Spatial Analytics of Mature Panamanian Forest Stands

7/25/2019 Spatial Analytics of Mature Panamanian Forest Stands

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Spatial Analytics of Mature Panamanian Forest Stands

Map of forest inventory research plots near central Panama. Note locations of Fort Sherman, Cocoli, and BCI (BarroColorado Island) inventory plots, (R. Condit, Ecological Implications of Changes in Drought Patterns: Shifts in Forest

Composition in Panama 1998).

Ross MazurUndergraduate Student

Department of Environmental Resources Engineering,State University of New York College of Environmental Science and Forestry

[email protected]
mailto:[email protected]:[email protected]:[email protected]


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AbstractThe high productivity and diversity of tropical forest ecosystems makes them an

attractive candidate for studying species dynamics. This study sought to identifyunderlying spatially-dependent trends in both stand and species level dynamics of trees inCentral America. Spatiotemporal data collected on two intensively-inventoried forested

sites (Cocoli4 ha dry semi-deciduous, and Fort Sherman6 ha moist tropicalforest) was analyzed using variations in kernel density estimation and the cross-Kfunction methods. During the study period, the Cocoli research site exhibited moreuniform tree growth than the Sherman plot, likely due to a lesser degree of disturbance.Tree species diversity was both higher on average and more locally variable (over a 15 mbandwidth) on the Sherman plot, than the Cocoli plot. Select species, (Aspidospermacruentum andBactris coloniata at Sherman, andAlibertia edulis andProtium tenuifoliumat Cocoli) were analyzed pair-wise for differences in spatial distribution. At distances 100 m. At distances < 200 m,Alibertia edulisandProtiumtenuifoliumexhibited a moderate degree of regularity, and were randomly distributed at

distances > 200 m.

IntroductionTropical ecology is a field heavily studied around the world. Even with the

volume of research being carried out in tropical regions, little is known about them,(Basset 2001). There are a number of factors driving change, or more importantlydiversity loss, in these systems. Because they are major providers of ecosystem servicesand scientific ecological knowledge (SEK), there is often a fear of lost opportunityassociated with notstudying these sites in their current state, (Maass, et al. 2005).

As a term project associated with a spatial analysis course in the department ofEnvironmental Resources Engineering at SUNY-ESF, the task of answering research

questions pertaining to spatially-dependent datasets using analytical methods wasassigned. The Smithsonian Institute Tropical Research Group (STRG) seeks to further thescience of tropical ecology by carrying out various surveys and experiments. Two STRGforested plots located in Panama were each intensively surveyed on three occasions onseparate years. The high resolution of data for both sites makes them appropriate for thisstudy.


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Literature Review Determinants and Spatial Modeling of Tree Beta-Diversity in a Tropical Forest

Landscape in Panama.

Summary: This research team, which includes Dr. Richard Condit, the PI

of the censuses at the Cocoli and Fort Sherman sites sought to explain thediversity of plant species in Panama. Their goal was to figure out whichenvironmental factors were responsible for presence, and density of plant species,for all of the plant species at the plots studied. They used canonical multiple-regression analyses, predominantly, to draw connections. The Sherman datasetand two similar ones were used as ground truth data to calibrate a modelgenerating predictions given Landsat Thermic Mapper datasets. They found that~1/3 of species diversity has been resultant from spatial variability, while closerto 10% was directly due to environmental factors and roughly 15% of diversitycan be attributed to the combination of geographic distance and environmentalfactors, (Chust, et al. 2006).

Controls over aboveground forest carbon density on Barro Colorado Island, PanamaSummary: This group attempted to breakdown factors which were

substantial in controlling aboveground carbon density. For example the for thepurposes of this study, the Sherman site has roughly twice the number of trees peracre and roughly the same average diameter at breast-height (DBH) as the otherstand, this would likely cause it to have a much greater above-ground carboncontent, (Figure 2), (Mascaro, et al. 2011).

