I really should have titled my project the “Multi-scale exploration of California Market Squid” to begin with, but c’est la vie! Throughout the quarter I’ve worked mostly in R, both to make interpolated spatial maps of my data as well as to run spatial analyses. Specifically, I’ve been interested in determining which oceanographic covariates are correlated with the abundances of squid at the fine scale as well as at a broader regional scale.

Fine scale exporation:

In order to explore this data, I started this quarter by plotting the distributions of market squid based on the yearly sampled (in June) fine scale spatial abundance data (see yearly plots below). For this class I chose to work with only positive abundance data (as absences may be pseudoabsences due to catchability issues and therefore potentially not good indications of poor quality habitat for the species), however I may re-introduce the 0 catch data in future analyses (with this caveat in mind).

In  the interpolated surface maps, warmer colors indicate regions of higher squid abundance and colder colors indicate regions of lower squid abundance on a yearly basis. To create thises maps, wrote a script in R to create an IDW interpolation surface of squid abundances per year with overlayed contours using R’s gstat and maptools libraries.

California market squid (catch per unit effort: #/km2 towed):

cms_JUNE

Additionally, I interpolated and contoured both the temperature and salinity data (in situ data) collected at each station in each year so as to have a better idea of the environmental context that this species was experiencing in each year.

Temperature @ 3m depth:

CTD_temp_3m_JUNE

Salinity @ 3m depth:

CTD_sal_3m_JUNE

While my initial and future plan is to look at the satellite data corresponding to each cruise (June of each year), I realized I was biting off more than I could chew this quarter by having this as a goal, as working with remotely sensed oceanographic data files (daily images for 14 years!) is remarkably difficult to access and process due to the size of the individual files as well as the multiple frequencies (wavelengths) that correspond to distinct oceanographic processes (SST, Chl, turbidity, etc). Before tackling the satellite data from a brute force kitchen sink fashion, I instead was eager to identify which in-situ environmental variable with a remotely sensed analog dataset (SST from MODIS-Aqua would be an analog to surface temperature measured with a CTD from the ship), showed strong relationships with abundance.

Below is are simple scatterplots of  the log abundances of california market squid with the points colored by the years in which they were sampled. The top row has market squid abundance in relationship to temperature, salinity and chlorophyll (all variables with a remotely sensed equivalent, or nearly), the bottom row has market squid abundance in relationship to density (function of temperature and salinity), oxygen and water column depth (all variables with no remotely sensed analog).

cms_enviros

Unfortunately, there were no strong linear or non-linear correlations of abundance with any of the in-situ covariates (R2<0.01).

Broad scale exploration:

Next, I approached this dataset from broader perspective (zooming out if you will). I was interested in tracking the centers of gravity and isotropy (a measure of the non-uniformity of a spatial dataset along multiple axes) of the sampled population through time. The center of gravity statistic is estimated from the data through discrete summations over sample locations. Practically, from sample values zi at locations xi, with areas of influence si, we have: Screen Shot 2014-06-04 at 10.48.52 AM

The inertia indicates how dispersed the population is around its center of gravity, and is given as follows:

Screen Shot 2014-06-04 at 10.49.00 AM

A more in depth discussion of this and other spatial indicators can be found in Wolliez et al. (2009). Notes on survey-based spatial indicators for monitoring fish populations. Aquat. Living Resour. 22, 155-164.

I used both of these calculate to determine the center of gravity and isotropy for market squid captured in June (and Sept, not shown on map) of each year. I then created a map with each year’s center of gravity of the market squid population in the sampled region (caveat: centers of gravity and isotropy are not for entire squid population but specifically for the data sampled off the coasts of OR and WA). There is a remarkably strong northwards shift in the centers of gravity over time for both the June data as well as the Sept. data. I haven’t gotten a chance to correlate this northwards shift in the centers of gravity with summarized environmental variables yet, but this is the plan.

cg_iso_cms

 

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 –thanks 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.

800px-Opalescent_inshore_squid

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 for 30 min (Brodeur, Barceló et al. In Press MEPS).

bpasampling

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.  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.

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.

Some of the spatial and temporal hurdles I face with this dataset include:

Unequal spacing between sampling locations: This may pose a challenge when attempting to spatially interpolate.

Scope of inference: The habitat modeling that I’ll attempt for this species is likely applicable only in the Northern California current or in a slightly extended region.

Scale of environmental data: The fact that I will be using environmental data from two different sources (in situ data (point data – localized measurement) vs. remotely sensed data (raster satellite data – 500m-1km grain)) will affect the resolution of my interpretations of habitat for this species.

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.

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.