Integrating telemetry and point observations to inform management and conservation of migratory marine species

[1]  Jiangang Luo,et al.  First Atlantic satellite tracks of ‘lost years’ green turtles support the importance of the Sargasso Sea as a sea turtle nursery , 2021, Proceedings of the Royal Society B.

[2]  Danielle E. Haulsee,et al.  A satellite‐based mobile warning system to reduce interactions with an endangered species , 2021, Ecological applications : a publication of the Ecological Society of America.

[3]  H. Bailey,et al.  Using fisheries observation data to develop a predictive species distribution model for endangered sea turtles , 2021, Conservation Science and Practice.

[4]  Gang Liu,et al.  CoralTemp and the Coral Reef Watch Coral Bleaching Heat Stress Product Suite Version 3.1 , 2020, Remote. Sens..

[5]  Melissa S. Chapman,et al.  A comparative analysis of dynamic management in marine and terrestrial systems , 2020, Frontiers in Ecology and the Environment.

[6]  Jaime Fern'andez del R'io,et al.  Array programming with NumPy , 2020, Nature.

[7]  M. Hooten,et al.  Animal movement models with mechanistic selection functions , 2019, Spatial Statistics.

[8]  O. Gimenez,et al.  Combining multiple data sources in species distribution models while accounting for spatial dependence and overfitting with combined penalized likelihood maximization , 2019, Methods in Ecology and Evolution.

[9]  Joel Nothman,et al.  SciPy 1.0-Fundamental Algorithms for Scientific Computing in Python , 2019, ArXiv.

[10]  P. I. Miller,et al.  On the use of satellite-derived frontal metrics in time series analyses of shelf-sea fronts, a study of the Celtic Sea , 2019, Deep Sea Research Part I: Oceanographic Research Papers.

[11]  Peter I. Miller,et al.  Predicting residence time using a continuous‐time discrete‐space model of leatherback turtle satellite telemetry data , 2019, Ecosphere.

[12]  Krishna Pacifici,et al.  The recent past and promising future for data integration methods to estimate species’ distributions , 2019, Methods in Ecology and Evolution.

[13]  Elliott L. Hazen,et al.  Practical considerations for operationalizing dynamic management tools , 2018, Journal of Applied Ecology.

[14]  Shinichiro Shirota,et al.  Preferential sampling for presence/absence data and for fusion of presence/absence data with presence-only data , 2018, bioRxiv.

[15]  Peter Leimgruber,et al.  Disentangling social interactions and environmental drivers in multi-individual wildlife tracking data , 2018, Philosophical Transactions of the Royal Society B: Biological Sciences.

[16]  Helen Bailey,et al.  A dynamic ocean management tool to reduce bycatch and support sustainable fisheries , 2018, Science Advances.

[17]  Danielle E. Haulsee,et al.  Satellite driven distribution models of endangered Atlantic sturgeon occurrence in the mid-Atlantic Bight , 2018 .

[18]  Mark J. Costello,et al.  A Systematic Review of Marine-Based Species Distribution Models (SDMs) with Recommendations for Best Practice , 2017, Front. Mar. Sci..

[19]  Dana K. Briscoe,et al.  Fit to predict? Eco-informatics for predicting the catchability of a pelagic fish in near real time. , 2017, Ecological applications : a publication of the Ecological Society of America.

[20]  Helen Bailey,et al.  WhaleWatch: a dynamic management tool for predicting blue whale density in the California Current , 2017 .

[21]  G. Tavecchia,et al.  High and variable mortality of leatherback turtles reveal possible anthropogenic impacts. , 2017, Ecology.

[22]  S. Wood.  Generalized Additive Models: An Introduction with R, Second Edition , 2017 .

[23]  Yang Wang,et al.  Integrated species distribution models: combining presence‐background data and site‐occupancy data with imperfect detection , 2017 .

[24]  Krishna Pacifici,et al.  Integrating multiple data sources in species distribution modeling: a framework for data fusion. , 2017, Ecology.

[25]  Brett T. McClintock,et al.  Animal Movement: Statistical Models for Telemetry Data , 2017 .

[26]  M. Cowles,et al.  Bayesian MODIS NDVI back-prediction by intersensor calibration with AVHRR , 2016 .

[27]  M. Hooten,et al.  Hierarchical Species Distribution Models , 2016 .

