GEOG 566

         Advanced spatial statistics and GIScience

Archive for My Spatial Problem

April 9, 2018

Predicting Produce Safety Rule compliance through spatiotemporal analysis of publicly-available water quality data

Filed under: 2018,My Spatial Problem @ 11:05 am
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Research Question

Because of the numerous foodborne illness outbreaks associated with fresh produce, the Food and Drug Administration finalized the Produce Safety Rule in November 2015. This rule implements a variety of new food safety practices on the farm to prevent foodborne pathogens from reaching the consumer. As part of this new rule, growers of fresh produce are required to meet water testing requirements for all water used in the growing, handling, and harvesting of produce. Growers are expected to test their surface water source a minimum of 20 times to establish a baseline Water Quality Profile (WQP). The WQP is then to be updated annually with 5 additional samples. The WQP consists of the geometric mean and statistical threshold value of generic E. coli in the water.

My objectives with this dataset are twofold:

  1. Determine whether Oregon produce growers will face difficulty in meeting the water quality requirements based on historical trends
  2. Explore whether produce growers who share a common surface water source can pool their data to collectively establish a WQP to meet the requirements


Oregon Department of Environmental Quality maintains a public database (Ambient Water Quality Monitoring System) of statewide surface water testing for a variety of contaminants. I will analyze the dataset for generic E. coli. I have also acquired data for pH and temperature as potential explanatory variables for the data set. These data exist as point data at DEQ monitoring stations that are adjacent to a water source (river/stream), with the data spanning from January 1, 2013 through December 21, 2016. Each monitoring station has different temporal spans (for example: one monitoring station only contains data for 2015, while another covers the entire three-year span).


 I hypothesize that generic E. coli concentrations will correlate most strongly to time of year for sampling. I predict that pH and temperature variations will contribute insignificantly to the fluctuations of generic E. coli. Additionally, I predict that trends will be consistent within each watershed, but vary greatly between.


 I will test the dataset within ArcGIS and R to determine statistically significant factors in generic E. coli concentrations within watersheds in the state of Oregon.

Expected Outcome

The outcome of this research will help inform food safety extension work. Additionally, this data may help growers alleviate the water-testing burden if we identify that current testing regimes by government agencies is sufficient to meet the requirements of the rule, or if the data within a watershed can be collectively shared to meet compliance standards.


This study will help guide the direction of future food safety extension work related to the Produce Safety Rule to prevent foodborne illness outbreaks associated with fresh produce.


I am very comfortable working with the suite of ArcInfo software. I have limited beginner level experience with R, Python, Modelbuilder, and image-processing software (ENVI Classic).

April 8, 2018

Spatial and temporal variation in the historical fire regime of the Oregon pumice ecoregion

A description of the research question that you are exploring.

I’m researching historical fire regimes in ponderosa pine, lodgepole pine, and mixed-conifer forests in south central Oregon. Previous fire history reconstructions demonstrate fires were historically frequent occurring every 10-20 years, and were predominantly low severity.  However, the size and sampling design of earlier reconstructions does not describe historical fire sizes and their spatial patterns across landscapes. We mapped historical fire perimeters to answer the following research questions;

1) How did topography, vegetation (fuels), climate, and previous fires constrain fire spread and fire perimeters?

2) Do these constraints create landscape regions (firesheds) with a distinct fire regime?

3) What defines a fireshed?  Do landforms or distinct vegetation types envelop them? In other words, are there significant spatial relationships between firesheds and a combination of topography and vegetation?

3) Are constraints on fire spread and perimeters stable over time or do they vary temporally with climate or land use?


A description of the dataset you will be analyzing, including the spatial and temporal resolution and extent

I systematically reconstructed fire history at 52 plots by removing partial cross sections from 3-10 fire scarred trees within 250 m (20 ha) of plot center. Fire scars were dated to their exact year of formation to build a record of fire events during ~1670-1919. The fire record for unsampled areas is represented by the nearest known fire record from a sampling point using Thiessen polygons (Figure 1 ). This method of interpolation of fire history to unsampled areas assumes that the best predictor of fire history in an unsampled area is the nearest sampled area (Farris 2010). The fire reconstruction area is 85,000 ha and includes a mosaic of landforms, soil types, and forest types.

Fire_maps -Fig. 1


Hypotheses: predict the kinds of patterns you expect to see in your data, and the processes that produce or respond to these patterns.


Fire regimes vary with and are constrained by broad scale top-down drivers primarily climate and local bottom-up drivers including topography, (slope, aspect, landforms, soil) vegetation, and ignitions.

Top Down controls on fire

In central Oregon, climate and ignitions are generally not limiting to fire. Summers are hot and dry and lightning ignitions are abundant (Morris 1934). Previous dendrochronological reconstructions of historical fires demonstrate most fires burned in years with below average spring precipitation (Johnston 2016). However, fire size has not yet been related to climate and we do not know if extensive fires (10,000-80,000 ha) have a different relationship with annual and previous year climate than small fires.

Hypothesis1 – Large fire events depend on continuous fuels in a hot dry year. A series of wetter than average years followed by a drought year provides abundant fuel with low moisture allowing the spread of extensive fires.

If this hypothesis is true, I expect extensive fires to be negatively related to drought in previous years and positively related to drought in the year of the fire event. Small fires should have poorer relationships with climate or may be more common during cool wet years.

Hypothesis 2 – Large and small fire events are limited by fuel not climate.

If this hypothesis is true, I expect no there is no relationship between previous year climate and fire size, and that both extensive and small fires are positively correlated with drought in the year of the fire event. Fire maps would also show that fires did not burn areas that had recently burned.


Bottom-up controls on fire

I expect that bottom-up drivers of topography and fuels are stronger constraints on fire spread than climate, and will explain more of the spatial variation in fire history. Merschel et al. (2018) demonstrated that pumice basins characterized by coarse soils, low productivity, and low fuel abundance constrained spread of some extensive fires and drove spatial variation in fire history on the eastern slope of the central Oregon Cascades. Similar, but more extensive pumice basins occur in this study area. In addition, there are large topographic features including long steep ridges, large volcanic buttes, and gentle rolling topography intermixed across the study region.

Hypothesis 3 – Lodgepole pumice basins limited fire spread because of slow fuel recovery and formed firesheds.

If this hypothesis is true, I expect fire frequency to decrease with increasing abundance of lodgepole pumice basin within an analysis polygon. I also expect variability in fire frequency and the amount of small fires to increase with the increasing abundance of lodgepole pumice basin.

Hypothesis 4 – Fuel recovery controls fire spread throughout the area.

If this hypothesis is true, I expect fire frequency to vary little across the reconstruction area, and that it would not vary significantly among vegetation (forest) types. Time since fire would be the best predictor of whether an area burned in a fire event, and firesheds would not be apparent across the study area.


Approaches: describe the kinds of analyses you ideally would like to undertake and learn about this term, using your data.

My first priority is to develop maps of fire events and the metrics that describe fire history.  For example, I would want to map how frequency, variation in frequency, minimum and maximum fire interval, and fire size vary spatially across the study area. It would be very helpful to develop fire maps that also show how time since last fire spatially varied across the study area.  This would help us understand whether fuel recovery limits or constrains fire in the region. I’ve made an animation of fire maps through time before, but it was extremely glitchy and limited in ArcMap. I would like to learn a better animation process because videos of fire maps are effective in presentations. Ideally unburned reconstruction polygons would be shaded by time since fire, current year fires would be depicted in red, and the maps would include a sidebar that summarized drought conditions in the current year. The viewer would simultaneously learn how fires were related to topography, vegetation type, fuel recovery, and climate.

Potential analyses (I’ve got a lot more to learn about)

Cluster analysis to identify fire regime types based on fire regime metrics

Hot Spot analysis to identify how fire history or fire regime metrics vary spatially

Superposed ephoch analysis to identify relationships between climate and fire occurrence and climate and fire size.

Generalized linear mixed modeling to check for spatial autocorrelation in fire regime metrics and to understand relationships between fire regime metrics and topography, vegetation, and landscape structure


Expected outcome: what do you want to produce — maps? statistical relationships? other?

I want to produce maps of fire events and fire regime metrics.

I would like to know how fire size and occurrence was statistically related to climate

I want to produce statistical models that describe how fire regimes vary spatially with topography, vegetation type, and fuel recovery. I suspect that relationships may vary with climate and that interactions between variables may be important (e.g. vegetation type may only be important on steep volcanic buttes)


Significance. How is your spatial problem important to science? to resource managers?

Currently there is much interest in restoring fire in forested landscapes with a history of fire exclusion and vigorous debate on the historical role of fire in different forest types and environmental settings. The science of fire ecology provides us with a good theoretical understanding of what drives variation in fire regimes. However, few datasets available allow us to quantify what drivers of variation have the most influence, and how they interact. By identifying what drives variation in fire regimes we can better plan for, manage, and reintroduce fire into forests with a history of fire suppression.


  1. I’m comfortable working with Arc-Info to make maps, but would like more experience with spatial statistics.
  2. I’m a Python beginner
  3. I’m comfortable working through problems in R, but I often have sloppy code. I could take a big step forward by learning how to produce maps in R and perform multivariate stats in R.




April 6, 2018

It’s all in the timing: Assessing risk of an introduced insect on a native plant through investigations of phenological synchrony

Filed under: 2018,My Spatial Problem @ 10:39 pm

1. Research Question / Background:

Biological control of weeds involves introducing or augmenting natural enemies, such as insect herbivores, for population control of a target weed. Choosing insect herbivores that are highly specific to their host plants gives managers some confidence that these species will be safe & effective as biocontrol agents. However, our ability to predict outcomes of introductions is imperfect, and resulting risks to non-target native plants must be weighed in evaluating success & safety of biological control programs.