Ecological Implications of Changes in Drought Patterns; Shifts in ForestComposition in Panama

Summary: Dr. Condit discusses the potential effects of significant changesin duration of wet/dry seasons on forests in regions which experience long dryseasons, as well as regions which have very short if any dry seasons. Additionallyhe discusses the effects of increased precipitation during wet seasons, anddecreased precipitation during dry seasons. He postulates that the greatest threatof species loss would occur as a result of increased duration of dry seasons inregions which historically had brief, if any, dry season. Furthermore, althoughthere is a potential for biodiversity loss as a result from increased annualprecipitation in dry regions, it would not be as big of a concern as theaforementioned, (R. Condit, Ecological Implications of Changes in DroughtPatterns: Shifts in Forest Composition in Panama 1998).


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Figure 1. Cocoli (blue/left) research plot overlaid the Fort Sherman(red/right) research plot for comparison of size andtree distribution. All blue and red hatches represent an individual tree > 1 cm in DBH. There are ~9500 trees in 4 ha on

the Cocoli plot and ~24500 in 6 ha trees on the Sherman plot.

Study AreaThis study looked at two different forest types located in central Panama, located

south west of the Panama Canal, (Figure 1). One site, namedFort Sherman, was locatedon the northern side of the country, closer to the Atlantic Ocean. This site was situated ona plateau near the Chagres River. The site was located nearby a region called FortSherman, which lies on the opposite side of the Chagres River, (Figure 1). Because of itsrelative position, the Sherman site received about 2.73.0 m of precipitation annually,placing it under the classification of moist tropical rainforest, (Condit, Aguilar, et al.2004).

The second site studied was located on the south side of the country, near the

Pacific Ocean, on a hillside near the region called Cocoli. The Cocoli site received about1.92.0 m of precipitation annually, defining its classification as a dry semi-deciduousforest, (Condit, Aguilar, et al. 2004).

Dr. Richard Condit, a researcher with the STRG, was the principal investigatorwho oversaw the two research plots. Dr. Condit worked with Panamanians on themanagement of the research plots, as well as data collection.


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DatasetsThe data sets used for this project were sourced from the STRGs website. The

STRG works in collaboration with the Forest Global Earth Observatory. Together theymonitor or have data on six million trees spanning roughly 10,000 species. IncludingCocoli and Sherman, they are the host organization to 60 research plots globally,

although there are only three in Panama, (Figure 1), (D. R. Condit 2014). The Cocoli andSherman datasets contain thousands of data points each, one point per stem > 1cm inDBH. An attribute table, containing items such as: x,y-location within the plot, species,DBH (at three different times, in separate years), tree health, if the tree died, and main-stem -ploidy level, has been completed for each individual tree, (Smithsonian TropicalResearch Institute June 2004). The entirety of the Cocoli dataset was collected in threecensuses which took place in 1994, 1997 and 1998. The Sherman site data was gatheredin the same mode, but censuses were carried out in 1996, 1997 and 1999. The plotsthemselves were L-shaped and were neatly dimensioned, bounded by regions sized in 50m increments.

MethodsFour main classes of analyses, derived from kernel estimations and the cross-k

function, were carried out on the datasets. These analyses sought to generate graphics thatwould enable graphical and quantitative interpretation of specific site characteristics.

The first method used to process the data wassimple kernel density estimation,used to depict the spatial density of trees. As a part of the study, this was carried out onboth absolute and relative scales. Simple kernel density estimation is a relativelysimplistic analysis in which a matrix of analysis points (grid-layout) within the study

region is looped through, where at each analysis point, the number of events (trees in thiscase) within a specified radius were recorded. By assigning colors to these values, chosenby a color scale which covered a specified range, a visualization of density variation wasgenerated. In order to generate plots which gave a clear image showing spatial variabilityat resolution, this procedure was replicated a number of times for both datasets, tweakingbandwidth and resolution during subsequent runs. In order to draw sound conclusionsfrom comparisons of the two sites; the final resolution, bandwidth, and color bar-colorboundaries and -color stops used had to be set equal for both sites.

