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Habitat template approach to benthic habitat mapping Vladimir Kostylev Natural Resources Canada Bedford Institute of Oceanography Pluto didn't make the cut • • • • • The International Astronomical Union, a worldwide society of astronomers from 75 countries voted on the final proposal after more than a week of heated debates over the standards in properly classifying celestial bodies into different categories. The new guidelines — introduced in Prague in August 2006 after a week of debate by the 2,500 astronomers at the organization's conference — define what is a planet and what is not. Pluto didn't make the cut. The "New Definition" explicitly says that "a planet is a celestial body that is in orbit around the sun, has sufficient mass for self-gravity to overcome rigid body forces so that it assumes an almost round shape, and has cleared the neighborhood around its orbit." “Astronomers have been working without a solid definition of a planet since the days of Copernicus. The new definition fills that void.” Habitat classification • “It should be understood that we are not referring to here to the realities or limits of these subdivisions or the problems of zonation on the shore, but to the manner in which the terms have been applied”. – Joel Hedgpeth (1957) In: National Research Council (U.S.). Committee on a Treatise on Marine Ecology and Paleoecology. What are habitats? • For mapping purposes, habitats are defined as spatially recognizable areas where the physical, chemical and biological environment is distinctly different from surrounding areas. • The degree of similarity is defined ecologically. Which characteristics should habitat maps have? • Should be based on repeatable methodology – Assumptions, techniques are explicit • Should have low uncertainty – data, positional, taxonomic, statistical, boundary, drill-down • Should reflect temporal continuity of the environment – They will not be repeated often • Should be useful four understanding of ecosystem processes – Responses of ecosystems to management actions • Should reflect and predict emergent properties – Summaries instead of compilations • Should be useful to managers – (like navigation charts to sailors, not too complex) www.resortmaps.com Thick sand Thin sand Thin mud Video + acoustic - Armoured sand Armoured mud Video + grab - Thick mud Acoutics- bio + Till Bedrock Classification- bio + Seabed surface ? Remote sensing (acoustic) data Physical samples (groundtruthing) ? Texture, stratigraphy, etc. Interpretation of Morphology Hardness ? Roughness ? Surficial geology map/ Substrate map Assumptions on animal-sediment ? coupling ? Benthic habitat map Which paths should be tested? Benthic distributions Benthic populations Benthic distribution data Benthic habitat map Seabed surface Physical sediment samples Benthic populations Benthic distribution data Assumptions on animal-sediment coupling Benthic habitat map Seabed surface Remote sensing (acoustic) data Interpretation of seabed Morphology, Processes etc. Assumptions on animal-sediment coupling Benthic habitat map Seabed surface Remote sensing (acoustic) data Physical samples (groundtruthing) Texture, stratigraphy, etc. Interpretation of Morphology Hardness Roughness Surficial geology map/ Substrate map Assumptions on animal-sediment coupling Benthic habitat map A – series map of German Bank surficial geology B. Todd 2008 Three paths • The path from remote sensing to habitat is lacking groundtruthing information, • The path from geological samples to habitat map lacks coverage and definition of boundaries, • The path from benthic community samples to habitat map is as problematic as creating geological maps from grab samples alone without acoustic information which provides spatial context. Seabed surface Benthic populations ? Remote sensing (acoustic) data Physical samples (groundtruthing) ? Texture, stratigraphy, etc. Interpretation of Morphology Hardness ? Roughness ? Surficial geology map/ Substrate map + Other important environmental factors Assumptions on animal-habitat ? coupling ? Benthic habitat map Benthic distribution data Animal-sediment coupling assumptions • The classical marine ecology assumption that similar groups of species occur on similar substrates (Thorson 1957) – Species diversity is shown a significant function of sediment particle size diversity – Composition of sediment communities can be influenced by sediment particle size • Effects of sediment grain size on benthos produced contradictory results (e.g. Snelgrove and Butman 1994) • Arbitrary subdivisions between different classes in geological classifications do not need to be meaningful to benthic fauna. • Effects of geomorphological features on benthos are usually induced from ordination techniques and not followed by hypothesis testing • Processes had not been considered Saint John harbor Sable Island Gully Browns Bank Assumptions implicit to habitat maps • • Assumption that geological boundaries are accurate and meaningful Assumption that species are habitat specialists (as the opposite to generalists) – Some animals have well-defined functional association with the sediment type (sand lance), sessile animals attaching to sediment. • • • • • Assumption that animals have 0m home range Relative homogeneity of other important variables (oceanography) Assumption of temporal stability of patterns Assumption of causal relationship Empirical data problem: If in a field study we find a relationship between a particular type of sediment and a particular species, does it mean that – 1 this will work in unsampled areas, – 2 this will work in other years/seasons – 3 the studied species or assemblages will always be found where this particular sediment type exists. 