Author Archives: klajborw

Final Project Summary – Spatial Analyses of the Relationships Between Seascape Classes and Rockfish Distributions

Question Asked

The question that I sought to answer in this class was “How is the spatial pattern of forage fish assemblage in the California Current System related to the spatial pattern of seascapes based on the sea-surface conditions used to classify the seascapes?”  This is outlined in my first blog post, which can be found here. Additionally, I was seeking to characterize the spatial distribution and extent of seascape classes and forage fishes. Seascape classes are used as a way to simplify the myriad of physical, biological, and chemical processes that can affect organisms in the ocean. Eventually, the focus of the analyses shifted to young-of-the-year rockfish, as opposed to forage fish. All analyses mentioned in this blog post were conducted using the YOY-Rockfish data as opposed to the forage fish data.

Data

Midwater trawls have been conducted annually by the National Oceanic and Atmospheric Administration’s (NOAA) Southwest Fisheries Science Center (SWFSC) in an attempt to monitor the recruitment of pelagic rockfish (Sebastes spp.) and other epipelagic micronekton at SWFSC stations off California. The trawls have informed a dataset that represents overall abundance of all midwater pelagic species that commonly reside along the majority of the nearshore coast of California from 1998 to 2015. Each trawl collected information about both fish abundance, recorded in absolute abundance, and location data, recorded in the form of latitude and longitude. The dataset also includes a breakdown of species by taxa.

Seascapes have been classified using a combination of in-situ data (from the trawls) and remotely sensed data from NASA’s MODIS program. Seascapes were classified using the methods described in Kavanaugh et al., 2014 and represent the seascape class in the immediate area that each trawl occurred. Seascapes are classified at 1 km and 4 km spatial resolution and at 8-day and monthly temporal resolution. Each seascape has been assigned an ID number which is used to identify similar conditions throughout the dataset.

The current seascape classification methods are able to classify surface conditions into one of 33 distinct seascape classes. The variables used to classify the seascapes are listed below:

  • Sea-surface temperature (Celsius)
  • Sea-surface salinity (Practical salinity units)
  • Absolute Dynamic Topography (meters)
  • Ice cover (% of area)
  • Colored dissolved organic matter (m^-1)
  • Spatial averaged sea-surface chlorophyll a (mg m^-3)
  • Phytoplankton physiological state (NFLH) (W m^-2 um^-1 sr^-1)
  • NFLH:Chlorophyll ratio

It is inferred that certain, carefully-selected sea-surface conditions can imply favorable conditions to support healthy ocean ecosystems in the water column and near the ocean floor. The conditions listed above have been studied and selected for this reason. However, this may not be a perfect science – any attempt to delineate and simplify the multitude of factors and conditions that facilitate life in the ocean is likely to leave out important factors. This underscores the importance of research that tests these classification methods and measures their ability to replicate and predict the true natural environment. The analyses conducted for this class attempt to do that in the context of one functional group of fishes within one major ecosystem in the Pacific Ocean.

Hypotheses

Simply put, I hypothesized that any measurable spatial changes in the spatial extent of certain seascape classes will also be identifiable in the spatial variability of forage fish assemblage over time. The California Current ecosystem is meticulously studied and examined by a myriad of researchers from a number of different affiliate institutions. Studies reviewing the physical and biogeochemical conditions of the area indicate that from an environmental perspective, many areas off of the coast of California should support areas of high fish abundance.

Specifically, I was expecting areas of high forage fish and young of the year rockfish abundance to exist in seascape classes that represent nutrient-rich, upwelled water. These conditions have been shown to support thriving ecosystems throughout the water column due to an abundance of energy and food for fishes that live higher in the water column. Upwelling brings cold, nutrient-rich water to the surface and occurs seasonally in most places along the California Coast. Using the variables listed above, that would mean below average sea-surface temperature, high dissolved organic matter, and high chlorophyll a. Some classes that represent conditions similar to this are:

  • Temperate Transition (Class 7)
  • Tropical/Subtropical Upwelling (Class 11)
  • Subpolar (Class 12)
  • Temperate Blooms Upwelling (Class 14)
  • Warm, Blooms, High Nutrients (Class 21)

Preliminary multivariate community structure analysis has shown some statistically significant relationships between certain species and certain seascape classes using this data. If spatial patterns do exist, I expect there to be some relationship between the surface conditions and the fish found at depth of the midwater trawls.

