Application of machine learning algorithms to identify cryptic reproductive habitats using diverse information sources

[1]  J. Krebs,et al.  Fish schooling , 2020, Nature.

[2]  Max Kuhn,et al.  Classification and Regression Training [R package caret version 6.0-86] , 2020 .

[3]  A. Adams,et al.  Stress, predators, and survival: Exploring permit (Trachinotus falcatus) catch-and-release fishing mortality in the Florida Keys , 2020 .

[4]  S. Heppell,et al.  Recovery of critically endangered Nassau grouper (Epinephelus striatus) in the Cayman Islands following targeted conservation actions , 2020, Proceedings of the National Academy of Sciences.

[5]  A. Adams,et al.  Seasonal occupancy and connectivity amongst nearshore flats and reef habitats by permit Trachinotus falcatus: considerations for fisheries management. , 2019, Journal of fish biology.

[6]  Vivitskaia J. D. Tulloch,et al.  Where the Ecological Gaps Remain, a Modelers' Perspective , 2019, Front. Ecol. Evol..

[7]  A. Adams,et al.  Bridging the knowledge-action gap: A case of research rapidly impacting recreational fisheries policy , 2019, Marine Policy.

[8]  L. Halsey.  The reign of the p-value is over: what alternative analyses could we employ to fill the power vacuum? , 2019, Biology Letters.

[9]  A. Adams,et al.  Combining data sources to elucidate spatial patterns in recreational catch and effort: fisheries-dependent data and local ecological knowledge applied to the South Florida bonefish fishery , 2018, Environmental Biology of Fishes.

[10]  A. Adams,et al.  Potential connectivity among spatially distinct management zones for Bonefish (Albula vulpes) via larval dispersal , 2018, Environmental Biology of Fishes.

[11]  A. Adams,et al.  Bonefish in South Florida: status, threats and research needs , 2018, Environmental Biology of Fishes.

[12]  Aki Vehtari,et al.  Projective Inference in High-dimensional Problems: Prediction and Feature Selection , 2018, Electronic Journal of Statistics.

[13]  Steven J. Cooke,et al.  Applying a knowledge–action framework for navigating barriers to incorporating telemetry science into fisheries management and conservation: a qualitative study , 2018, Canadian Journal of Fisheries and Aquatic Sciences.

[14]  A. Adams,et al.  Identifying pre-spawning aggregation sites for bonefish (Albula vulpes) in the Bahamas to inform habitat protection and species conservation , 2018, Environmental Biology of Fishes.

[15]  Eric Hervet,et al.  Applications for deep learning in ecology , 2018, bioRxiv.

[16]  S. C. Riley,et al.  Fine‐scale acoustic telemetry reveals unexpected lake trout, Salvelinus namaycush, spawning habitats in northern Lake Huron, North America , 2018 .

[17]  Jennifer M. Durden,et al.  Integrating “Big Data” into Aquatic Ecology: Challenges and Opportunities , 2017 .

[18]  R. Olshen,et al.  Points of Significance: Classification and regression trees , 2017, Nature Methods.

[19]  V. Nguyen,et al.  A roadmap for knowledge exchange and mobilization research in conservation and natural resource management , 2017, Conservation biology : the journal of the Society for Conservation Biology.

[20]  Christopher M Holbrook,et al.  Acoustic telemetry and fisheries management. , 2017, Ecological applications : a publication of the Ecological Society of America.

[21]  Josep Alós,et al.  Reproductive resilience: a paradigm shift in understanding spawner-recruit systems in exploited marine fish , 2017 .

[22]  V. Parravicini,et al.  Extreme Inverted Trophic Pyramid of Reef Sharks Supported by Spawning Groupers , 2016, Current Biology.

[23]  S. Lowerre‐Barbieri,et al.  Assessing reproductive behavior important to fisheries management: a case study with red drum, Sciaenops ocellatus. , 2016, Ecological applications : a publication of the Ecological Society of America.

[24]  M. Heupel,et al.  Ghosts in the data: false detections in VEMCO pulse position modulation acoustic telemetry monitoring equipment , 2015, Animal Biotelemetry.

[25]  Paolo Giordani,et al.  A toolbox for fuzzy clustering using the R programming language , 2015, Fuzzy Sets Syst..

[26]  J. Ault,et al.  Transport and connectivity modeling of larval permit from an observed spawning aggregation in the Dry Tortugas, Florida , 2015, Environmental Biology of Fishes.

[27]  A. Adams,et al.  Advancing the science and management of flats fisheries for bonefish, tarpon, and permit , 2015, Environmental Biology of Fishes.