The cinnabar moth was released in Western Oregon beginning in the 1970s to 1990s to control a European weed, tansy ragwort. However, redistribution of the cinnabar moth was halted after it established on Senecio triangularis, a non-target native wildflower (Diehl and McEvoy, 1988). The moth has established and maintained populations on S. triangularis even in absence of the ancestral host. Cinnabar herbivory of foliage has not been found to cause long term decreases to plant fitness or reproduction (Rodman, 2017). Herbivory of flowers is less common given a mismatch between the flowering time of S. triangularis and peak feeding stages for the moth, but when it does occur, floral/seed herbivory may have direct impacts on population dynamics. Previous work has shown that that larvae experiencing phenological synchrony with S. triangularis flowers decreased seed set by 95% (Rodman, 2017); and that S. triangularis is seed-limited, so that reduction of seed set decreases seedling recruitment to the next generation (Lunde, unpublished data).

Because this plant is a novel host-plant, and because insects and plants can respond to disparate environmental cues to determine the timing of their life cycles (phenology), we expect to see phenological synchrony varying depending on environmental factors. Knowing which populations of S. triangularis would be most likely to experience seed herbivory by cinnabar larvae could help managers track and respond to cinnabar moth presence and the risk to S. triangularis on a site-by-site basis.

This project seeks to explore how variation in phenological synchrony is related to a set of environmental variables for a set of known populations of cinnabar moth on Senecio triangularis. Using a linear mixed model, and environmental variables measured directly or derived from zonal statistics of spatial data, we will use variable selection processes to determine which candidate factors best explain variation in phenological synchrony seen on a single date (July 22) in 2017. This relationship will then be used to predict phenological synchrony, and subsequent risk of seed herbivory, for a larger set of 26 sites with known S. triangularis populations extending across the Oregon Cascade & Coast Ranges.

2. Data:

This project uses a dataset of phenology scores (for cinnabar larvae and Senecio triangularis) and environmental variables taken for five populations in the Willamette National Forest near Oakridge, OR. The project also estimates environmental variables from assorted spatial datasets. Though some data were taken at the level of individual plants, all variables will be considered at the site level. Some variables (such as soil type, snowmelt date) cannot be measured and deemed meaningful at the level of individual plants. Data sources/details as follows:

Population locations: two polygon layers — one includes 5 sites surveyed over the 2017 season; one includes 26 additional sites previously surveyed for cinnabar presence and host-plant damage.

Phenological synchrony: tabular, count data for number of vulnerable and invulnerable flower heads (capitula), and counts of cinnabar larvae in peak feeding stages (4th & 5th instars), collected from a random subset of tagged plants on July 22, 2017. These count data will be used to derive a measure of phenological synchrony for each site. While data were collected at 10-day intervals, in order to capture variation in synchrony, this analysis will focus on data from July 22. This date is halfway between the date at which all capitula were vulnerable and the date at which all capitula were invulnerable in 2017.

The following environmental variables are included in the phenology dataset: ambient temperature at 6 inches and 36 from the ground; soil temperature and moisture at 6 inch depth; and approximate sun exposure measured by a Solar Pathfinder. These data are all constrained to the 5 surveyed sites.

Snow disappearance date will be approximated from a model developed by Ann Nolin and the Mountain Hydroclimatology Group. This model uses MODIS data to determine date of snow disappearance at a resolution of approximately 500m. Accumulated degree days will be approximated for July 22, 2017 from a model developed by Len Coop of the OSU Integrated Plant Protection Center (IPPC), based on a variety of climate data: AGRIMET, HYDROMET, ASOS/METAR and COOP networks, RAWS network, SNOTEL network, and others.

Soil type for each site will be determined from SSURGO soil layers, which provide polygon boundaries of component soil types as determined by the National Cooperative Soil Survey. Elevation and aspect (site average) will be determined from elevation and aspect surface previously developed from a Digital Elevation Model of Oregon at a 30m resolution. Each layer will be obtained for the extent of Oregon, and considered at the scale of 31 identified sites using zonal statistics.

3. Hypotheses:

The phenology of insects is usually predicted on degree days, because insects are ectotherms whose development is largely constrained by external heat gain (Johnson et al., 2007); whereas alpine and subalpine species of perennial plants have been shown to vary widely in terms of which environmental factors drive flowering phenology (Dunne et al., 2003). We expect to see differences in phenological synchrony of host-plant and larvae to the extent that environmental drivers underlying each species vary independently.

My hypothesis is that phenological synchrony varies between sites on July 22, 2017, and that a significant amount of the variation in phenological synchrony can be explained by a combination of candidate environmental factors (listed above). Further, I hypothesize that this model can be used to predict risk of cinnabar seed herbivory to S. triangularis based on values of environmental factors.

It is possible that the five sites represented in the available data do not represent a wide enough range in explanatory variables to adequately test this hypothesis. In this case, I would seek to approximate what site conditions need to be represented in the data in order to answer the research question.

4. Approaches:

Initial analysis will use zonal statistics and statistical analysis of tabular data to determine the variation in phenological synchrony and candidate explanatory variables across 5 sites surveyed in 2017. This will be used to develop a linear mixed model using appropriate variable selection methods to determine which environmental factors best explain variation in phenological synchrony for July 22.

Then, using a broader set of 26 sites with known S. triangularis populations and the same set of environmental data, I will use the linear mixed model to predict phenological synchrony for theoretical cinnabar moth populations at these sites. Results of this analysis would be used to display the estimated phenological synchrony, and risk of seed herbivory, for all 31 sites.

5. Expected outcome:

Outcomes for this project will be a linear mixed model that can predict mid-season phenological synchrony of cinnabar larvae and S. triangularis flowers from a set of environmental explanatory variables derived from spatial datasets and 2017 phenology survey data.

An additional product will be a map showing phenological synchrony for 26 additional sites as predicted from environmental factors deemed significant in this first part. Not all of these populations have cinnabar moth populations, but this map would allow us to identify sites at which S. triangularis would be at high or low risk of seed herbivory if populations of cinnabar moth were to establish.

6. Significance:

Estimations and predictions of phenological synchrony determined in this study will be significant in answering how often and under what conditions cinnabar larvae have the potential to decrease seed set for Senecio triangularis through floral herbivory. Experimental data could be used to estimate decreases in annual seedling recruitment based on seed reduction scenarios. Meanwhile, a map showing relative risk of seed herbivory due to phenological synchrony will allow managers to identify high-risk populations of S. triangularis in order to focus monitoring efforts at these sites and possibly intervene by reducing or moving cinnabar moth populations.

7. Level of preparation:

ArcINFO: 3 terms of coursework (GIS I, II, & III) and independent work; relatively confident.

Modelbuilder and/or GIS programming in Python: one term of coursework (GIS III); somewhat confident.

R: three terms coursework (Stats 511, 512 & FES 524); limited proficiency, no experience with spatial data


Diehl, J.W., and McEvoy, P.B. (1988). Impact of the Cinnabar Moth (Tyria jacobaeae) on Senecio triangularis, a Non-target Native Plant in Oregon. In Proceeding VII International Symposium on Biological Control of Weeds, (Rome, Italy), p. 119-126.

Dunne, J.A., Harte, J., and Taylor, K.J. (2003). Subalpine Meadow Flowering Phenology Responses to Climate Change: Integrating Experimental and Gradient Methods. Ecol. Monogr. 73, 69–86.

Johnson, D., Bessin, R., and Townsend, L. (2007). Cooperative Extension Service, University of Kentucky. Resource 474, 7727.

McEvoy, P.B., Higgs, K.M., Coombs, E.M., Karaçetin, E., and Ann Starcevich, L. (2012). Evolving while invading: rapid adaptive evolution in juvenile development time for a biological control organism colonizing a high-elevation environment. Evol. Appl. 5, 524–536.

Rodman, M. (2017). Non-target Effects of Biological Control: Ecological Risk of Tyria jacobaeae to Senecio triangularis in Western Oregon. Oregon State University.

Associations between physical substrate complexity and spatiotemporal kelp forest dynamics

Filed under: 2018,My Spatial Problem @ 10:08 pm


Nearshore temperate kelp forests are structured by an interaction of physical forces and biological processes that produce patchy species assemblages across space and rapid shifts in community structure through time. At short temporal scales seasonal storm surge removes adult giant kelp (Macrocystis pyrifera) and can instigate community shifts, while longer-term periodicity (e.g., El Niño Southern Oscillation events, Pacific Decadal Oscillation) modulates the oceanographic conditions influencing macroalgal growth. Disturbance and herbivore driven community shifts are often transient; however, deforested regions do not always immediately recover, and urchin dominated communities may persist for a decade or more before reverting back to a macroalgal state. While the processes involved in both directions of the switch between urchin and macroalgal states have been repeatably observed and qualitatively described, it is less clear how biological and physical context at local scales may modify positive and negative feedback mechanisms to either perpetuate or dampen these shifts. To (1) investigate how variation in physical substrate complexity is associated with varying temporal kelp forest dynamics, and (2) map a future survey, I will incorporate a spatially explicit 38-year time series of community dynamics with 2m side scan sonar bathymetry around San Nicolas Island, CA, in the Channel Islands.


(1) Are spatial associations between physical habitat complexity (i.e., relief) and community structure consistent through time? Are varying levels of relief associated with (a) cyclical periodicity through time or (b) edges in community structure? Does increasing the spatial scale of bathymetry incorporated around each site influence the previous results? That is, does the composition or heterogeneity of an expanding spatial window of substrate provide insight into the nature or persistence of species-habitat associations?