Since my shift focused from forage fish to YOY rockfish, my hypothesis shifted from a prediction of correlation between certain seascape classes and forage fish abundance to relationships between rockfish and other seascape classes (as determined by the aforementioned multivariate analyses).

My hypothesis, taking into account all of the aforementioned considerations, can be restated as follows:

It is expected that the spatial pattern of areas of high young-of-the-year rockfish abundance will be related to the spatial patterns of seascape classes that represent areas of high nutrient availability and upwelled water along the California Coast.

Additionally, I expect even higher areas of abundance to occur in areas where two or more seascapes representing these favorable conditions converge or border one another. These border areas are likely to indicate much larger swaths of ocean that hold habitable conditions that are likely to be able to support entire communities rather than smaller numbers of fishes. While this hypothesis was not tested in this project, future work can be conducted using FRAGSTATS, patch analysis software, and other landscape ecology methods to seek an answer related to this prediction.

Approaches Used

Exercise 1: For the first exercise, different interpolation methods were used to explore their effects on the point trawls data. The interpolations (Kriging and IDW were tested) were used to model the abundance of rockfish in four selected years.

Exercise 2: Next, a neighborhood analysis was used to determine what the dominant seascapes were in the areas around trawls that produced high rockfish abundance (both according to the data and the interpolation) and low rockfish abundance. Buffers were set at 5, 10, and 25km distances around two trawls of each kind and seascape classes were measured as a function of % area of each buffer.

Exercise 3: Finally, a confusion matrix was calculated that measured the agreement between occurrence of the significant seascapes (as determined by exercise 2) and areas of high abundance (as determined by the interpolations in exercise 1).

Results

Exercise 1: The interpolations produced a number of maps of varying spatial extents and with varying resolution. Methods had to be refined multiple times to procure workable results, but maps interpolating rockfish abundance for 4 distinct years were eventually created and used.

Exercise 2: The neighborhood analysis produced statistics related to the percent of each buffer area occupied by each seascape class. This information was then used to create two plots – one for areas of high abundance and one for areas of low abundance. The most important result was an understanding of which seascape classes represented areas of high rockfish abundance. The dominant seascape classes in areas of high abundance turned out to match two of the seascape classes predicted in my hypothesis: Temperate Transition (Class 7) and Subpolar (Class 12).

Exercise 3: The final exercise produced a confusion matrix that measured true positives, true negatives, false positives, and false negatives as they related to the agreement between the seascape classes and the interpolated maps. The final matrix also produced an overall agreement score (Kappa).

Significance

The confusion matrix ended up being a bit of a failure, as both the seascape information and interpolated information had to be turned into presence-absence data for the matrix to be calculated. This eliminated some of the fine-tuned resolution associated with the interpolation and ultimately led to a Kappa score that suggested less agreement than would be expected by chance. While this specific result is not important to science or managers, these methods can be refined to improve the agreement between the two sources, ultimately providing a way to statistically link physical ocean conditions and rockfish abundances in California.

For these reasons, I consider this project to be an excellent pilot study for these data – one that introduces me to the types of analyses available to researchers who work with these types of data and their strengths and shortcomings.

Learning Outcomes

Software and Statistics

This class gave me an opportunity to brush up on my Arcmap and Modelbuilder skills, which I used to be very proficient in but had lost. I’ve also had the opportunity to explore some analyses through the lenses of R and Python, though I did not end up executing any parts of my project in these programs. The most important statistical learning outcomes for me had to do with interpolation and confusion matrices. The first exercise taught me all about the different ways point data can be interpolated and the consequences that that can have for results. I was familiar with the different types of hotspot analysis available to users in ArcGIS, but was not aware of their differences. The confusion matrix introduced me to an entirely new way to connect independent modeling methods. Methodologically, I learned about the importance of recording your methods as you execute processes and about the benefits of naming files sequentially. Had I not been organized, I would have had a hard time keeping track of the different processes executed and files used during each step.

