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SNH Commissioned Report 594: Statistical Approaches to Aid the Identification of Marine Protected Areas for Minke Whale, Risso

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SNH Commissioned Report 594: Statistical Approaches to Aid the Identification of Marine Protected Areas for Minke Whale, Risso Scottish Natural Heritage Commissioned Report No. 594 Statistical approaches to aid the identification of Marine Protected Areas for minke whale, Risso’s dolphin, white- beaked dolphin and basking shark COMMISSIONED REPORT Commissioned Report No. 594 Statistical approaches to aid the identification of Marine Protected Areas for minke whale, Risso’s dolphin, white-beaked dolphin and basking shark For further information on this report please contact: Morven Carruthers Scottish Natural Heritage Great Glen House INVERNESS IV3 8NW Telephone: 01463 725018 E-mail: [email protected] This report should be quoted as: Paxton, C.G.M., Scott-Hayward, L.A.S. & Rexstad, E. 2014. Statistical approaches to aid the identification of Marine Protected Areas for minke whale, Risso’s dolphin, white-beaked dolphin and basking shark. Scottish Natural Heritage Commissioned Report No. 594. This report, or any part of it, should not be reproduced without the permission of Scottish Natural Heritage. This permission will not be withheld unreasonably. The views expressed by the author(s) of this report should not be taken as the views and policies of Scottish Natural Heritage. © Scottish Natural Heritage 2014. COMMISSIONED REPORT Summary Statistical approaches to aid the identification of Marine Protected Areas for minke whale, Risso’s dolphin, white- beaked dolphin and basking shark Commissioned Report No. 594 Project no: 13926 Contractor: Paxton, C.G.M., Scott-Hayward, L.A.S. & Rexstad, E. Year of publication: 2014 Keywords Statistical modelling; Marine Protected Areas; Risso’s dolphin; white-beaked dolphin; minke whale; basking shark; Scottish territorial waters. Summary The 2010 Marine (Scotland) Act makes provision for Nature Conservation Marine Protected Areas (hereafter MPAs). In response to this Marine Scotland established the Scottish MPA Project to select MPAs and develop the Scottish MPA network. Of particular interest here are four megafaunal species that are being used to aid the identification of MPAs in Scottish territorial waters: Risso’s dolphin (Grampus griseus), white-beaked dolphin (Lagenorhynchus albirostris), minke whale (Balaenoptera acutorostrata) and basking shark (Cetorhinus maximus). Areas that support significant aggregations of these species, or that are essential for key life cycle stages may be relevant to consider for designation. The aim of this project was to identify regions of persistent use by each of these species with a view to informing MPA designation. Here we used a combined data set of marine megafaunal surveys to create a spatially indexed set of adjusted densities. Spatial models were fitted to these data sets for the four species above. These models were then used to make seasonal and annual predictions of relative density over the entire Scottish territorial waters which could inform MPA decision making. Effort-linked sightings data contained within the Joint Cetacean Protocol (JCP) plus additional data sourced by Scottish Natural Heritage were used to generate estimated densities ( ) per area surveyed (corrected for detection/availability) for minke whale (2000 – 2012), Risso’s dolphin (1994 – 2012) and white-beaked dolphin (1994 – 2012). A further relative density per area surveyed index was obtained for basking shark (2000 – 2012). There were up to 23 distinct data sources used for each analysis (25 used in total) with data from at least 172 distinct survey platforms (ships and aircraft) representing up to 180300 km of effort depending on the species considered. i The analyses for each species followed this procedure: 1. Estimates were derived of the probability of detecting a group of animals as a function of covariates affecting detectability measured on all surveys. This detection probability had up to three components: (a) probability of detecting a group given that it was available for detection on the surface and assuming all groups on the survey trackline were seen with certainty; (b) probability of detecting a group on the trackline given it was available for detection (“perception bias”); and (c) probability of a group being available for detection at the surface (“availability bias”). The first component was estimated using available line transect data, modelling detection probability as a function of available covariates such as group size and vessel type. The second component for some vessel types and species was estimated from a limited amount of double-observer line transect data, as well as previous published analyses. The third component was obtained from a limited amount of published data combined with expert opinion. 