(2) Create a pool of possible survey sites for fieldwork currently slated for summer 2019. Based on previous surveys, our inference into community structure is influenced by the complexity of the substrate sampled, and thus it is possible to obtain a misleading or incomplete snapshot of community structure if a completely random design is implemented irrespective of local heterogeneity. Therefore, my second objective is to create a sampling design that incorporates physical substrate complexity (e.g.,, a stratified sampling design, but not necessarily scaled by area per condition), allowing divers in situ to survey both low- and high-relief all around the island, providing an independent test of my hypotheses for how species-habitat relationships vary over time given broader context (e.g., sea otter distribution, storm exposure).


Benthic bathymetry

I will use existing side-scan sonar bathymetry data of the nearshore subtidal around San Nicolas Island (SNI), CA (Kvitek, 2011). These data have a 2m grain and extend approximately 1km offshore around the island. I will predominantly use a slope layer containing measures of substrate verticality.

Time series

I will use data from an ongoing 38-year biannual sampling program that has surveyed seven subtidal sites in the nearshore subtidal (35-40 feet sea water) around SNI (Kenner et al, 2011). Macroalgae and urchins species are surveyed within 10x2m2 transects (5 per site, 35 total), filamentous red algae and colonial species are recorded in 1m2 percent-cover quadrats (10 per site, 70 total), and fish are recorded in benthic and midwater 8x50m2 transects (5 per site, 35 total). As incorporating multiple community matrices sampled at different scales may require more time than the scope of this class, a few key indicator species will be retained and standardized for initial analyses, including 1) Giant kelp (Macrocystis pyrifera), 2) Purple urchins (Strongylocentrotus purpuratus), 3) the California Sheephead (Semicossyphus pulcher), and percent coverage of 4) fleshy red algae, and 5) suspension feeders.


1) I hypothesize sites predominantly comprised of low-relief will exhibit large shifts in community structure through time (e.g., large swings in local urchin densities), sites comprised of a mix of low- and high-relief will exhibit spatially explicit differences in species-habitat associations through time, and sites comprised of high-relief will exhibit relatively uniform species-habitat associations and minimal shifts in community structure over time (e.g., consistently low urchin densities).

2) I hypothesize low-relief sites that are homogenous across an increasing window of spatial scale will experience rapid and lasting shifts in community structure, while other sites comprised of a mix of low- and high-relief will exhibit a spatial patchwork of urchin barrens and kelp regions. I hypothesize homogenous high-relief sites will be associated with high Sheephead densities, an urchin predator whose spatial aggregation may locally increase the strength of top-down trophic regulation, limiting herbivory, and yielding macroalgal species that exhibit cycles with periodicities characteristic of populations governed by age-structured growth and senescence.


My objective is to increase my technical proficiency in ArcGIS and learn how to apply various spatiotemporal analyses, e.g., autocorrelation and spatial and temporal cross-correlation among transects within a site. Wavelet analysis has proven very useful, and perhaps those results could be related to the bathymetry. I would like to explore a variety of methods to motivate future efforts (e.g., incorporate the entire time series at a later date, perform more robust spatial autocorrelation analysis once the 2019 island-wide survey has taken place).


I would like to produce maps that show the increasing scales of benthos analyzed for the later part of question (1), along with various statistical output for analyzing patterns within- and among-sites (e.g., variograms, correlograms). For the sampling design I would like to map a pool of potential sample sites linked to GPS coordinates from which I’ll randomly select and sample 35.


Results from these analyses will provide insight into how local features are associated with varying temporal dynamics over time, potentially contextualizing experimental work planned for summer 2018 that will test a key mechanism hypothesized to vary with substrate complexity. Additionally, abrupt transitions in community structure often negatively affect ecosystem function over time; for example, sea star wasting has resulted in the domination of purple urchin populations and a crash in macroalgae, and as a consequence, for the first time since its creation, the recreational abalone fishery will not open this season. Results for the islandwide snapshot survey sampling design will directly inform questions of management interest, as the recent arrival of the invasive macroalgae Sargassum horneri threatens native species at SNI, and the current distribution is unknown.


I have a rudimentary working knowledge of ArcGIS, and can probably figure out how to do most tasks once I explicitly know what it is I need to do (and which tools or packages are required for the specific tasks). I conceptually know what I need to do, but I don’t know how that translates in terms of ESRI tools. I’ve used R to structure data, run analyses, and create figures, but I have yet to analyze spatial data.


Kvitek, R. 2012. Bathymetry data used in this study were acquired, processed, archived, and distributed by the Seafloor Mapping Lab of California State University Monterey Bay.

Kenner, M.C., Estes, J.A., Tinker, M.T., Bodkin, J.L., Cowen, R.K., Harrold, C.H., Hatfield, B.B., Novak, M., Rassweiler, A., Reed, D.C. 2013. A multi-decade time series of kelp forest community structure at San Nicolas Island, California (USA). Ecology 94(11): 2654

What landscape factors are most important to predicting beaver dam occurrence in the Oregon Coast Range?

Filed under: 2018,My Spatial Problem @ 5:51 pm

Project Overview:

North American beavers (Castor canadensis) are often referred to as ‘ecosystem engineers’ because they can fundamentally transform stream and riparian ecosystems through dam building, pond creation, and intensive foraging on vegetation. Recent literature suggests that beaver restoration via, introduction of beavers to unoccupied stream reaches, may provide for a cost-effective strategy to restore degraded watersheds. Despite these potential benefits there is also considerable uncertainty around the efficacy of beaver restoration including 1) survival of reintroduced beavers, 2) what constitutes suitable habitat for dam building, 3) quantifiable benefits of those dams, and 4) possible mal-effects or unintended consequences of beaver restoration efforts, such as flooding and damage to private property.   The goal of this analysis to consider what landscape factors can be used to predict 1) the presence or absence of beaver occupancy and 2) presence or absence of beaver dams in the West Fork Cow Creek, a tributary of the South Umpqua River in Southern Oregon.


These questions will be analyzed using data that were collected during beaver occupancy and dam presence surveys in the West Fork of Cow Creek (WFCC) during August and September of 2017. A total of 144 survey locations were sampled from the basin using metrics of stream gradient, bank-full stream width, and valley floor width. Using these geomorphic characteristics, survey locations were organized into three strata: 1) suitable for damming habitat and beaver occupancy; 2) unsuitable damming habitat but suitable for beaver occupancy, and 3) unsuitable for damming habitat and beaver occupancy. Surveys were collected along longitudinal transects upstream from the survey locations to 100m upstream.


  1. Availability of suitable beaver dam habitat will be most limited by stream gradients in the West Fork Cow Creek.

Literature on suitable damming habitat identifies more than a dozen variables that have been used to predict dam sites in watersheds throughout North America (Dittbrenner et al., 2018) but generally include factors related to perennial streamflow, stream geomorphology and food supply. In the Oregon Coast Range, dams sites have been found to occur most commonly in stream reaches with low gradient (≤ 5%), moderate bank-full width (3-6m) and wide valley floors (≥25m) (Suzuki & McComb, 1998). These characteristics reflect the criteria for selection of stream reaches in Stratum 1. However, it is not clear that each of these factors exert an equal influence on the occurrence of beaver dams with evidence that stream slope may be the most important factor because of high annual precipitation and the generally steep, dissected nature of the regions watersheds that produce high seasonal peak flows that can cause dam failures.

  1. Observed dams sites will occur more frequency where connectivity among suitable damming habitat is greatest.

Connectivity and neighborhood effects are important factors in habitat selection studies. For example, Issak et al. (2007) found Chinook salmon preferentially selected spawning locations with greater habitat size and connectivity over habitat quality. (Isaak Daniel J., Thurow Russell F., Rieman Bruce E., & Dunham Jason B., 2007). To my understanding these factors have not been well considered in efforts to predict the occurrence of beaver dam sites across watersheds.


I would like to build a logistic regression model that considers the occurrence of beaver dams sites based on a number of explanatory variables including, stream slope, bankfull width, valley slope, connectivity, and proximity to non-damming beavers.

Expected Outcome:

My goal in this effort to develop a predictive model of where beaver dams are either most likely to occur, or identify‘opportunity areas’, i.e. where dam sites could occur with beaver introductions in the West Fork Cow Creek drainage. This would include maps of predicted dam locations, and dammed stream reaches as well as locations where conflict may arise due to proximity to roads or agricultural lands.


There have been growing interest in the Umpqua River Basin among stakeholders and watershed managers to explore what opportunities beaver restoration may provide to watershed enhancement. A predictive tool would provide guidance and help to improve chances of success in relocation of beavers.

Level of preparation:

My experience with Arc-Info is low to moderate and it has been quite some time since I used any of the ESRI products on a regular basis so I anticipate there will be a challenging learning curve. Over the past 6 months I have been using R studio and feel moderately comfortable running regression analyses and developing basic charts and figures. I have no experience with Python.


Dittbrenner, B. J., Pollock, M. M., Schilling, J. W., Olden, J. D., Lawler, J. J., & Torgersen, C. E. (2018). Modeling intrinsic potential for beaver (Castor canadensis) habitat to inform restoration and climate change adaptation. PLOS ONE, 13(2), e0192538.

Isaak Daniel J., Thurow Russell F., Rieman Bruce E., & Dunham Jason B. (2007). Chinook salmon use of spawning patches: relative roles of habitat quality, size, and connectivity. Ecological Applications, 17(2), 352–364.

Suzuki N, McComb WC. Habitat classification models for beaver (Castor canadensis) in the streams of the central Oregon Coast Range. Northwest Sci. 1998;72: 102–110.


How does photosynthetic pathway of a grassland affect seasonality and drought response of productivity?