The second series of analyses dealt with variable-value kernel estimation(alsoknown as continuous kernel estimation). During this component, DBH values were thevariable-values tested. This method worked similarly to the simple kernel estimation, butrather than tally up the number of events within a specified radius of an analysis point,the average variable value of events within a certain radius of an analysis point wasdetermined. This was done by summing up the event-variable values and dividing by thenumber of events, within a radius of an analysis point. A color map was generated usingthe same method of assigning a color to each analysis point. Again, a single set of colorbar-color boundaries andcolor stops were used for plots of both the Cocoli and Shermansurvey sites. The plots generated depicted average DBH (mm), of the trees withinspecified radius of an analysis points. A color map was generated for each of the threetime steps for both sites. In order to meaningfully summarize these 6 rasters, and solvefor growth rates, two methods were used. First, two separate .gif files were assembled to


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show how the DBH changed with time, showing growth on a superficial level, only ableto be identified observably by humans. Second, plots showing measured differences inthe DBH from one time step to the next, and plots showing change in DBH over two timesteps were generated. These plots are by definition growthratein that they show thechange in diameter over a change in time (growth rate = DBH/t).

The third method involved the use of a third type of kernel estimation, looking atspecies diversity. This method operated similarly to the first two types, but rather thantallying the number of events or averaging a variable value within a specified radius, thenumber of unique identifiers within a specified radius was tallied for each analysis point.In the case of this procedure, the identifiers were species codes, one assigned to eachevent (tree). The script quantified number of different identifiers by looking for thenumber of unique ASCII strings associated with events (trees) within the specified radius.The bandwidth used for this method was 15 meters.

The fourth method made use of the cross-k functionmethod. The cross-k functioncompares two distinct, yet spatially comparable, sets of events, (two species wereselected for this procedure). It operates by calculating Ripleys cross-k function, which

determines the expected number of events of the other type (the other tree species) withinvarious distances. This is a cumulative function. The L-hat(h) function looks at variabilityof expected number of trees of the other species over various radii, (Bailey and Gatrell1995). This method was used to compare relative clustering of a pair of species on bothplots. In order to draw conclusions about clustering or regularity, random simulation-upper and -lower bounds needed to be generated. These bounds would act as a baselinefor expected variability. Using many replications of random relabeling, these boundscould be determined by recording extrema, and plotting bounds defined by these values.The species selected for this test were chosen because they represented a large number oftrees at the sites. At the Sherman site,Aspidosperma cruentuman upright tree, andBactris coloniataa patchy root-sucker-forming palm. At the Cocoli site,Alibertia edulisa coffee tree relative, andProtium tenuifolium,a tree with a ridged lower stem, typical ofCentral America which provides better structural support in regions with thin soils.

In addition to the L-hat(h) function plots, the locations of the trees of the selectedspecies were plotted (in isolation from the rest of the trees on the plots) to assist withvisualization of the data, and confirmation of the L-hat(h) function.


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Figure 2. Sherman research plot simple kernel density estimation. Kernel estimation carried out with a 1m kernel centerspacing, and a 10m bandwidth. Color bar values represent number of stems within a bandwidth-distance (m) of analysis

point.


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Figure 3. Cocoli research plot simple kernel density estimation. Kernel estimation carried out with a 1m kernel center

spacing and a 10m bandwidth. Color bar values represent number of stems within a bandwidth distance (1m) ofanalysis point.