Exercise 3: Confusion Matrix to Test Predictive Capacity of Seascapes

Question Asked

I’ve been using my time in this class to explore methods and analyses that could help me to better understand the spatial and temporal relationships between satellite-derived seascapes and fishes in the California Current Ecosystem. In Exercise 1, different types of interpolations were explored and applied to the trawl data to see how results changed. After refining methods related to the Kriging Interpolation, Exercise 2 was conducted, exploring he possible uses of neighborhood analysis in determining the prominent seascapes related to high rockfish abundance in the California Coast Ecosystem. Exercise 3 builds on the results of the previous two exercises: in order to explore the relationship between seascapes and rockfish abundance, a confusion matrix was calculated to measure the ability for certain seascapes to predict the occurance of rockfish hotspots. Simply put, the question being asked is: How were seascapes related to areas of high rockfish abundance in the California Coast Ecosystem in May of 2007?

Tool Used

In order to answer this question, I’m going to use a confusion matrix. A confusion matrix is a table that measures the agreement between two raster layers and provides an overall measure of a model’s predictive capacity. Additionally, these matrices can also measure a raster’s error (in terms of false-positives or false negatives). Each statistic calculated is useful, as each one clues the researcher into spatial or methodological processes that may account for certain types of error. The confusion matrix works by taking original data and creating accuracy assessment points, which are points assigned to random spatial points and given the value indicated by the raster value at that point. Those points are then matched up to points on the model raster and statistics are calculated measuring how much they agree.

Methods

The data used for this exercise are the following:

  • Significant seascape classes that indicate high rockfish abundance for May 2007, as determined by the neighborhood analysis in Exercise 2
  • Seascape raster data for the relevant month
  • Rockfish abundance raster (interpolated, from Exercise 1)

Confusion matrices can only be calculated when your ground-truthed and modeled layers are the same units. For this reason, my raster data had to be converted to presence absence data for both the seascape classes and the interpolated rockfish abundances. In order to do this, I used the Reclassify tool in ArcGIS to change “present” values to 1 and “absent” values to 0. For the interpolated abundance raster, any nonzero and nonegative values were changed to 1 to indicated modeled rockfish presence. The remaining cells were changed to 0 to indicate modeled rockfish absence. The result was the raster shown below:

Rockfish Presence-Absence Layer calculated using the Reclassify too on the interpolated trawl data.

Similar steps were used to reclassify the seascape data – seascape classes 7, 12, 14, 17, and 27, which were shown to be present within a 25km radius of the high abundance trawls in Exercise 2, were counted as present cells, while the other classes were counted as absent cells. The result was the raster shown below:

Rockfish Presence-Absence raster calculated using the results from exercise 2 to reclassify the seascape data

It was determined that the seascpae layer would be the base or “ground-truthed” layer and the interpolation layer would be the modeled layer, since the interpolated values are simply estimations. The next step was using the “Create Accuracy Assessment Points” Tool to create randomized points within the modeled layer. The result was the following shapefile:

Randomized accuracy assessment points created using the Create Accuracy Assessment Points tool

Once the points are created, they must be compared to the values in the modeled layer. The Update Accuracy Assessment Points tool does this automatically, and my points were matched up to corresponding values from the seascape layer. Finally, the final step was to run the resulting shapefile through the “Calculate Confusion Matrix” tool.

Results/Discussion

The resulting confusion matrix is displayed below:

ClassValue C_0 C_1 Total U_Accuracy Kappa
C_0 9 79 88 0.102272727  
C_1 89 276 365 0.756164384  
Total 98 355 453  
P_Accuracy 0.091836735 0.777464789 0.629139073  
Kappa         -0.135711

The two rasters measured in the matrix did not compare well at all, resulting in a negative Kappa value. Kappa values measure overall agreement between two measures, with 1 being perfect agreement. A negative score indicate lower agreement than would be found by chance. The interpolated model performed especially poorly measuring absent values, only agreeing with the seascape model on 9 pixels. While the two rasters agreed on a vast majority of the areas where rockfish may have been found, the irregularities on the absence areas drove the matrix to failure. The large number of both false positive and false negative readings shows that this was not a fundamental disagreement based on scale or resolution of the models – the two methods of measuring rockfish abundance fundamentally disagreed on where they should be found, to the point where they cannot be reconciled.