Filed under: 2018,My Spatial Problem @ 3:24 pm
  1.       A description of the research question that you are exploring.

Grasslands are key social, economic, ecological components of US landscapes, and globally, ecosystems containing abundant grassy cover are estimated to compose ~30 percent of non-glacial land cover (Still et al., 2003; Asner et al., 2004). Yet, compared to forests, we know relatively little about how the productivity of grassy landscapes will respond to future, more-intense droughts induced by climate change. The community composition of a grassland mediates its response to drought, and is critical to consider in forecasting the climate change impacts (Knapp et al., 2015). Photosynthetic pathway (C3 or C4) used by grass species is a first-order factor of community composition that strongly affects resource-use efficiencies. Grasses with the C4 photosynthetic pathway, in contrast to the ancestral C3 pathway, have comparatively higher light-use and photosynthetic efficiencies, especially under high temperatures, as well as higher water use efficiency. As a result, C3 or C4 grasses will have distinct responses to warming climate and rising CO2 (Collatz et al., 1992, 1998; Lloyd & Farquhar, 1994; Suits et al., 2005). Thus, the photosynthetic pathway composition (C3 or C4) of grass communities is a fundamental aspect of grassland and savanna function, ecology, and biogeography.

The light use efficiency (LUE) of photosynthesis is one metric that we can use to track the growth of natural grasslands. LUE is calculated as gross primary productivity (GPP) divided by absorbed photosynthetically active radiation (APAR), and can be obtained from eddy covariance (EC) flux tower measurements of ecosystem productivity and environmental conditions. Though the comparative water-use efficiency (WUE) of C3 and C4 grasslands has been well-studied, LUE has received less attention. Importantly, LUE is also correlated with sun-induced chlorophyll fluorescence (SIF), a new remote sensing index that captures intra-annual variation in production better than NDVI and EVI (Rossini et al., 2010; Guanter et al., 2014), and should be particularly useful across systems with distinct resource-use efficiencies.

Guided by these knowledge gaps, I am interested in a) comparing the seasonal dynamics of LUE between C3 and C4 grassland sites, and b) quantifying the impacts of a 2012 drought on the LUE of C3 and C4 grasslands.

Specific questions include:

  • How does the timing of annual spring greenup / increase in LUE differ between the C3 and C4 site?
  • How does the slope of the annual cycle of increase in LUE differ between the C3 and C4 site?
  • How much do these parameters vary from year to year?
  • What climatic factors (e.g., degree days, temperature, precipitation, drought severity, previous year production) are correlated, autocorrelated, or temporally cross-correlated with this variation?
  • What anomalies are associated with the timing, slope, and magnitude of LUE during a known drought year, and how do these anomalies differ between a C3 and C4 site?
  • How is coarse-scale SIF correlated with EC flux tower-scale measurements of GPP and LUE, and how does this relationship differ between the C3 and C4 sites?
  1.     A description of the dataset you will be analyzing, including the spatial and temporal resolution and extent.

My study sites are two eddy covariance (EC) flux tower locations in natural grassland areas located ~90 miles apart in eastern Kansas. The sites experience nearly identical climates, but the first is a natural tallgrass prairie composed of 99% C4 grass at Konza Prairie Biological Station outside Manhattan, KS, while the second is a replanted agricultural field composed of 75% C3 grass at the University of Kansas Field Station (Fig. 1). Because the two sites experience a very similar climate, I hypothesize that photosynthetic type strongly controls differences in LUE at each site.

LUE can be calculated from ecophysiological equations that use ground-based measurements of atmospheric gas concentrations and meteorological data. The eddy covariance (EC) flux approach uses tower-mounted instruments to measure atmospheric concentrations of water and CO2, as well as air temperature, solar radiation, and other environmental data. All measurements are taken continuously every 30 minutes. EC flux data reflect the “footprint,” or area upwind of the tower where the instruments are mounted. The footprint varies with wind speed and direction, but averages about ~250m2. EC flux data span from 2008-2015.

The main metric I am interested in is daily total LUE, which is calculated as the daily sum of gross primary productivity (GPP) divided by the daily sum of the amount of incoming radiation, or photosynthetic photon flux density (PPFD). Daily LUE is converted to units of gC·MJ-1·day-1·m-2 from units of µmol CO2· µmol photon-1·day-1· m-2   using the molecular weight of carbon and Planck’s equation. Example time series of GPP, PPFD, and LUE appear in Fig. 2.

SIF is available from the NASA GOME-2 satellite at 0.5 degree spatial resolution, at 14- and 30-day temporal resolution. Because of the coarse spatial resolution of GOME-2 data (0.5 degree), SIF from GOME-2 will be weighted by MODIS Land Cover Type quantify the amount and type of land cover within the GOME-2 grid cell associated with each flux tower site, sensu Wagle et al. (2016).

Fig. 1: Study sites and ecoregions considered in the analysis. Study sites are the EC flux tower site at Konza Prairie Biological Station (US-Kon), in Manhattan KS, and the EC flux tower site at the University of Kansas Biological Field Station (US-KFS), outside Lawrence, KS.
Fig. 2: Time series of: GPP, photosynthetic photon flux density (PPFD), and LUE from EC flux data, as well as SIF from GOME-2 for for the C3 (orange) and C4 (blue) Kansas flux tower sites. The 2012 drought appears between the gray lines.
  1.     Hypotheses: predict the kinds of patterns you expect to see in your data, and the processes that produce or respond to these patterns.

My hypotheses are driven by the seasonality and comparative resource-use efficiencies of C3 and C4 photosynthesis (Fig. 3). I hypothesize that, when examining multi-year trends, the C4 site will have a later greenup, but higher maximum GPP and LUE than the C3 site. I also hypothesize that the average slope of annual increase in GPP will be statistically significantly different between the C3 and C4 sites.

I hypothesize that precipitation and growing degree days will be strongly correlated with parameters describing the timing and seasonality of GPP and LUE.

I hypothesize that C4 sites, compared to C3 sites, will show more stable GPP and LUE under 2012 drought conditions, due to the higher WUE of the C4 pathway and higher rates of photosynthesis under high temperatures.

I also hypothesize that there will be distinct relationships between SIF and GPP, and between SIF and LUE, when compared between C3 and C4 sites, driven by the distinct resource use efficiencies of the distinct functional types. I hypothesize that the slope of the relationship between SIF and GPP and SIF and LUE will be statistically significantly higher at the C4 site than at the C3 site.

Fig. 3: Comparison of the simulated responses of C3 (solid line) and C4 (dashed line) photosynthesis. Response of net photosynthesis (a) to quantum flux, at 25 degC, and intercellular C02 partial pressure (pi) of 25 and 15 Pa for Cg and C4 respectively; and (c) to leaf temperature at pi of 25 and 15 Pa for C3 and C4 respectively and quantum flux of 1500 kmol m-2 s-‘. From Collatz et al. 1992.
  1.    Approaches: describe the kinds of analyses you ideally would like to undertake and learn about this term, using your data.

I am interested in learning about harmonic curve fitting this term. I expect that harmonic curve fitting will allow me to quantify and investigate interannual patterns in production and extract coefficients, minima, maxima, and timing of production dynamics. better than simple linear regression or generalized linear models.

I am also curious about exploring wavelet analysis with my EC flux data to investigate the degree to which annual patterns of production mimic daily patterns of production.

  1.     Expected outcome: what do you want to produce — maps? statistical relationships? other?

I want to produce statistical models that describe interannual patterns of seasonality at the C3 and C4 grassland sites. Further, I also I want to produce statistical relationships between metrics average annual seasonality and environmental conditions. I also want to produce statistical relationships between drought year anomalies in production indices and environmental conditions.

  1.     Significance. How is your spatial problem important to science? to resource managers?

LUE is a relatively under-utilized metric of tracking plant production, but will be increasingly valuable for its relationship to new remote sensing indices. Quantifying seasonal differences in LUE and other production indices and drought response at closely-located C3 and C4 grassland sites will a) clarify how LUE differs between C3 and C4 grasslands, and b) describe the drought response of LUE and how it differs between C3 and C4 grasslands. Exploring LUE dynamics facilitates using LUE-correlated satellite indices to track and predict variation in plant production. Exploring initial correlations between LUE and SIF at these sites will facilitate using SIF to track variation in production across functional types at larger spatial scales. Ultimately, I am interested in investigating how seasonal dynamics of LUE differ across plant communities of varying C4%; developing statistical relationships between the C4% of a site, seasonality, and drought response; and using SIF to track the drought response of plant communities of varying functional types.

  1.     Your level of preparation: how much experience do you have with (a) Arc-Info, (b) Modelbuilder and/or GIS programming in Python, (c) R?

  • a)  Significant experience with Arc Softwares and GUI-based image processing and analysis in Arc.
  • b)  Some exposure to ModelBuilder and Python in Arc. Some exposure to coding in Python outside Arc.
  • c)  Proficient and comfortable in statistical and spatial analysis and data visualization using R.

Works Cited

Asner GP, Elmore AJ, Olander LP, Martin RE, Harris AT (2004) Grazing Systems, Ecosystem Responses, and Global Change. Annual Review of Environment and Resources, 29, 261–299.

Collatz G, Ribas-Carbo M, Berry J (1992) Coupled Photosynthesis-Stomatal Conductance Model for Leaves of C4 Plants. Australian Journal of Plant Physiology, 19, 519.

Collatz GJ, Berry JA, Clark JS (1998) Effects of climate and atmospheric CO2 partial pressure on the global distribution of C4 grasses: Present, past, and future. Oecologia, 114, 441–454.

Guanter L, Zhang Y, Jung M et al. (2014) Global and time-resolved monitoring of crop photosynthesis with chlorophyll fluorescence. Proceedings of the National Academy of Sciences, 111, E1327–E1333.