[28]  Jacob S. Ivan,et al.  Hierarchical animal movement models for population‐level inference , 2016, 1606.09585.

[29]  Christophe Giraud,et al.  Capitalizing on opportunistic data for monitoring relative abundances of species , 2016, Biometrics.

[30]  Jason J. Roberts,et al.  Habitat-based cetacean density models for the U.S. Atlantic and Gulf of Mexico , 2016, Scientific Reports.

[31]  E. Hazen,et al.  Statistical integration of tracking and vessel survey data to incorporate life history differences in habitat models. , 2015, Ecological applications : a publication of the Ecological Society of America.

[32]  Peter I. Miller,et al.  Basking sharks and oceanographic fronts: quantifying associations in the north‐east Atlantic , 2015 .

[33]  Alistair J. Hobday,et al.  Dynamic ocean management: Defining and conceptualizing real-time management of the ocean , 2015 .

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

[35]  Steven J. Phillips,et al.  Point process models for presence‐only analysis , 2015 .

[36]  James C. Russell,et al.  Dynamic Models of Animal Movement with Spatial Point Process Interactions , 2015, 1503.08692.

[37]  Alistair J. Hobday,et al.  Dynamic Ocean Management: Identifying the Critical Ingredients of Dynamic Approaches to Ocean Resource Management , 2015 .

[38]  Elliott L. Hazen,et al.  Climate change impacts on leatherback turtle pelagic habitat in the Southeast Pacific , 2015 .

[39]  Brendan A. Wintle,et al.  Is my species distribution model fit for purpose? Matching data and models to applications , 2015 .

[40]  P. I. Miller,et al.  Oceanic loggerhead turtles Caretta caretta associate with thermal fronts: Evidence from the Canary Current Large Marine Ecosystem , 2015 .

[41]  Timothy F. R. Burgess,et al.  Reef-Scale Thermal Stress Monitoring of Coral Ecosystems: New 5-km Global Products from NOAA Coral Reef Watch , 2014, Remote. Sens..

[42]  T. Hastie,et al.  Bias correction in species distribution models: pooling survey and collection data for multiple species , 2014, Methods in ecology and evolution.

[43]  Mevin B. Hooten,et al.  Temporal variation and scale in movement-based resource selection functions , 2014 .

[44]  Jason J. Roberts,et al.  Pelagic movements of pacific leatherback turtles (dermochelys coriacea) highlight the role of prey and ocean currents , 2013 .

[45]  Connie Y. Kot,et al.  Impacts of fisheries bycatch on marine turtle populations worldwide: toward conservation and research priorities , 2013 .

[46]  P. Dutton,et al.  Long‐term decline of the western Pacific leatherback, Dermochelys coriacea: a globally important sea turtle population , 2013 .

[47]  R. Morrow,et al.  A new estimate of the global 3D geostrophic ocean circulation based on satellite data and in-situ measurements , 2012 .

[48]  Mevin B. Hooten,et al.  Continuous-time discrete-space models for animal movement , 2012, 1211.1992.

[49]  P. L. Traon,et al.  High Resolution 3-D temperature and salinity fields derived from in situ and satellite observations , 2012 .

[50]  Brendan J. Godley,et al.  Trading information for conservation: a novel use of radio broadcasting to reduce sea turtle bycatch , 2012, Oryx.

[51]  Helen Bailey,et al.  Ontogeny in marine tagging and tracking science: technologies and data gaps , 2012 .

[52]  Rory P. Wilson,et al.  Tagging through the stages: technical and ecological challenges in observing life histories through biologging , 2012 .

[53]  G. Hays,et al.  Movement Patterns for a Critically Endangered Species, the Leatherback Turtle (Dermochelys coriacea), Linked to Foraging Success and Population Status , 2012, PloS one.

[54]  S. Bograd,et al.  Identification of distinct movement patterns in Pacific leatherback turtle populations influenced by ocean conditions. , 2012, Ecological applications : a publication of the Ecological Society of America.

[55]  J. Fieberg,et al.  Comparative interpretation of count, presence–absence and point methods for species distribution models , 2012 .

[56]  Jane Elith,et al.  Pushing the limits in marine species distribution modelling: lessons from the land present challenges and opportunities , 2011 .

[57]  K. M. Schaefer,et al.  Tracking apex marine predator movements in a dynamic ocean , 2011, Nature.

[58]  S. Bograd,et al.  Vertical and horizontal habitat preferences of post-nesting leatherback turtles in the South Pacific Ocean. , 2011 .