Critique of Method

Overall, I think a confusion matrix is a good way to compare modeled and true (or ground-truthed) measured information. However, in my case, both of my informational rasters are the results of modeled analyses, each with their own set of assumptions and flaws. The interpolation, for example, is at the mercy of the settings used when executing the Kriging: how many points shall be measured per pixel? Is it a fixed sampling method, or variable, spatially based? And the seascape raster is at the mercy of the neighborhood analysis, which was only conducted on 4, hand-picked points, which introduce all kinds of sampling and spatial biases. All of this, coupled with the fact that both rasters had to be distilled into presence-absence data, which eliminates much of the resolution that makes Kriging so great, results in a heavily flawed methodology that I do not believe truly represents the data. This framework which I’ve used, however, has the potential to be modified, standardized, and rerun in order to address many of these issues. But for now, I feel as though the confusion matrix is a helpful tool, just not for two sets of modeled data.

Exercise 2: Using Neighborhood Analysis to Identify Relationships Between Seascape Classes and Rockfish Abundance Hotspots

Background and Question Asked

In Exercise 1, different interpolation methods were used to create a heat map of rockfish abundance based off of a large collection of point data. That blog discussed some of the challenges that arose while attempting to use a time series of point data with many points in close proximity to one another (if not overlapping). The exploring was in many ways successful: it was discovered that the Kriging method provided a more robust representation of the data than Inverse Distance Weighting. However, in the time since that post was published, my interpolation methods have been refined:

  • Instead of using the entire time series as an input for the interpolation, four individual years were selected to represent the whole dataset (2003, 2007, 2011, 2015). Kriging was then used to create heat maps for each individual year.
  • Additionally, the union tool was used to remove the land boundaries from the environment so that the interpolation only affected parts of the ocean
  • The symbology of the abundance point data was synced across all four years being used in the analyses so that they could be easily compared to one another
  • The symbology of the interpolated heat maps was also modified to be consistent throughout the analyses

For this exercise, I plan to compare my new, interpolated data to an already existing set of data, effectively comparing my two variables. Specifically, I hope to answer the question “Is there a spatial relationship between areas of significantly high and low rockfish abundances and specific seascape classes?”

An example of the most recent Kriging output using the 2007 data.

Name of Tool or Approach Used

I will be using a neighborhood analysis to seek an answer to this question. The neighborhood analysis requires taking areas of interest and examining the environmental conditions around that area from the perspective of another variable. By varying the distance from your original point of interest, a researcher is able to infer about the spatial relationship between the two variables.

Methods

Data Used

  • “Points of Interest” chosen from plot below
  • Buffers created around points of interest at 5km, 10km, and 25km radii
  • YOYRock Kriging Abundance Interpolation for 2007
  • Seascape NetCDF Raster File for May 5th, 2007

Rockfish abundance plotted against water column depth for trawls from 2007.

The first thing that was needed to complete this analysis was points of interest. I chose to use four points form the year 2007, as the data from this year provided the largest spatial footprint of all of the years of interest. Two of the points represented trawls that found significantly high rockfish abundance, and the other two represent trawls which found no rockfish. All four points vary spatially and physically (latitude, longitude, water column depth, etc). All points were selected from interpolated areas with different modeled outputs. Next, circular buffers were created around each point of interest with 5km, 10km, and 25km radii.

Map showing Points of Interest with circular buffers overlaid on seascape NetCDF file.

In order to use the overlay tool in ArcGIS, two polygon features are needed. In order to convert my NetCDF Raster files into a polygon, I used the Raster to Polygon tool. Once the seascape classes were converted to polygons, the Intersect tool was used to measure the shape area of each seascape class within each buffer. Those statistics were then converted to .xlsx files and summarized in Excel.

