Data fusion of distance sampling and capture-recapture data
Tài liệu tham khảo
Andrews, 2015, When to use social cues: Conspecific attraction at newly created grasslands, Condor Ornithol. Appl., 117, 297
Araujo, 2006, Five (or so) challenges for species distribution modelling, J. Biogeogr., 33, 1677, 10.1111/j.1365-2699.2006.01584.x
Borchers, 2015, A unifying model for capture–recapture and distance sampling surveys of wildlife populations, J. Amer. Statist. Assoc., 110, 195, 10.1080/01621459.2014.893884
Boyle, 2019
Buckland, 2001
Burnham, 1984, The need for distance data in transect counts, J. Wildl. Manage., 48, 1248, 10.2307/3801785
Burnham, 1980, Estimation of density from line transect sampling of biological populations, Wildl. Monogr., 72, 3
Chakraborty, 2011, Point pattern modelling for degraded presence-only data over large regions, J. R. Stat. Soc. Ser. C. Appl. Stat., 60, 757
Cressie, 1993
Diggle, 1976, Statistical analysis of spatial point patterns by means of distance methods, Biometrics, 32, 659, 10.2307/2529754
Dorazio, 2012, Predicting the geographic distribution of a species from presence-only data subject to detection errors, Biometrics, 68, 1303, 10.1111/j.1541-0420.2012.01779.x
Dorazio, 2014, Accounting for imperfect detection and survey bias in statistical analysis of presence-only data, Global Ecol. Biogeogr., 23, 1472, 10.1111/geb.12216
Farr, 2020, Integrating distance sampling and presence-only data to estimate species abundance, Ecology, 102
Fithian, 2015, Bias correction in species distribution models: Pooling survey and collection data for multiple species, Methods Ecol. Evol., 6, 424, 10.1111/2041-210X.12242
Fletcher, 2019, A practical guide for combining data to model species distributions, Ecology, 100, 10.1002/ecy.2710
Gelfand, 2018, Bayesian inference and computing for spatial point patterns, NSF-CBMS Regional Conf. Ser. Probab. Statist., 10, 1
Gerber, 2012, Evaluating the potential biases in carnivore capture–recapture studies associated with the use of lure and varying density estimation techniques using photographic-sampling data of the Malagasy civet, Popul. Ecol., 54, 43, 10.1007/s10144-011-0276-3
Hefley, 2014, Correction of location errors for presence-only species distribution models, Methods Ecol. Evol., 5, 207, 10.1111/2041-210X.12144
Hefley, 2015, Use of opportunistic sightings and expert knowledge to predict and compare whooping crane stopover habitat, Conserv. Biol., 29, 1337, 10.1111/cobi.12515
Hefley, 2020
Hefley, 2017, Bias correction of bounded location errors in presence-only data, Methods Ecol. Evol., 8, 1566, 10.1111/2041-210X.12793
Hefley, 2016, Hierarchical species distribution models, Curr. Landsc. Ecol. Rep., 1, 87, 10.1007/s40823-016-0008-7
Hefley, 2013, Nondetection sampling bias in marked presence-only data, Ecol. Evol., 3, 5225, 10.1002/ece3.887
Herse, 2018, The importance of core habitat for a threatened species in changing landscapes, J. Appl. Ecol., 55, 2241, 10.1111/1365-2664.13234
Hooten, 2019
Isaac, 2020, Data integration for large-scale models of species distributions, Trends Ecol. Evol., 35, 56, 10.1016/j.tree.2019.08.006
Johnson, 2010, A model-based approach for making ecological inference from distance sampling data, Biometrics, 66, 310, 10.1111/j.1541-0420.2009.01265.x
Kéry, 2011, Towards the modelling of true species distributions, J. Biogeogr., 38, 617, 10.1111/j.1365-2699.2011.02487.x
Kéry, 2015, vol. 1
Knapp, 1998
Koshkina, 2017, Integrated species distribution models: Combining presence-background data and site-occupancy data with imperfect detection, Methods Ecol. Evol., 8, 420, 10.1111/2041-210X.12738
Little, 1992, Regression with missing X’s: A review, J. Amer. Statist. Assoc., 87, 1227
Little, 2019
Martino, 2021, Integration of presence-only data from several sources: A case study on dolphins’ spatial distribution, Ecography, 44, 1533, 10.1111/ecog.05843
Mason, 2012, Strategy for modelling nonrandom missing data mechanisms in observational studies using Bayesian methods, J. Off. Stat., 28, 279
McShea, 2016, Volunteer-run cameras as distributed sensors for macrosystem mammal research, Landsc. Ecol., 31, 55, 10.1007/s10980-015-0262-9
Miller, 2019, The recent past and promising future for data integration methods to estimate species’ distributions, Methods Ecol. Evol., 10, 22, 10.1111/2041-210X.13110
Mohankumar, 2021, Using machine learning to model nontraditional spatial dependence in occupancy data, Ecology, 103
Otis, 1978, Statistical inference from capture data on closed animal populations, Wildl. Monogr., 62, 3
Pollock, 1990, Statistical inference for capture-recapture experiments, Wildl. Monogr., 107, 3
Renner, 2015, Point process models for presence-only analysis, Methods Ecol. Evol., 6, 366, 10.1111/2041-210X.12352
Rubin, 1976, Inference and missing data, Biometrika, 63, 581, 10.1093/biomet/63.3.581
Seber, 1982
Shaffer, 2021
Sicacha-Parada, 2021, Accounting for spatial varying sampling effort due to accessibility in citizen science data: A case study of moose in Norway, Spatial Stat., 42, 10.1016/j.spasta.2020.100446
Warton, 2010, Poisson point process models solve the “pseudo-absence problem” for presence-only data in ecology, Ann. Appl. Stat., 4, 1383
Williams, 2018, Patterns and correlates of within-season breeding dispersal: A common strategy in a declining grassland songbird, Auk Ornithol. Adv., 135, 1
Williams, 2019, Causes and consequences of avian within-season dispersal decisions in a dynamic grassland environment, Anim. Behav., 155, 77, 10.1016/j.anbehav.2019.06.009
Winnicki, 2020, Social interactions do not drive territory aggregation in a grassland songbird, Ecology, 101, 10.1002/ecy.2927