![\"800px-Opalescent_inshore_squid\"](\"http:\/\/blogs.oregonstate.edu\/geo599spatialstatistics\/files\/2014\/04\/800px-Opalescent_inshore_squid-300x199.jpg\")
<\/p>\nFor the purposes of this class I am going to attempt to construct habitat suitability models characterizing the pelagic habitat of an invertebrate species, California market squid (Doryteuthis opalescens<\/i>) (Fig. 1 \u2013thanks wikipedia), an important prey species for multiple predatory fish (i.e. spiny dogfish sharks and seabirds) and is also commonly captured in the survey region in high abundances.<\/p>\n
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The dataset I am working with consists of pelagic fish and invertebrate abundance data that have been collected by NOAA over a 14 year-long (1998-2011) period in the Northern California Current off the Oregon and Washington coasts. Pelagic fish and invertebrates were collected along up to at ~50 stations along eight transect lines off the Washington and Oregon coast in both June and September of each year (Fig. 2). Species were collected using a 30 m (wide) x 20 m (high) x 100 m (long) Nordic 264 pelagic rope trawl (NET Systems Inc.) with a cod-end liner of 0.8 cm stretch mesh. For each sample, the trawl was towed over the upper 20 m of the water column at a speed of ~6 km h-1<\/sup> for 30 min (Brodeur, Barcel\u00f3 et al. In Press MEPS).<\/p>\n In addition to species abundance data, survey personnel also collect in situ environmental data at each fish sampling station during each survey, including; water column depth, salinity, temperature and chlorophyll a data, as well as oxygen and turbidity data when instruments were available.\u00a0 One of my goals for this class is to supplement this in-situ environmental dataset with remotely sensed temperature and primary productivity as well as turbidity data from the MODIS-Aqua and SeaWiFS platforms in order to obtain a broader environmental context.<\/p>\n For my habitat suitability modeling approach I will utilize R to conduct Generalized Additive Mixed Effects Models (GAMMs) correlating the environmental covariates to both presence\/absence data as well as abundance (catch per unit effort) data. Additionally, I will experiment with Maxent and other habitat suitability modeling techniques available to compare their output to my GAMM models.<\/p>\n Some of the spatial and temporal hurdles I face with this dataset include:<\/p>\n Unequal spacing between sampling locations: This may pose a challenge when attempting to spatially interpolate.<\/p>\n Scope of inference: The habitat modeling that I\u2019ll attempt for this species is likely applicable only in the Northern California current or in a slightly extended region.<\/p>\n Scale of environmental data: The fact that I will be using environmental data from two different sources (in situ data (point data \u2013 localized measurement) vs. remotely sensed data (raster satellite data \u2013 500m-1km grain)) will affect the resolution of my interpretations of habitat for this species.<\/p>\n Spatial autocorrelation among stations: Abundances and\/or presence\/absence of market squid may be spatially correlated among nearby stations due to autocorrelation in environmental covariates that define their habitat.<\/p>\n Temporal autocorrelation for each station: As the data I am using is a bi-annual survey, it is possible that the abundance and spatial structure of market squid within our sampling area is correlated between the two seasons of sampling. It is also possible that the temporal autocorrelation of an individual station with itself though time is not too big of a problem given the fluid medium in which sampling occurs and the highly variable inter-seasonal winds and currents in this region.<\/p>\n","protected":false},"excerpt":{"rendered":" For the purposes of this class I am going to attempt to construct habitat suitability models characterizing the pelagic habitat of an invertebrate species, California market squid (Doryteuthis opalescens) (Fig. 1 \u2013thanks wikipedia), an important prey species for multiple predatory fish (i.e. spiny dogfish sharks and seabirds) and is also commonly captured in the survey… Continue reading ![\"bpasampling\"](\"http:\/\/blogs.oregonstate.edu\/geo599spatialstatistics\/files\/2014\/04\/bpasampling.png\")
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