[59]  S. Bograd,et al.  Identification of high-use internesting habitats for eastern Pacific leatherback turtles: role of the environment and implications for conservation , 2010 .

[60]  D. Fabre,et al.  Global Bathymetry and Elevation Data at 30 Arc Seconds Resolution: SRTM30_PLUS , 2009 .

[61]  P. I. Miller.  Composite front maps for improved visibility of dynamic sea-surface features on cloudy SeaWiFS and AVHRR data , 2009 .

[62]  Janet Franklin,et al.  Mapping the bycatch seascape: multispecies and multi-scale spatial patterns of fisheries bycatch. , 2009, Ecological applications : a publication of the Ecological Society of America.

[63]  J. Elith,et al.  Species Distribution Models: Ecological Explanation and Prediction Across Space and Time , 2009 .

[64]  George H. Balazs,et al.  TurtleWatch: A tool to aid in the bycatch reduction of loggerhead turtles Caretta caretta in the Hawaii-based pelagic longline fishery , 2008 .

[65]  V. Saba,et al.  Effects of Illegal Harvest of Eggs on the Population Decline of Leatherback Turtles in Las Baulas Marine National Park, Costa Rica , 2008, Conservation biology : the journal of the Society for Conservation Biology.

[66]  A. Peterson,et al.  Effects of sample size on the performance of species distribution models , 2008 .

[67]  S. Bograd,et al.  Persistent Leatherback Turtle Migrations Present Opportunities for Conservation , 2008, PLoS biology.

[68]  James C. McWilliams,et al.  Gradient-based edge detection and feature classification of sea-surface images of the Southern California Bight , 2008 .

[69]  Helen Bailey,et al.  Identifying and comparing phases of movement by leatherback turtles using state-space models , 2008 .

[70]  Bernie J. McConnell,et al.  Estimating space‐use and habitat preference from wildlife telemetry data , 2008 .

[71]  Ian D. Jonsen,et al.  Identifying leatherback turtle foraging behaviour from satellite telemetry using a switching state-space model , 2007 .

[72]  P. Dutton,et al.  Status and Genetic Structure of Nesting Populations of Leatherback Turtles (Dermochelys coriacea) in the Western Pacific , 2007 .

[73]  Alistair J. Hobday,et al.  Near real‐time spatial management based on habitat predictions for a longline bycatch species , 2006 .

[74]  S. Wood.  ON CONFIDENCE INTERVALS FOR GENERALIZED ADDITIVE MODELS BASED ON PENALIZED REGRESSION SPLINES , 2006 .

[75]  Edzer J. Pebesma,et al.  Multivariable geostatistics in S: the gstat package , 2004, Comput. Geosci..

[76]  J. Kerr,et al.  From space to species: ecological applications for remote sensing , 2003 .

[77]  S. Wood.  Thin plate regression splines , 2003 .

[78]  S. Eckert,et al.  Swim speed and movement patterns of gravid leatherback sea turtles (Dermochelys coriacea) at St Croix, US Virgin Islands. , 2002, The Journal of experimental biology.

[79]  David R. B. Stockwell,et al.  Effects of sample size on accuracy of species distribution models , 2002 .

[80]  Antoine Guisan,et al.  Predictive habitat distribution models in ecology , 2000 .

[81]  R. Reina,et al.  Pacific leatherback turtles face extinction , 2000, Nature.

[82]  P. Dutton,et al.  Global phylogeography of the leatherback turtle ( Dermochelys coriacea ) , 1999 .

[83]  S. Morreale,et al.  Migration corridor for sea turtles , 1996, Nature.

[84]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[85]  Shinto Eguchi,et al.  Sampling bias correction in species distribution models by quasi-linear Poisson point process , 2020, Ecol. Informatics.

[86]  Brian C. Stock,et al.  Comparing predictions of fisheries bycatch using multiple spatiotemporal species distribution model frameworks , 2020 .

[87]  Jonathan P. Rose,et al.  Integrating multiple data sources and multi-scale land-cover data to model the distribution of a declining amphibian , 2020 .

[88]  M. Plummer.  Cuts in Bayesian graphical models , 2015, Stat. Comput..

[89]  Helen Bailey,et al.  Enhancing the TurtleWatch product for leatherback sea turtles, a dynamic habitat model for ecosystem‐based management , 2015 .