arctos) population in Abruzzo (central Apennines, Italy) suffered high mortality during the past 30 years and is potentially at high risk of extinction, no formal estimate of its abundance has been attempted. In 2004, the Italian Forest Service and Abruzzo National Park applied DNA-based techniques to hair-snag samples from the Apennine bear population. Even though sampling and theoretical limitations prevented estimating population size from being the objective of these first applications, we extracted the most we could out of the 2004 Data to produce the first estimate of population size. To overcome the limitations of the sampling strategies (systematic grid, opportunistic sampling at buckthorn [Rhamnus alpina] patches, incidental sampling during other field activities), we used a Multiple Data-Source approach and Huggins closed models implemented in program MARK. To account for model uncertainty, we averaged plausible models using Akaike weights and estimated an...