Knapp AK, Carroll CJW, Denton EM, La Pierre KJ, Collins SL, Smith MD (2015) Differential sensitivity to regional-scale drought in six central US grasslands. Oecologia, 177, 949–957.

Lloyd J, Farquhar GD (1994) 13C Discrimination during CO₂ Assimilation by the Terrestrial Biosphere. Source: Oecologia, 994, 201–215.

Rossini M, Meroni M, Migliavacca M et al. (2010) High resolution field spectroscopy measurements for estimating gross ecosystem production in a rice field. Agricultural and Forest Meteorology, 150, 1283–1296.

Still CJ, Berry JA, Collatz GJ, DeFries RS (2003) Global distribution of C3 and C4 vegetation: Carbon cycle implications. Global Biogeochemical Cycles, 17, 6-1-6–14.

Suits NS, Denning AS, Berry JA, Still CJ, Kaduk J, Miller JB, Baker IT (2005) Simulation of carbon isotope discrimination of the terrestrial biosphere. Global Biogeochemical Cycles, 19, 1–15.

Wagle P, Zhang Y, Jin C, Xiao X (2016) Comparison of solar-­induced chlorophyll fluorescence , light-use efficiency , and process-based GPP models in maize. Ecological Applications, 26, 1211–1222.


The wrong snow for sheep…

Filed under: 2018,My Spatial Problem @ 3:20 pm

Dall Sheep on Jaeger Mesa, Wrangell St Elias National Park, Alaska. Laura Prugh.

Research question.

Dall Sheep are a species of wild sheep whose ranges extend throughout mountainous Alaska and the Yukon Territory. As a large ungulate specialised in grazing sub-Arctic to Arctic alpine regions, they are important maintainers of ecosystem function in habitats considered particularly sensitive to environmental change. They also provide an important service to local, often remote, human populations, traditionally through subsistence hunting but more recently through lucrative trophy hunting and wildlife tourism facilitation.

Since the 1980s range-wide populations of Dall Sheep have decreased by up to 21%, and in some areas emergency harvest closures have been enacted. The common explanation for this population decline has been an increase in extreme winter snow conditions reducing access to forage and increasing energy expenditure. Dall Sheep lamb in April and May and the physical condition of the ewes at the end of winter is a key determinant of the survival of their lambs, and hence longer-term population size.

To date, there has been limited empirical investigation into the relationships between longer term Dall Sheep population health and patterns of seasonal snow cover. This project seeks to address this by answering the following research questions;

  • Has there been an increased frequency of extreme seasonal snow conditions (e.g. snow depth, density/icing, duration) from 1980 to present day in Dall Sheep habitats?
  • Do instances of extreme seasonal snow conditions correlate to reduced recruitment of Dall Sheep?

Dall Sheep ranges in Alaska and the Yukon Territory. Study site is the author’s field site in the Wrangell St Elias.


Seasonal snow condition data for the analyses in this project will be prepared from the outputs of a physically based, spatially explicit snow evolution model, SnowModel (Liston and Elder, 2006). SnowModel has been run at a daily timestep for domains using climate reanalysis forcing data within 6 Alaska National Parks and Preserves (NP) from 1980 to 2017; Wrangell St Elias NP, Lake Clark NP, Denali NP, Gates of the Arctic NP, and Yukon Charley NP. Snow condition data, e.g. depth/density, has been aggregated for sheep habitat (e.g. mean snow depth above shrubline) by month and season (e.g. Winter = December, January and February) for each water year.

For each of these domains we have summer sheep count datasets that have been taken at differing frequencies and methodologies. For the scope of this project we will use a metric derived from these sheep counts as an indication of recruitment success; the lamb:ewe ratio. More lambs per ewe each summer season shows greater recruitment success.


    • Deeper seasonal snow will inhibit Dall Sheep movement and forage access, therefore increasing energy expenditure during winter, leading to decreased spring reproductive success
    • Longer durations of seasonal snow cover will correspondingly cause poorer Spring sheep condition and hence decrease reproductive success


For RQ#1 I would like to explore approaches that detect and describe statistically significant features of trends in snow cover data from 1980 to 2017. This might be the incidence of hazardous events per season, e.g. rain-on-snow; mean snow depth by month/season/water year; or snow cover duration. Potential extension to RQ#1 would be to compare the importance of climate indices, e.g. Pacific Decadal Oscillation, on the spatiotemporal patterns of seasonal snow in each domain.

For RQ#2 varying complexities and flavours of regression analysis will be explored to discover the most important features of the snow season that influence the following summer’s Dall Sheep recruitment. Initial ideas are linear mixed models and random forest.

Preliminary results comparing summer lamb:ewe ratios to simple metrics of seasonal snow and climate

Expected Outcomes

Expected, and hoped for, outcomes are statistical relationships between Dall Sheep recruitment success and seasonal snow conditions, aiding our understanding on the mechanisms driving their population decline. The sheep survey data is patchy in time and space, so identification of broad, range-wide relationships will help identify years that may have been hazardous for sheep but where no surveys were conducted. Statistical relationships describing correlations of seasonal snowfall in relation to the strength of teleconnections, e.g. the Arctic Oscillation, for each domain could help wildlife managers anticipate sheep-hostile years and make informed decisions in regard to their controlled harvest.


This project will help improve our understanding on the drivers of long-term Dall Sheep decline and inter-annual recruitment success. Understanding these drivers will improve evidence-based decision making in regard to their future management, aiding the sustainability of a critical ecosystem service.


I have a reasonable level of proficiency in ArcInfo, less so in Model Builder. I am confident in Python for geospatial analysis, though I most often use Matlab when working SnowModel output. I have zero experience in R.


Liston, G.E. and Elder, K., 2006. A distributed snow-evolution modeling system (SnowModel). Journal of Hydrometeorology7(6), pp.1259-1276.


Spatial and Temporal Patterns Among Multi-day, Overnight Wilderness Users

Filed under: My Spatial Problem @ 10:48 am
  1. A description of the research question that you are exploring.

Glacier Bay National Park and Preserve (GLBA), located in southeast Alaska, contains over 2.7 million acres of federally designated terrestrial and marine wilderness (National Park Service, 2015). Recreation users access GLBA Wilderness primarily by watercraft; the park lacks formal trail networks in its wilderness and terrestrial connectivity is fragmented by the park’s water resources. First designated as wilderness in 1980 through the Alaska National Interest Lands Conservation Act, management of the park’s wilderness has been guided by a 1989 Wilderness Management Plan (National Park Service, 1989). Much has changed in Alaska and GLBA since that time, including an increasing cruise ship tourism industry using the park’s waters and reductions in glacial ice, and the park is currently engaged in updating its Wilderness Management Plan to adapt its management practices to these modern contexts. Additionally, the Wilderness Act of 1964 includes explicit statements about how wilderness should be managed – these statements have been operationalized into the Wilderness Character framework (US Forest Service, 2008). This framework provides managers with benchmarks for understanding the degree to which the wilderness experiences of recreationists align with the characteristics of wilderness described in its enabling legislation. Of interest to the park in this wilderness management planning process is developing a better understanding the wilderness experiences of backcountry overnight users.

The advent of widely available access to Global Positioning System (GPS) technology has led to the ability to continuously track the movement of people through space and time (van der Spek et al., 2009). Using GPS tracking in recreation research expands on previous methods of data collection by providing a reliable way to continuously measure behavior and to generate precise estimates of the spatial and temporal components of visitor movement (van der Spek et al., 2009). Specifically, GPS provides researchers with exact locations and time stamps of visitor movement, whereas self-reported or researcher recorded methods of data collection are subject to estimation error and imprecision that can lead to misrepresentations of actual travel patterns (Hallo et al., 2012; van der Spek et al., 2009). Using GPS technology also removes the potential negative impacts to experience caused by more invasive methods of data collection such as physically following the visitor or observing visitor movements (Cole & Hall, 2012). Furthermore, as GPS technology has continued to advance, earlier obstacles to using GPS technology in visitor use studies, such as burden to the visitor and unit cost, have been resolved (Hallo et al., 2012).

In wilderness settings, GPS technology has been primarily employed as a tool for studying the behavior of day users – meaning those users that do not stay overnight in wilderness as part of their recreation experience. Previously, limits in GPS battery life have been the primary factor preventing the study of overnight wilderness users through GPS technology. Recently, Stamberger et al. (2018) used recreation-grade GPS units to track overnight users in Denali National Park and Preserve. While Stamberger et al. (2018) successfully collected 113 GPS tracks from multi-day users, success of the study was limited by GPS battery life, reliability of the units used, and challenges in data collection and management. Additionally, Stamberger et al.’s analyses focused primarily on the spatial distributions of users with primary results focusing on use density. This study seeks to continue to expand on the contributions of Stamberger et al. by overcoming the battery life and reliability limitations through use of a different, recreation-grade GPS unit with enhanced battery life and through implementing data collection methods in the field that reduce and address the reported logistical challenges. Moreover, this study seeks to explore the potential for new analyses for analyzing GPS data from overnight wilderness users through employing analyses that not only provide descriptions of the spatial component of use but that equally consider the temporal component of use. In this way, this study seeks to describe patterns in the behavior of multi-day, backcountry users through analysis of the spatial and temporal data collected.

Research Questions

Primary Focus: Behavior of Wilderness Users

  • What spatial and temporal use patterns emerge among overnight, multi-day wilderness users in Glacier Bay National Park and Preserve?
  • What differences or similarities emerge in spatial and temporal use among days in an overnight, multi-day wilderness trip (i.e., looking at the spatial and temporal use of all trips on day 1 do we see emerging characteristics)?