Results and Discussion

Example of data after Raster to Polygon and Intersect tools used

The neighborhood analysis found evidence that specific seascape classes may have impacted young of the year rockfish abundances in the locations selected to be a part of this analysis.

The low-abundance trawls were dominated by three seascape classes: Class 14 (Temperate Upwelling Blooms), Class 19 (Subpolar Shelves), and Class 21 (Warm, Blooms, high Nutrients). While there were more classes represented overall by the high abundance trawls, those areas were mostly dominated by two seascape classes: Class 7 (Unnamed) and Class 12 (Subpolar Nutrient). Additionally, there was very little overlap between the two areas – the only seascape class that appeared in both the high abundance radii and the low abundance radii was Class 14. Further analyses would be needed to determine if these trends are representative to the entire region or year, but this neighborhood analysis provides results that give us a place to start. Overall, I found this analysis to be extremely useful despite the number of steps needed to make it work. In addition to working in GIS normally, the data type of my seascapes had to be changed and much of my analysis had to be done in Excel, as ArcGIS cannot summarize key statistics. However, I feel as though streamlining this method could be done now that I am familiar with it.

Exercise 1: Comparison of Interpolation Methods Using Fisheries Abundance Data

Question Asked

I would like to understand how the spatial variability of forage fish in the California Current Ecosystem is related to the spatial pattern of seascape classes (based on remotely sensed sea-surface variables)? In exercise 1, I will asking “what is the spatial pattern of forage fish in the California Current ecosystem?” In order to address this question, I will be testing the use of different types of interpolation on my point-data.

Approaches Used and Methods

To address these questions, the Kriging Interpolation and Inverse Distance Weighting Interpolation tools were employed in ArcMap. All processes were completed in ESRI ArcMap 10.6.1. Interpolation is described as a method of constructing new data using existing data as references. In spatial and temporal analyses, there are a range of different types of interpolation that can be used.

The original data, which includes a series of about 1300 trawls, the catch per unit effort (CPUE) per species per trawl, the latitude, longitude, and water column depth of each trawl, and the functional group of each species caught, were loaded into ArcMap using the “Display X,Y Point Data” function. The four functional groups used in this analysis are Forage, Young-of-the-Year Rockfish (YOYRock), YOYGround, and crustaceans. In the case of this analysis, all fishes that are part of the YOYRock functional group were included.

Representation of the raw YOYRock Trawl data in ArcMap

After the data were uploaded, they had to be converted to feature classes. For the purposes of this exercise, all trawl data from 2002 to 2015 was included as one feature class, though the way in which the data are organized make it easy to break the trawls down at a finer temporal resolution. The result was a Point Shapefile that was then binned into four abundance groups to make interpretation easier. The IDW and Kriging tools (from the “Spatial Analyst” Toolbox) were then employed. The base equation for both interpolations is virtually the same, and both are commonly used methods for continuous data in point form, but there are some major differences in the calculation of some of the stated variables:

Representation of the equation used to assign values to missing cells using the IDW and Kriging methods. Z(si) = the measured value at the ith location, λi = an unknown weight for the measured value at the ith location, s0 = the prediction location, and N = the number of measured values

Z(si) = the measured value at the ith location, λi = an unknown weight for the measured value at the ith location, s0 = the prediction location, and N = the number of measured values

1) IDW: IDW, or Inverse-distance weighting, is what’s known as a deterministic interpolation method, as it relies on surrounding values to populate the resulting surface. One of the major assumptions with using IDW is that it assumes that the variables being mapped decrease in influence linearly as you move away from a given value. In IDW, the weight given to the “measured value at the ith location” is solely calculated linearly, decreasing as you move farther away from a given value. In this case, the “power” value, which corresponds to the weight, was the default 2.

2) Kriging: Kriging is an interpolation method based on geostatistics including autocorrelation. The equation used to calculate the missing values is the same as for IDW, except that the weight variable is calculated using a mathematical function within a certain specified radius of the missing value. For this reason, one of the main assumptions when using Kriging is that the “distance or direction between sample points reflects a spatial correlation that can be used to explain variation in the surface.” (ESRI, ND).