2. The survey data were divided into short (target approx. 10 km) segments of effort and the observed number of animals per segment was converted into an estimated abundance of animals per segment using the estimated detection probabilities and probability of being at the surface (for the cetacean species only). 3. The following predictor variables were allocated to effort segments for density surface modelling: Depth, Day of Year, Sediment Type, Sea Surface Temperature, a Front Index, a Tidal Energy Index, Chlorophyll concentration and, optionally, position as a 2D smooth. 4. Spatial generalized estimating equations (GEE) models were fitted to the data using methods that allow for modelling around complex topography as well as locally adaptive smoothed responses to predictors. Predictions of relative density were made for all Scottish territorial waters. 5. Uncertainty in the surfaces was generated by means of a bootstrap. Total uncertainty was considered with and without the uncertainty associated with model selection. 6. Persistent areas of relative high density (defined as cells of relative density greater than the mean relative density for that point in time) were identified by considering the summer prediction surface for each year of the data. 7. Uncertainty in the persistence surface was incorporated by means of the bootstrap, resulting in a persistence-certainty score for each 5 km by 5 km cell around the Scottish coast. Identified areas of interest for minke whale included the areas south and west of the Hebrides, the sea of the Hebrides and the Moray Firth. The single identified contiguous higher than average density area for Risso’s dolphin was the region to the north of Lewis/Harris. White-beaked dolphins were widely dispersed in slightly offshore waters. Identified areas for basking shark included the waters of the eastern Sea of the Hebrides and to the west of the Hebrides. The project also identified several data poor regions notably west of the Hebrides, around the Isle of Arran, the coast of Sutherland and Caithness, Orkney and Shetland. For further information on this project contact: Morven Carruthers, Scottish Natural Heritage, Great Glen House, Inverness, IV3 8NW. Tel: 01463 725018 or [email protected] For further information on the SNH Research & Technical Support Programme contact: Knowledge & Information Unit, Scottish Natural Heritage, Great Glen House, Inverness, IV3 8NW. Tel: 01463 725000 or [email protected] ii Table of Contents Page 1. INTRODUCTION 1 2. METHODS 2 2.1 Overview of Methods 2 2.2 Overview of the Data 4 2.2.1 Spatial and Temporal Range of the Dependent Data 4 2.2.2 Predictor Variables 7 2.2.3 Spatial Data Processing 16 2.3 Detection Function Modelling 16 2.3.1 Sightings Classes 16 2.3.2 Fitting Detection Functions 18 2.3.3 Detection Without Distances 18 2.4 Adjustments in Addition to Detectability 19 2.4.1 Perception Bias (g(0)) 19 2.4.2 Availability bias 19 2.5 Density Surface Modelling 20 2.5.1 Partitioning Data into Segments 20 2.5.2 Model Fitting 23 2.5.3 Model selection 24 2.5.4 Prediction 25 2.5.5 Estimation of Uncertainty 26 2.5.6 Estimating the Effect of Prediction Area on Prediction Accuracy 27 2.6 Investigating Persistence 27 2.6.1 Testing Persistence 27 2.6.2 Illustrating Persistence 28 3. RESULTS 29 3.1 Detection Function Results 29 3.1.1 Minke whale 29 3.1.2 Dolphins 30 3.1.3 Basking Shark 32 3.2 Adjustments to Detectability 33 3.2.1 Detection on the Trackline (g(0)) 33 3.2.2 Availability at the surface 34 3.3 Realized Effort 37 3.4 Spatial Modelling 44 3.4.1 Minke whale 47 3.4.2 Risso’s dolphin 55 3.4.3 White-beaked dolphin 67 3.4.4 Basking shark 78 4. DISCUSSION 87 4.1 Effort Coverage 87 4.2 Interpretation of Surfaces 87 4.2.1 Minke whale 88 4.2.2 Risso’s dolphin 88 4.2.3 White-beaked dolphin 89 4.2.4 Basking shark 89 4.3 Conclusions & Future Work 89 5. REFERENCES 91 APPENDIX 1: DESCRIPTION OF THE DATASETS AND INCLUSION CRITERIA 104 APPENDIX 2: PARAMETERS OF DETECTION FUNCTIONS 116 iii APPENDIX 3: DETAILS OF THE SPATIAL MODELS 117 APPENDIX 4: BIOLOGICAL BACKGROUND FOR CETACEANS (PETER EVANS, UNIVERSITY OF BANGOR & SEA WATCH FOUNDATION) 118 APPENDIX 5: BIOLOGICAL BACKGROUND FOR BASKING SHARK (PHILIP DOHERTY & MATTHEW WITT, UNIVERSITY OF EXETER) 123 iv Acknowledgements Our thanks to all those individuals and groups associated with collecting, compiling, organizing and providing data to the JCP analyses that were used in this analysis, especially Mick Baines for Phase I and Tim Dunn for Phases II & III of the JCP and Morven Carruthers for this project. The individual contributors/organisations included in this project were: Colin Macleod and Sarah Bannon (University of Aberdeen); DECC; EDP Renewables and Repsol Nuevas Energias UK; European Seabirds at Sea data providers; the UK Government and the Oil and Gas Industry for funding Seabirds at Sea Surveys; Hebridean Whale and Dolphin Trust; Henrik
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