Secondary Focus: Intersection of Behavior and Location

  • What are the land cover characteristics of terrestrial wilderness use in Glacier Bay National Park and Preserve? Do relationships exist between the spatial and temporal characteristics of terrestrial use and land cover classes?
  • What are the bathymetry (or other?) characteristics of marine wilderness use in Glacier Bay National park and Preserve? Do relationships exist between the spatial and temporal characteristics of marine use and marine features? Note: I’m not sure what data I’d use to operationalize this at this time.

Practical/Data Analysis/Class Questions

  • How can the overnight, multiday tracks be meaningfully displayed and/or symbolized for reporting? I’d like to try to figure out a way that both space and time can be represented given that a central contribution of tracking overnight users is seeing their use of space through time (i.e. multiple days).
  • What analyses can be used that move beyond descriptive statistics (i.e., calculations of distance traveled, time spent)? Are there clustering analyses that take in to account both spatial and temporal characteristics rather than just spatial characteristics?
  • Are there standard diagnostic or exploratory data plots (outside of viewing the data in ArcGIS) that can be used to understand the GPS data and determine appropriate spatial statistics for analysis?
  • How can I “normalize” the data to ensure that observed differences are not a function of the number of GPS points dropped but a function of actual distances in behavior? Do I need to normalize? Note: This may not be relevant for this dataset, but I’m working with another spatial dataset for a publication where this is relevant.
  1. A description of the dataset you will be analyzing, including the spatial and temporal resolution and extent.

Dataset: The dataset for analysis is a sample of 38 GPS tracks of multi-day trips in GLBA Wilderness (Figure 1). Recreation grade, personal GPS units were administered to a sample of wilderness visitors, June through August 2017, prior to the start of their multi-day wilderness trip. Study participants were asked to carry the GPS unit for the duration of their trip and return the unit at the end of their trip. GPS units tracked visitor movement continuously throughout the trip.

Figure 1. GPS track data collected from wilderness users in GLBA Wilderness during the summer 2017 use season.

Temporal Resolution and Extent: Units recorded a GPS point at various intervals, determined as a function of speed of travel. When speed was recorded at 0 miles per hour (MPH), the GPS units recorded an X,Y location point every 60 seconds. When speed was recorded at 1 MPH, the GPS units recorded an X,Y point every 15 seconds. When speed was 2 MPH or greater, the units recorded an X,Y GPS point every 8 seconds. Data collection began with the first GPS unit distributed on June, 17, 2017 and ended with the last GPS unit returned on August, 6, 2017. Most tracks recorded between two and four days of data. Some tracks are incomplete (i.e., the entire trip was not recorded) because the battery died or the unit malfunctioned prior to being returned at the end of the participant’s overnight trip.

Spatial Resolution and Extent: At each time interval (described above), the GPS units recorded X and Y coordinates. Coordinates were recorded in decimal degrees. The geographic coordinate system for the data is GCS_WGS_1984. The spatial extent for the dataset is the park boundary for GLBA.

  1. Hypotheses: predict the kinds of patterns you expect to see in your data, and the processes that produce or respond to these patterns.

At this point, I do not have any formal hypotheses about the spatial or temporal behavior of wilderness users in GLBA Wilderness. My analyses will be exploratory, and I hope to look at several different analyses and outputs to ultimately identify an approach/analysis that works well within the limits of the data and will be practically meaningful for Wilderness managers. Ultimately, I’d like to be able to describe hot spots in both space and time and to identify spatial and temporal trends among the days of each trip.

  1. Approaches: describe the kinds of analyses you ideally would like to undertake and learn about this term, using your data.

Spatial Descriptions: I would like to create a kernel density map and perform a hot spot analysis to get practice using those tools and to understand spatially where clustering is occurring in the data. These analytical outputs are common density outputs in the recreation literature and I’d like to make sure that I’m applying them appropriately. I am also interested in potentially using a nearest neighbor hierarchical cluster analysis to understand where spatially explicit clusters exist in the data. I’ve used this analysis before, but again I’d like to make sure that I’m applying it appropriately and interpreting the output appropriately. I am also interested in an analysis (maybe path analysis?) that identify statistical patterns in the sequence of the X,Y points rather than identifying statistical hot or cold spots among the points in the GPS tracks.

Spatiotemporal Analyses: I have read a paper that uses the Space-Time Cube in ArcGIS to understand hot and cold spots in space and time and thought that the output was interesting; I would be interested in using that tool, if appropriate, to try to analyze the space and time elements of the GPS tracks together. Generally, this next level of analysis is an area where I am looking for guidance, as I’m not really familiar with other spatiotemporal analyses. I’ve been doing some initial research, but need to keep working on this end to find out what analytical tools are available. At this point, I’d be looking for something that is descriptive, and data driven as I do not have formal hypotheses to test.

  1. Expected outcome: what do you want to produce — maps? statistical relationships? other?

Ideally, I’d like to be able to produce visualizations, whether it be maps or other, that represent statistically significant spatiotemporal behaviors in the data. In essence, when a manager looks at a map or visual output, I’d like to be able to show that what is displayed is statistically significant and doesn’t just look significant because of the symbology used.

  1. How is your spatial problem important to science? to resource managers?

Since the establishment of “wilderness” as a federal lands designation, recreation researchers have engaged in research to understand the unique experience of recreating in wilderness. To date, primary methods for conducting research to shed light on the quality of wilderness experiences has used qualitative and quantitative approaches to collect interview and/or survey data from wilderness recreationists. These studies have focused on understanding an individual’s perceptions of their experiences, with topics ranging from motivations, meaning and importance, aspects of the experience, preferences for management, the social and environmental impacts of wilderness use, and the emotional benefits of wilderness experiences (Dawson & Hendee, 2009). Researchers have also sought to understand wilderness behaviors through data collection techniques such as visitor-recorded trip itineraries, visitor-mapped travel trajectories, and visitor reports on such items as trip duration, activities, and encounters with other users to name a few items. A common characteristic of these data collection methods targeting measurement of behavior is that all measure visitor perceptions or recollections of wilderness experiences and behaviors rather than actual experiences and behaviors themselves. While approximations of actual behavior, these measures are limited in utility, creating uncertainty in the understanding of such basic questions as where do wilderness users go during their trips and how long do they stay in wilderness? In seeking to measure actual behavior, researchers have employed such methods as research observation to record occurrences of specific behaviors in which visitors engage or the use of sensor technology to record counts of visitors passing a location at one time. These data collection techniques provide measures of actual behavior; however, the measurements are made at one time, and rarely can be used to provide a continuous record of visitor behavior in wilderness. Through using GPS technology to track overnight wilderness users in this study, an increased level of data accuracy and resolution is available for analyzing and understanding patterns in overnight wilderness users than has been previously possible.

From a managerial perspective, the analysis of these data will provide Wilderness managers at GLBA with an increased understanding of the overnight wilderness visitor population for use in upcoming Wilderness management planning efforts This new information is notable, as the overnight wilderness visitor population is the primary user group in GLBA Wilderness.

  1. Your level of preparation: how much experience do you have with (a) Arc-Info, (b) Modelbuilder and/or GIS programming in Python, (c) R?

Arc-Info: My level of experience with Arc-Info is proficient – I can easily navigate my way around the software and work independently to problem solve. I would not consider myself an expert as there are several tool boxes within Arc-Info that I have never used. I work primarily with vector data (point, line, and polygon) and am much more familiar with tools built for these data types. I am somewhat familiar with the spatial analysis toolbox, but have not had much success using these tools in Arc-Info as my datasets have been too large in the past. I would consider myself an expert in navigating the online help available through ESRI.

Modelbuilder/Python: My level of experience with Modelbuilder is proficient, although I have not used Modelbuilder in recent years. I know that it is an available tool for linking processes, but in the past of have automated those processes using Python rather than model builder. In my master’s program I took a course specifically oriented around learning how to use Python for data management and to leverage Arc-Info tools. The course content focused on batch processing, data management, and calling tools from Arc-Info using Arcpy. It has been a little bit since I have used these skills directly, but I’ve tried to maintain those skills and could work through some code if needed. I consider myself a beginning Python programmer with much to learn. I did save my resources from my prior class and have a great textbook on Python programming in the Arc environment that I’d be happy to share with others.

R: My R experience is new, and gained through taking STAT 511 last term. I feel comfortable in the RStudio environment, and find many similarities between Python and R. I would like to learn how to work with and analysis my GPS data in R, and how to leverage any spatial visualization tools that R has to offer. My experience in R is novice, but not intimidated!


Cole, D. N., & Hall, T. E. (2012). Wilderness experience quality: Effects of use density depend on how experience is conceived. In Cole, D.N. (Ed.), Wilderness Visitor Experiences: Progress in Research and Management, 2011 April 4-7, Missoula, MT. Proc. RMRS-P-66 (pp. 96–109). Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station.

Hallo, J. C., Beeco, J. A., Goetcheus, C., McGee, J., McGehee, N. G., & Norman, W. C. (2012). GPS as a method for assessing spatial and temporal use distributions of nature-based tourists. Journal of Travel Research, 51(5), 591–606.

National Park Service. (1989). Wilderness Visitor Use Management Plan: Glacier Bay National Park and Preserve.

National Park Service. (2015). Glacier Bay: Wilderness Character Narrative. Available:

Stamberger, L., van Riper, C. J., Keller, R., Brownlee, M., & Rose, J. (2018). A GPS tracking study of recreationists in an Alaskan protected area. Applied Geography, (93), 92-102.