[28]  J. Kocik,et al.  Aquatic animal telemetry: A panoramic window into the underwater world , 2015, Science.

[29]  Max Kuhn,et al.  caret: Classification and Regression Training , 2015 .

[30]  Jacob E. Allgeier,et al.  Hot moments in spawning aggregations: implications for ecosystem-scale nutrient cycling , 2015, Coral Reefs.

[31]  Olac Fuentes,et al.  Harnessing the power of big data: infusing the scientific method with machine learning to transform ecology , 2014 .

[32]  Debra P. C. Peters,et al.  Taking the pulse of a continent: expanding site???based research infrastructure for regional??? to continental???scale ecology , 2014 .

[33]  Roland Kays,et al.  Observing the unwatchable through acceleration logging of animal behavior , 2013, Animal Biotelemetry.

[34]  R. Arlinghaus,et al.  Voluntary institutions and behaviours as alternatives to formal regulations in recreational fisheries management , 2013 .

[35]  V. Nguyen,et al.  Mobilizing New Science into Management Practice: The Challenge of Biotelemetry for Fisheries Management, a Case Study of Canada's Fraser River , 2013 .

[36]  J. Atema,et al.  Reef Odor: A Wake Up Call for Navigation in Reef Fish Larvae , 2013, PloS one.

[37]  S. Lowerre‐Barbieri,et al.  Site fidelity and reproductive timing at a spotted seatrout spawning aggregation site: individual versus population scale behavior , 2013 .

[38]  Matthew B Jones,et al.  Ecoinformatics: supporting ecology as a data-intensive science. , 2012, Trends in ecology & evolution.

[39]  J. Claisse,et al.  The illusion of plenty: hyperstability masks collapses in two recreational fisheries that target fish spawning aggregations , 2011 .

[40]  Steven J. Cooke,et al.  Aggregations and offshore movements as indicators of spawning activity of bonefish (Albula vulpes) in The Bahamas , 2011 .

[41]  F. Cagnacci,et al.  Animal ecology meets GPS-based radiotelemetry: a perfect storm of opportunities and challenges , 2010, Philosophical Transactions of the Royal Society B: Biological Sciences.

[42]  W. Heyman,et al.  Sea bottom geomorphology of multi-species spawning aggregation sites in Belize , 2010 .

[43]  Alain F. Zuur,et al.  A protocol for data exploration to avoid common statistical problems , 2010 .

[44]  L. Dagorn,et al.  First field-based experiment supporting the meeting point hypothesis for schooling in pelagic fish , 2009, Animal Behaviour.

[45]  T. Guilford,et al.  Migration and stopover in a small pelagic seabird, the Manx shearwater Puffinus puffinus: insights from machine learning , 2009, Proceedings of the Royal Society B: Biological Sciences.

[46]  Holger R. Maier,et al.  Future research challenges for incorporation of uncertainty in environmental and ecological decision-making , 2008 .

[47]  W. Heyman,et al.  Geomorphometric Patterns of Nassau Grouper (Epinephelus striatus) Spawning Aggregation Sites in the Cayman Islands , 2008 .

[48]  R. Brook,et al.  Trends and prospects for local knowledge in ecological and conservation research and monitoring , 2008, Biodiversity and Conservation.

[49]  Achim Zeileis,et al.  BMC Bioinformatics BioMed Central Methodology article Conditional variable importance for random forests , 2008 .

[50]  J Elith,et al.  A working guide to boosted regression trees. , 2008, The Journal of animal ecology.

[51]  Julian D Olden,et al.  Machine Learning Methods Without Tears: A Primer for Ecologists , 2008, The Quarterly Review of Biology.

[52]  C. Roberts,et al.  Historical and contemporary evidence of a mutton snapper (Lutjanus analis Cuvier, 1828) spawning aggregation fishery in decline , 2008, Coral Reefs.

[53]  Louie H. Yang,et al.  Comparing resource pulses in aquatic and terrestrial ecosystems. , 2008, Ecology.

[54]  Lisa Gibbs,et al.  Generating best evidence from qualitative research: the role of data analysis , 2007, Australian and New Zealand journal of public health.

[55]  A. Salski,et al.  Fuzzy clustering of fuzzy ecological data , 2007, Ecol. Informatics.

[56]  Achim Zeileis,et al.  Bias in random forest variable importance measures: Illustrations, sources and a solution , 2007, BMC Bioinformatics.

[57]  A. Aguilar-Perera.  Disappearance of a Nassau grouper spawning aggregation off the southern Mexican Caribbean coast , 2006 .

[58]  K. Hornik,et al.  Unbiased Recursive Partitioning: A Conditional Inference Framework , 2006 .