1000 EROSION current speed (cm/sec) 100 10 TRANSPORTATION hydraulically rough 1 DEPOSITION 0.1 hydraulically smooth 0.01 0.001 0.0001 CLAY SILT SAND 0.001 0.01 0.1 1 Particle size (mm) GRAVEL 10 100 1000 Fishery and regulatory priorities Fishery: • Get higher catches • Decrease fishing effort and cost • Make fishery sustainable = make profits sustainable Governments: • Preserve rare species and exploited stocks • Preserve habitats (vulnerable, sensitive, unique) and ecosystems • Preserve biodiversity Both sides require: Knowledge of habitat properties and distribution Knowledge of life history traits and habitat association of species •Putting demographic r-K theory in habitat context •“Energy allocation” and “adaptive strategy” •Determined by genetical and environmental constraints which result in trade-offs in life history traits •Physiological adaptation •Ontogenetic processes •Reproduction and somatic growth •Behaviour (predator avoidance and migration) •Applies to species and communities The Blender habitat model reproduction growth biomass stress energy removal What these habitats do to the species occupying them? POPULATION RECOVERY FROM IMPACT Fast Slow Low Migration high Offspring medium large Longevity medium Tolerance high R Migration high Offspring many small Longevity small Tolerance low Risk of habitat destruction High K rK Migration low Offspring few and large Longevity great Tolerance high Migration low Offspring medium and small Longevity medium Tolerance low Scope for Growth Disturbance A Biotic interactions Fish Winemiller 1995 Size at maturity Maximum size Growth rate Fecundity Max age Egg size Parental investment J.R.KING &G.A.MCFARLANE 2003 Disturbance We define disturbance as the ratio of the characteristic friction velocity to the critical shear stress required for initiation of sediment movement (1) high resolution bathymetry of the region (the analysis grid), (2) 42 year hindcast of the wave height and period data, (3) near-bottom tidal current extracted from models; (4) grain size estimates Disturbance 0 Kostylev and Hannah 2007 1 Scope for growth Reflects energy available for growth and reproduction of a species after accounting for energy spent adapting to the environment. Fuzzy index SfG = (Fa + Tm – Ta – Ti + O)/5 Fa = C – S Scope for Growth 1 Kostylev and Hannah 2007 Similarity in template values % 100 95 90 85 80 75 Similarity in species % 60 40 20 0 70 0 20000 40000 60000 80000 100000 120000 140000 160000 180000 Distance (m) Figure 4. Comparison of the relationship of Bray-Curtis similarity in species composition with distance between samples and with similarity in habitat template values. The closer two samples are within the habitat template the more likely they are to have similar faunas, while their spatial proximity is a poor predictor. Similarity in species composition is spline interpolated, red - high, green - low. D is tu r b a n c e Gorgonians Arctica Brachiopoda Cucumaria Filograna Flabellum Lobster Modiolus Placopecten Potamilla Sand dollar Snow crab Mactromeris Sponges Kostylev and Hannah 2007 Scope for growth ? Disturbance Scope for growth ir sk Risk map (O’Boyle, Kostylev, et al 2006) 150 km Kostylev and Hannah 2007 A. Adversity B. Disturbance Edward Gregr, 2006. Digital data for the benthic classification of British Columbia offshore waters. Technical Report. C. Habitat Template D=disturbed S=stable A=adverse B=benign D. Habitat Template With Winter Flounder Noji, T.T., Fromm, S., Fromm, S., Vitaliano, J. and Smith, K. 2008. Habitat Suitability Modeling using the Kostylev Approach as an Indicator of Distribution of Benthic Invertebrates. In ICES Annual Science Conference. International Council for the Exploration of the Sea, Halifax, Nova Scotia, Canada, p. 5 K. Smith. 2005. A benthic habitat model for the Gulf of Maine. Scope for Growth Disturbance r Beaufort Sea (climate change) Kostylev et al. in prep. Scope for Growth Scotian Shelf (fishery) 1000 km This work is possible because of help, advice, encouragement and hard work provided by: Brian Todd, Charles Hannah, Bob O’Boyle, John Shaw, Dick Pickrill, Steve Blasco, Russ Parrott, Angus Robertson, Kevin MacKillop, Walli Rainey, Kim Conway, Gordon Fader, Don Gordon, Brian Petrie, Victor Soukhovtsev, Igor Yashayayev, Colin Dickson, Bob Courtney, Gary Grant, Phil Oregan and many others.