United States Department of Agriculture Forest Service. (2008). Wilderness character and characteristics: What is the difference and why does it matter? Available:

Van der Spek, S., van Schaick, J., de Bois, P., & de Haan, R. (2009). Sensing human activity: GPS tracking. Sensors, 9(4), 3033–3055.

April 5, 2018

Exploring spatial and temporal behavior patterns of recreationists in Grand Teton National Park

Filed under: My Spatial Problem @ 12:33 pm

Overall context about my research and spatial problem:

For my Master’s thesis I will be exploring the spatial and temporal behavior patterns of water-based recreationists at a popular lake destination in Grand Teton National Park. More specifically, I will be examining if there are differences in the movements between three primary paddlesport user groups: canoers, kayakers, and stand-up paddleboarders. I will analyze the total distance people traveled, the amount of time people spent on the lake, the total distance traveled from shore, and if there are hot/cold spots of visitor use. This spatial analysis will be coupled with a survey that uses goal interference theory to explore perceptions of conflict between and among these user groups. Each person who receives a GPS unit will also participate in a survey about their experience and self-reported behavior during their visit. The analysis for the survey component of the research will be a bivariate regression analysis, examining the relationship between user group (independent variable) and perception of conflict (dependent variable). This research will be one of the first to combine survey data with spatial data to understand how people perceive and respond to conflict in time and space within water-based recreation settings. Further, this research will contribute to the dearth of knowledge about the spatial/temporal movements of water-based recreationists in parks and protected areas.

The caveat is that I do not have these data as I will be collecting them this summer. Therefore, for the purposes of this class, I will be using a mock dataset that will ideally allow me to use similar spatial analysis tools that can be applied towards my upcoming research. It is important to note that the dataset I’m using for this class is not water-based, but rather land-based hikers along a complex trail system. Because water-based recreation movement is typically more diffuse than trail-based recreation, and because I don’t have survey data, the research question for this class will be different from my actual thesis research question. However, the proposed research questions for this course aim to answer similar questions that I will be asking in my own research.

1. A description of the research question that you are exploring.

A. What spatial and temporal patterns emerge from of day-use hikers in Grand Teton National Park?

This research question seeks mostly descriptive answers about human movement and behavior within this trail system. How far are people going? How much time is spent recreating? Where is visitor movement clustered? Where is movement more diffuse?

B. To what extent does group size influence the spatial and temporal behaviors of people pausing or congregating in certain areas along a trail system in Grand Teton National Park?

This research question seeks to examine the relationship between two variables and allows me to explore temporal characteristics of visitor movement.

I imagine as I delve into the data other research questions will emerge.

2. A description of the dataset you will be analyzing, including the spatial and temporal resolution and extent.

The dataset I will be analyzing is a collection of 652 GPS tracks of day-use visitors at String and Leigh Lakes in Grand Teton National Park. These GPS units were distributed to a random sample of visitors between July 15 – September 8, 2017. Each intercepted visitor was asked to carry the GPS unit with them throughout the duration of their visit at String and Leigh Lakes. When deploying the units, study technicians also recorded the total number of people in the group, and the intended destination for their day visit. To maintain independence between samples, only one GPS unit was given to each group.

The GPS units used in this study were Garmin eTrex 10 units. These units collected point data every 5 seconds. The GPS tracks were saved as point features for analysis in ArcGIS so that each visitor’s hiking path can be represented by a series of points. The positional accuracy of these units can vary up to 15 meters. However, the Garmin units were calibrated with a high accuracy Trimble GPS unit which indicated a low average positional error of 1.18 meters.

3. Hypotheses: predict the kinds of patterns you expect to see in your data, and the processes that produce or respond to these patterns.

I expect to find various spatial and temporal hot spots in the trail system surrounding String and Leigh Lake. Specifically, I predict that people will cluster around the eastern shoreline of String Lake, the area of shoreline that connects to the parking lots. I imagine this clustering will be influenced by a couple factors: 1.) this area is closest to the parking lots, allowing for easy access to and from vehicles; and 2.) this area provides beach access with sections of land denuded of vegetation providing spaces for picnicking, lounging, and watersport activity.

I expect that larger groups will take more breaks than smaller groups. Moreover, I predict a positive relationship between group size and stopping behavior, i.e. as group size increases, so will the stopping behavior. A process that may be influencing this pattern is that more people in a group increases the likelihood that at least one person will want, or need, to stop. Therefore, all people in the group will be more likely to stop.

4. Approaches: describe the kinds of analyses you ideally would like to undertake and learn about this term, using your data.

As stated previously, my aim for this course is to learn analysis tools that will enable me to analyze my research data next Fall. There may be additional questions and tools I will discover throughout this course. As of now, to answer the above research questions, I intend to learn the following analyses:

Spatial Pattern Analysis Tools:

  1. Density analysis – where are there clusters in visitor use? I’d like to try doing a Kernel density analysis to achieve this.
  2. Hotspot analysis – are these clusters statistically significant compared to use in other areas of the trail system?
  3. Nearest Neighbor analysis – I am interested in learning how to use this tool but am unsure if it is appropriate given my data set. I need to investigate this analysis further.

Modeling Spatial Relationships Using Regression Analysis Tools:

  1. Ordinary Least Squares Regression – determine the relationship between group size (independent variable) and stopping behavior (dependent variable).
  2. Unknown. Perhaps there are more appropriate analyses available to answer this question. Ultimately, I would like to learn how to do bivariate and multivariate correlation analysis in this course as these approaches will be used in my own research.

Spatial- Temporal Analysis Tools:

  1. ArcGIS space-time cube – to determine the length of time people spend in certain areas. In general, I am interested in learning more about how to apply temporal analysis to these data.

5. Expected outcome: what do you want to produce — maps? statistical relationships? Other?

I’d like to create maps that represent visitor density and hot/cold spots. This will visually indicate where people are clustering both spatially and temporally.  I also want to produce a linear representation of the relationship between group size and stopping behavior. I’d also like to represent temporal results in a way that is digestible to outside audiences; perhaps through the space-time cube?

6. Significance. How is your spatial problem important to science? to resource managers?

Parks and protected area land managers strive to provide a quality user experience while also protecting natural and cultural resources. Accurately understanding how people move and behave in a recreation system allow for more informed management decision making. For example, understanding where and when there are hot-spots in visitor use could indicate a need for additional infrastructure, signage, or educational initiatives depending on the management objectives for the area. Additionally, by exploring spatial relationships between variables (in this case, relationships between group size and stopping behavior), the results can have predictive power for managers.

In the scientific and academic communities, applying spatial methods to outdoor recreation science allows for a more accurate understanding of how people move, experience, and interact in outdoor spaces. By integrating GIScience with other common social science techniques in outdoor recreation — such as surveys, observations, and interviews — scientists glean richer results that can support and contribute to existing theory, generate deeper understandings about human behavior, and inspire additional studies.

7. Your level of preparation: how much experience do you have with (a) Arc-Info, (b) Modelbuilder and/or GIS programming in Python, (c) R?

I am new to all the tools necessary for answering these research questions. Therefore, I anticipate needing to spend additional time outside of class familiarizing myself to the software before diving into the analysis.

a.) Arc-Info — I took an introductory GIS course during Winter term 2018. While I did well in the course, I did not gain as much hands-on experience with ArcGIS as I would have liked.

b.) I used Modelbuilder once during a lab exercise. Other than that, I have little experience. I have no experience in Python.

c.) I have familiarity using R and became fast friends with YouTube and Google to learn how to use this software. I initially learned how to use R in the Statistics 511 course. I also used R to analyze and graphically represent summary statistics from numerous datasets for a large visitor use and visitor impact study for the National Park Service.


Understanding trends in and environmental drivers of bull kelp abundance

Filed under: My Spatial Problem @ 10:30 am

Research Question:

Along the pacific coast of North America, Nereocystis luetkeana is the dominant canopy forming kelp from central California up to Alaska (Springer et al., 2006). This annual seaweed is subject to extensive swings in population size (up to two orders of magnitude) from year to year. This study aims to identify potential environmental drivers of the substantial observed interannual variability in N. luetkeana populations along southern Oregon and northern California. I want to compare trends in population size and correlates of population size in southern Oregon and northern California because there is a biogeographic break between the two areas, which may results in differential environmental control of kelp population size between the two regions.

Supervised classification and multiple endmember spectral mixing analysis were used to quantify canopy area and density of N. luetkeana from Landsat imagery over the past 30 years. Area and density will then be correlated with several environmental conditions expected to influence N. luetkeana population size, including water temperature, nutrient availability, wave height, climate oscillations, and urchin population size.

Datasets I will be using:

I will eventually be using a dataset of N. leutkeana canopy extent from northern California to southern Oregon provided to me from Dr. Tom Bell. This dataset covers 1985-2018 and is derived from 30m resolution Landsat data. These data will not have a set sampling routine, because clouds, tide, waves and malfunctioning sensors often make measurement of the kelp canopy impossible. Additionally, there are only a few months of the year when bull kelp reaches the surface and is quantifiable via remote sensing (roughly July – November). Therefore, temporally these data will be patchy, catching different areas at different points of the growing season in different years. Spatially, although the Landsat data is at 30m resolution, my collaborators and I use Multiple Endmember Spectral Mixing Analysis to estimate kelp percent cover within each 30m Landsat pixel. This improves our grain, but we are still limited to observing larger, more persistent patches of N. luetkeana.

I do not currently have the data, so I have generated a hypothetical stand in data set that has a similar temporal/spatial attributes.