[59]  Gabriel J Bowen,et al.  Stable isotopes as one of nature's ecological recorders. , 2006, Trends in ecology & evolution.

[60]  A. Begossi,et al.  When Does this Fish Spawn? Fishermen’s Local Knowledge of Migration and Reproduction of Brazilian Coastal Fishes , 2006, Environmental Biology of Fishes.

[61]  A. Adams,et al.  Patterns of Juvenile Habitat Use and Seasonality of Settlement by Permit, Trachinotus falcatus , 2006, Environmental Biology of Fishes.

[62]  Y. Sadovy,et al.  Are aggregation-fisheries sustainable? Reef fish fisheries as a case study , 2005, Coral Reefs.

[63]  Oscar E. Gaggiotti,et al.  Ecology, genetics, and evolution of metapopulations , 2004 .

[64]  G. Tang,et al.  Indian Hedgehog: A Mechanotransduction Mediator in Condylar Cartilage , 2004, Journal of dental research.

[65]  D. Cato,et al.  Coral‐reef sounds enable nocturnal navigation by some reef‐fish larvae in some places and at some times , 2003 .

[66]  Pierre Taberlet,et al.  Landscape genetics: combining landscape ecology and population genetics , 2003 .

[67]  L. Breiman.  Random Forests , 2001, Machine Learning.

[68]  E. Sala,et al.  Rapid Decline of Nassau Grouper Spawning Aggregations in Belize: Fishery Management and Conservation Needs , 2001 .

[69]  G. De’ath,et al.  CLASSIFICATION AND REGRESSION TREES: A POWERFUL YET SIMPLE TECHNIQUE FOR ECOLOGICAL DATA ANALYSIS , 2000 .

[70]  Phillip S. Lobel,et al.  Predator-prey relations at a spawning aggregation site of coral reef fishes , 2000 .

[71]  Young-Seuk Park,et al.  Determining temporal pattern of community dynamics by using unsupervised learning algorithms , 2000 .

[72]  V. J. Rayward-Smith,et al.  Fuzzy Cluster Analysis: Methods for Classification, Data Analysis and Image Recognition , 1999 .

[73]  D. Zeller.  Spawning aggregations: patterns of movement of the coral trout Plectropomus leopardus (Serranidae) as determined by ultrasonic telemetry , 1998 .

[74]  R. Michener,et al.  Stable isotopes in ecology and environmental science , 1995 .

[75]  C. Walters,et al.  Uncertainty, resource exploitation, and conservation: lessons from history. , 1993, Science.

[76]  Miguel Equihua,et al.  Fuzzy clustering of ecological data. , 1990 .

[77]  S. Pittman,et al.  Fish spawning aggregations: where well-placed management actions can yield big benefits for fisheries and conservation , 2017 .

[78]  D. R. Cutler,et al.  Utah State University From the SelectedWorks of , 2017 .

[79]  Achim Zeileis,et al.  Partykit: a modular toolkit for recursive partytioning in R , 2015, J. Mach. Learn. Res..

[80]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[81]  Y. Sadovy,et al.  Reef Fish Spawning Aggregations: Biology, Research and Management , 2012, Fish & Fisheries Series.

[82]  Y. S. D. Mitcheson,et al.  The Role of Local Ecological Knowledge in the Conservation and Management of Reef Fish Spawning Aggregations , 2012 .

[83]  Falk Huettmann,et al.  Alternative methods to quantify variable importance in ecology , 2009 .

[84]  Achim Zeileis,et al.  Party on! A new, conditional variable importance measure available in the party package , 2009 .

[85]  T. Hothorn,et al.  Party on! , 2009, R J..

[86]  Andy Liaw,et al.  Classification and Regression by randomForest , 2007 .

[87]  R. Evershed,et al.  Stable Isotopes in Ecology and Environmental Science (2nd edition) , 2007 .

[88]  C. Menzies.  Traditional Ecological Knowledge and Natural Resource Management , 2006 .

[89]  R. Graham,et al.  Courtship and spawning behaviors of carangid species in Belize , 2005 .

[90]  A. Adams,et al.  Spatial Patterns of Estuarine Habitat Type Use and Temporal Patterns in Abundance of Juvenile Permit, Trachinotus falcatus, in Charlotte Harbor, Florida , 2004 .

[91]  D. Snodgrass,et al.  Age, growth, and reproduction of permit (Trachinotus falcatus) in Florida waters , 2002 .

[92]  Marco Tulio Ribeiro,et al.  “Why Should I Trust You?”: Explaining the Predictions of Any Classifier , 2016, NAACL.