Results

The resulting interpolations can be found below. I’ve included output that focuses on the significant region of Monterey Bay to provide context regarding the proximity of the trawls to one another and to show detail on the boundaries of the interpolations.

Full Interpolation using IDW

Full Interpolation using Kriging

Detail of IDW in Monterey Bay, CA

Detail of Kriging in Monterey Bay, CA

Critiques of Methods

Any analysis of the results will conclude that the Kriging Tool provided a much more robust interpretation of the same patterns that can be observed in the original data and in the IDW interpolation. Both interpolations displayed significant patterns near Monterey Bay, and the Kriging Interpolation also represented additional areas of higher abundance north of San Francisco. While I do believe that both interpolation methods provide a good place to begin analysis, I believe that several adjustments will have to be made in order to create a useable result.

My first mistake was using the entire time series – since the interpolations are distance-based and extremely sensitive to points within close proximity to one another, the clear clusters of point most likely influenced the interpolations. A next step for me will be breaking the data down to an annual resolution, as I feel that shapefiles with one point at each trawl location will provide better data for interpolation. Additionally, this will provide multiple maps, which will allow for a chance to observe how the modeled patterns of YOYRock abundance have changed over time.

Another next step will be exploring the fine adjustments available within each interpolation method. I now have a greater understanding of the mechanics which drive IDW, so I’m eager o rerun the analyses at different powers to see how each impacts the interpolation. Similarly, the radius used to decide which points are considered while calculating a Kriging Interpolation can be adjusted, so that will be done in the future as an experiment.

Finally, I ran out of time to explore the symbology of the results – I hypothesize that classification by a smaller number of classes would result in more robust interpolation maps, as the visualizations now show a vast majority of the space to fall into the lowest class. The interpolation data is relatively bimodal in nature, so an adjustment in the symbology tab would likely result in a a more accurate and precise representation of abundance.

Overall, I see interpolation as a valuable way to identify spatial patterns from point data. In the case of species abundance data, I believe that Kriging is the superior method, as it does not have the linear influence assumption that’s baked into IDW. Additionally, the geostatical methods used in Kriging generally allow for a more robust and precise interpolation regardless of the type of continuous data being used.

The next steps mentioned above will be taken before the presentation of Tutorial 1.

References

http://desktop.arcgis.com/en/arcmap/10.3/tools/3d-analyst-toolbox/how-idw-works.htm

http://desktop.arcgis.com/en/arcmap/10.3/tools/3d-analyst-toolbox/how-kriging-works.htm

Santora, Jarrod & Hazen, Elliott & Schroeder, Isaac & Bograd, Steven & Sakuma, KM & Field, JC. (2017). Impacts of ocean-climate variability on biodiversity of pelagic forage species in an upwelling ecosystem. Marine Ecology Progress Series. 580. 10.3354/meps12278.

 

Examining the Spatial Relationships between Seascapes and Forage Fishes

Description of Research Question

My objective is to study the spatial relationships between sea-surface conditions and assemblages of forage fish in the California Current System from 1998 to 2015. Forage fish are a class of fishes that are of importance to humans and resource managers, as they serve as the main diet for economically and recreationally valuable large-game fishes. Using a combination of remotely sensed and in-situ data, sea-surface conditions can be classified into distinct classes, known as “seascapes,” that change gradually over time. These seascapes, which are based on a conglomeration of measurable oceanographic conditions, can be used to infer conditions within the water column. My goal is to determine if any relationship exists between forage fish assemblages and certain seascape classes by examining the changes in the spatial patterns related to each over time. Forage fish assemblage may be related to seascapes as certain seascape classes may correspond to physical (temperature) or biological (chlorophyll concentration) conditions, either on the surface or in the water column, which happen to be favorable for a specific species or group of species.

My question can be formatted as: “How is the spatial pattern of forage fish assemblage in the California Current System related to the spatial pattern of seascapes based on the sea-surface conditions used to classify the seascapes (temperature, salinity, and chlorophyll)?