As far as the independent variables I am looking to examine, I have:

  • Temperature data (offshore and onshore) going back to roughly 2004 (source: National Buoy Data Center and PISCO)
  • Nutrient data going back to ~1995 (source: PISCO
  • ENSO, PDO, and NPGO indices going back to the 1980s
  • Upwelling strength going back to the 1980s (source: NOAA)
  • Urchin abundances going back to the 1980s for Oregon. However, as of now this information is sparse. I currently only have information on fishing effort, rather than direct surveys of urchin abundance. When I do have surveys of urchin abundance they are extremely spatially and temporally limited.


I have two sets of hypotheses. One is my initial hypotheses, which I generated before looking at any of the kelp population data. These were that:

  1. Kelp populations in OR would have increased from about 1990-2000 due to urchin overfishing
  2. Kelp populations would have then began decreasing from about 2000 onwards due to the potentially recovery of the urchin populations and increasing SST due to climate change.
  3. California would not have seen as much variation in the kelp populations because urchin fishing did not go through a boom and bust cycle there the way it did in Oregon.

After a little initial data exploration, my more informed hypotheses are that:

  1. Kelp population size will be decreasing over time due to increasing water temperatures, decreasing nutrient availability, and increasing frequency of violent storms.
  2. Summer/fall wave height, temperature and nutrients, and upwelling will have the strongest effects on kelp population size.
  3. Climate oscillations will have weaker effects than those variables previously mentioned.
  4. Kelp population size in Oregon to be more related to upwelling strength since upwelling tends to be more variable in Oregon than in northern California. Conversely, I expect that urchin density will be more correlated with kelp population size in California than in Oregon since Oregon’s kelp populations have not collapsed in response to sea star wasting disease (and the resulting explosion in urchin populations) the way that California’s have.


I’m looking for a lot of help on this front. I’m hoping to use some analyses that will help me pick out temporal trends in kelp extent/density over time at least two spatial scales (coastwide and statewide). This might include linear regression and wavelet analysis. Furthermore, I hope to find the correlations between environmental variables and kelp extent/density at several spatial scales (coastwide, statewide, and sub-state). However, I don’t know how to go about this. My advisor feels that multivariate linear regression would be all I needed to do this. However, I have seen other researchers use methods such as: empirical orthogonal functions, generalized additive models, regression tree analysis, etc. I am overwhelmed by all the potential statistical methods and don’t know how to go about evaluating their relative merits. Also, I would like help figuring out how to quantify and deal with the high levels of correlation between many of the environmental variables.

Expected outcome:

I would like to be able to say something about temporal trends in kelp population size. Additionally, I want to produce models at 2-3 spatial scales that correlate various environmental factors with kelp abundance, and to identify whether those models differ substantially between areas and scales.


Kelp plays a number of roles in coastal ecosystems. It is a prolific primary producer, provides food, substrate, and habitat for many organisms including commercially important species, can modify wave impacts on shorelines, transports nutrients and carbon to both terrestrial ecosystems and aphotic marine ecosystems. On the pacific coast of North America, the dynamics of Macrocystis pyrifera (giant kelp) has been well-studied for decades, while those of Nereocystis luetkeana (bull kelp), the dominant canopy-forming kelp from Santa Cruz, California to Alaska, are far less understood (Springer et al., 2007). Kelp populations worldwide are declining (Krumshal et al., 2015). Understanding what environmental factors correlate with and potentially drive kelp abundance will fill a hole in our understanding of kelp dynamics along the northern pacific coast of North America and will help scientists predict the future dynamics of populations that depend on kelp for food and habitat.

Furthermore, this question is also of interest to resource managers. Bull kelp supports a variety of commercially important fisheries such rockfish and uni (sea urchin gonads), so understanding trends in kelp populations will help managers plan for fishery futures. There is an effort to reintroduce sea otters to Oregon. As sea otters frequently dine on urchins, which in turn are major grazers of kelp, kelp availability may help determine the feasibility of reintroduction. Kelp beds absorb tremendous amounts of wave energy, which is of growing importance to coastal communities battling sea level rise and increasingly violent storms.

Your level of preparation:

I am very familiar with Arc and am usually able to do just about anything with assistance from the Internet. I have used Modelbuilder and GIS programming in Python in the past, but my Python skills are limited and self-taught. I use R regularly but am less comfortable using it for spatial analyses than Arc.


April 4, 2018

The Hydraulic and Behavioral Impacts of a Floating Guidance Structure

Filed under: My Spatial Problem @ 1:36 pm

Research question:

Floating guidance structures (also called floating booms or guide walls) are long, partially-submerged panels that alter channel hydraulics to promote safe passage through man-made barriers (Schilt 2007). Their use and implementation is widespread, from the mouths of floating surface collectors to diversion channels and dam forebays (Scott 2014; Reeves et al 2016; Johnson et al 2001). However, their ability to reduce residence times and rates of turbine passage, divert individuals towards surface flow outlets, and ultimately improve passage survival at dams is highly dependent on site-specific characteristics of design and location (Johnson and Dauble 2006; Faber et al 2010; Johnson et al 2001). Until recently, the ability of an engineered structure to guide fish to safe passage has been largely tested either 1) after large-scale implementation in existing reservoirs or 2) in laboratory studies without live subjects (Johnson et al 2001; Scott 2014; Kock et al 2012; Mulligan et al 2017). This research investigates the hydraulic and behavioral impacts of a floating guidance structure in an experimental channel on juvenile Chinook salmon (Oncorhynchus tshawytscha), with the goal of informing the design of guidance structures for more efficient, effective, and safe downstream fish passage of anadromous species at man-made structures.

Description of dataset:

This dataset contains both hydraulic and behavioral information. High resolution tracks of individual fish as they encountered a floating guidance structure at 3 angles of deployment were obtained using the post-processing software, VidSync. Each of 60 trials tracks up to 5 individuals at sub-second intervals with spatial resolution of roughly 5 cm. Observations are constrained to the experimental section of the channel, which is 1.22 m wide, 0.61 m deep, and approximately 2 m long. Hydraulic measurements were interpolated from high-resolution, 3-dimensional velocity measurements at 7 cross-sections throughout the channel onto a hydraulic mesh with <1 cm resolution. Average velocity magnitude (m/s), velocity gradient (m/s/m), acceleration (m/s2), turbulent kinetic energy (TKE, m2/s2), and TKE gradient (m2/s2/m) were calculated for each corner in the hydraulic mesh. By merging these data, the hydraulics encountered by an individual at every point in its track are available.

Previous studies and hypotheses:

Previous flume studies analyzed hydraulic thresholds that incited behavioral change points in fish. Change points included changes in rheotaxis (swimming with head pointed downstream to swimming with head pointed upstream) or in tailbeat frequency. Hydraulic thresholds were discovered at a spatial velocity gradient near 1 cm/s/cm (Enders et al 2012; Vowles and Kemp 2012). Using our dataset, a change in velocity from downstream to upstream is analogous to a change in rheotaxis. Furthermore, we assume that accelerations greater than 2 standard deviations from the mean imply halting behavior (in the case of upstream acceleration) or increase in tailbeat frequency (in the case of downstream acceleration). First, we hypothesize that a hydraulic threshold exists in either spatial velocity gradient, TKE, or TKE gradient for these three behavioral changes. Second, we hypothesize that fish that do not present any of the above behavioral changes follow general rules of fish migration observed in nature: aversion to areas of accelerating or decelerating flow (Haro et al 1998), and attraction to high velocity and TKE (Coutant 1998). The experimental setup can be seen in Figure 1.

Figure 1. Image capture of video recording fish behavior in response to a floating guidance structure deployed at 40 degrees to the flow (seen on the left). Flow is from right to left – these fish are displaying positive rheotaxis.

Approach for analysis, expected outcomes, and preparation:

Proper analyses of fish tracks over time and space using Python will be essential to test our hypotheses. First, behavioral change points must be drawn from complex fish tracks that imply true behavior changes rather than subtle changes in trajectory. The end goal of the behavioral change point analysis contains two parts: 1) a series of graphs like the preliminary data shown in Figure 2, which detail the location of a change point in the channel and the hydraulics at that location, and 2) a statistical test investigating whether the hydraulics at change points between boom angles are significantly different from one another. We hypothesize that they will not; instead, a hydraulic threshold may govern the location of change points, as possibly seen at TKE equal to 10-4 m2/s2 (Figure 2). Second, fish tracks that show no behavioral change points will be investigated for their adherence to general rules of migration. This will be achieved using Arc-Info as a 3-dimensional time-series animation overlaid on the hydraulic mesh. This visualization will confirm whether fish adhere to general migration rules despite the setting of an experimental channel. I am confident in my abilities to complete these analyses using Python; however, I have a lot to learn about Arc-Info and its capabilities.

Figure 2. Preliminary data at 20 and 30 degree deployment angles of fish tracks that show behavioral change points, or halting points. Up arrows upstream behavioral changes (deceleration or upstream movement); down arrows imply downstream behavioral changes (acceleration). Colors differentiate 20 and 30 degree guidance structures.


Pacific salmon are a keystone species in both marine and riverine ecosystems. Their nutrient-rich bodies serve as a valuable food resource for many species, from humans to orca whales. Furthermore, Pacific salmon form a cornerstone of the West Coast’s industry, recreation, and culture. However, due to habitat loss, agriculture, logging, overfishing, negative interactions with invasive and hatchery-bred fish, and inadequate flows and passage through impounded rivers, native salmon populations are on the decline (Nehlsen et al 1991). For native stocks to exist in the future, new approaches to fisheries management should be explored. In this research, the impact of floating guidance structures on juvenile Chinook salmon are tested in an experimental channel, with the hope of reducing the negative impacts of dams on Pacific salmon migration and ensuring their survival for future generations.

Class test #2

Filed under: My Spatial Problem @ 12:09 pm


April 2, 2018

My spatial problem test

Filed under: My Spatial Problem @ 1:34 pm

test test

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