Description of Data

Midwater trawls have been conducted annually by the National Oceanic and Atmospheric Administration’s (NOAA) Southwest Fisheries Science Center (SWFSC) in an attempt to monitor the recruitment of pelagic rockfish (Sebastes spp.) and other epipelagic micronekton at SWFSC stations off California. The trawls have informed a dataset that represents overall abundance of all midwater pelagic species that commonly reside along the majority of the nearshore coast of California from 1998 to 2015. Each trawl contains both fish abundance, recorded in absolute abundance, and location data, recorded in the form of latitude and longitude. The dataset also includes a breakdown of species by taxa, which will be used to determine if a fish is a “forage fish.”

Seascapes have been classified using a combination of in-situ data (from the trawls) and remotely sensed data from NASA’s MODIS program. Seascapes were classified using the methods described in Kavanaugh et al., 2014 and represent the seascape class in the immediate area that each trawl occurred. Seascapes are classified at 1 km and 4 km spatial resolution and at 8-day and monthly temporal resolution. Each seascape has been assigned an ID number which is used to identify similar conditions throughout the dataset.

The map below shows the locations of every trawl over the course of the study.

Figure 1: Map showing all trawl sites contained in the dataset. Trawls occurred at a consistent depth using consistent methods between and including the years of 1998 and 2015

Hypotheses

I hypothesize that any measurable spatial changes in the spatial extend of certain seascape classes will also be identifiable in the spatial variability of forage fish assemblage over time. Preliminary multivariate community structure analysis has shown some statistically significant relationships between certain species and certain seascape classes using this data. If spatial patterns do exist, I expect there to be some relationship between the surface conditions and the fish found at depth of the midwater trawls.

Hypothesis: I expect the spatial distribution of forage fish species to be related to spatial distribution of seascape conditions based on the variables used to classify the seascapes (temperature, salinity, chlorophyll).

Potential Approaches

I hope to utilize the tools within both R and the ArcGIS Suite of products to identify and measure spatial patterns in both seascape classes and forage fish assemblages over the designated time period. I also aim to run analyses to determine if any relationship exists between the variability in spatial extent of each variable. These analyses will be used to supplement the previously completed multivariate community structure analyses done on these data.

For Exercise 1, I will identify and test for the spatial patterns of the forage fish family Gobiidae (Goby) and Seascape Class 10, as initial indicator species analyses indicated that there may be a relationship between the two. In Ex. 2, cross-correlation and/or GWR will examine relationships between these patterns.

Expected Outcome/Ideal Outcome

Ideally, I would like to determine and define the relationship between seascape classes and forage fishes in the California Current System over the designated period of time. Any sort of definitive answer, positive, negative, or none, provides valuable insight into the relationships between this remotely sensed data and these fishes. If that claim could be bolstered by a visual which outlines the relationship between my variables (or lack thereof), that would be icing on the theoretical cake.

Significance of Research

Measuring the predictability of forage fish assemblage has wide-ranging impacts and could be found useful by policymakers, fishermen, conservationists, and even members of the general public. Additionally, this research can be used to underscore the importance of seascape-based management or seascape approaches to ecology or management. This research could also be used as inspiration for future studies about different species, taxa, or geographic locations.

Level of Preparation

I completed a minor in GIS during my undergraduate studies, but have not had to utilize those skills for about 15 months. After some time, I believe that I will be extremely comfortable using the software. I have basic exposure to R software (mostly in the context of statistical analysis) and have used CodeAcademy to further my understanding of Python. I did some image processing during my undergraduate studies as well, but am not particularly comfortable with that set of skills. I have used leaflet to embed my maps and create time series before, so that could be an option for this work.

WORKS CITED

Kavanaugh M. T., Hales B., Saraceno M., Spitz Y.H., White A. E., Letelier R. M. 2014. Hierarchical and dynamic seascapes: A quantitative framework for scaling pelagic biogeochemistry and ecology, Progress in Oceanography, Volume 120, Pages 291-304, ISSN 0079-6611, https://doi.org/10.1016/j.pocean.2013.10.013.

Sakuma, K., Lindley, S. 2017. Rockfish Recruitment and Ecosystem Assessment Cruise Report.  United States Department of Commerce: National Oceanic and Atmospheric Administration, National Marine Fisheries Service.

-Willem Klajbor, 2019