Results and DiscussionThe following sample statistics provide a better perspective of the two sites. The

Cocoli site was a 4 hectare site located in a dry tropical climate, (Figure 3). There are~9500 trees located on the intensively surveyed plot, giving an average ~2370 trees/ha(~960 trees/ac), (Figure 3). The average DBH of trees on the Cocoli plot was 54.3 mm in1994, and increased to 56.2 mm by the 1998 survey, (Figure 5). The largest tree observed


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on the site had a DBH of 2.0 m. The Sherman plot was a 6 ha plot and had ~24,450 treeswith reported results, yielding an average of ~4240 trees/ha (~1970 trees/ac), (Figure 2).The average DBH of 54.6 mm in 1996, which decreased to an average of 53.2 mm for thestand in 1999, (Figure 4). The largest tree observed at this site had a DBH of 1.6 m.

Figure 4. Change in DBH over time at Sherman research plot using variable value-difference kernel estimation. Color-bar scale values represent mm ofchange in averaged DBH within a bandwidth distance of analysis point. Kernel center spacing was 2.5m and bandwidth was 20m.


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Figure 5. Change in DBH over time at Cocoli research plot using variable value-difference kernel estimation. Color-bar scale values represent mm ofchange in averaged DBH within a bandwidth distance of analysis point. Kernel center spacing was 2.5m and bandwidth was 20m.

The variation between the Cocoli and Sherman plots was most likely attributableto the greater density of trees at the Sherman plot. Two possible reasons why this wouldbe the case are one; stands could be comprised of trees which were younger on average,two; it could have to do with the fact that the Sherman site received almost twice theprecipitation.

During the study period, the Cocoli survey site exhibited more uniform treegrowth than did the Sherman plot, possibly due to a lesser degree of disturbance, (Figures4 and 5). The study period was short enough that a few seasons in a row of severe stormsat the Sherman site could cause some localized areas to decrease in DBH while assistingother areas to grow faster. Severe weather could assist in increasing the localized averageDBH via a release-cut effect, where some individuals and larger branches would beknocked out allowing the rest space to fill in and grow faster, provided the stand was in astem-exclusion phase, (Franklin, Mitchell and Palik 2007). In order to achieve a graphicwhich was not choppy, a wider bandwidth was used to plot the Cocoli site data.


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Figure 6. Cocoli research plot species diversity modified kernel estimation. Color-bar scale values represent number of different

species within a bandwidth of an analysis point. Kernel center spacing is 5m, bandwidth is 15m.


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Figure 7. Sherman research plot species diversity modified kernel estimation. Color-bar scale values represent number of different

species within a bandwidth of an analysis point. Kernel center spacing is 10m, bandwidth is 15m.

Tree species diversity was more locally variable (over a 15 m bandwidth) on theSherman plot, than at the Cocoli plot, (Figures 6 and 7 respectively). Additionally, theSherman plot had a greater overall average local species diversity, ~28 different specieswithin a 15 m radius, compared to an average of ~26 different species within a 15 m


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radius at the Cocoli plot. Even with these small differences, no spatial trends in diversitywere recorded; rather the locally-high diversity areas seemed to be randomly distributed,(Figures 6 and 7). A future study could look at degree of regularity or clustering to decidewhether or not a trend exists. There is a solid framework for basing future tree-speciesdiversity off of, as the Janzen-Connell hypothesis has received a substantial amount of

attention and rhetoric on the theory behind it. The hypothesis states that seed gathering,as well as seed parasitizing, organisms are one of the limiting factors preventing daughtertrees from being established too close to the parent tree. Reworded, the hypothesis statesthat the seeds dispersed furthest from the host plant have proportionally greater chancesof survival than closer seeds (Janzen 1970), (Hyatt, et al. 2003).

Figure 8. Cross-K function analysis, (L-function plot), of two selected species at the Sherman research site. Number of distance bins is60, number of random relabeling simulations was 100. The two selected species were;Bactris coloniata andAspidosperma cruentum.


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Figure 9. Spatial locations of individuals of the two selected species at the Sherman research plot. Red hatches represent individual

trees of species;Bactris coloniata, while blue hatches represent individuals of the species;Aspidosperma cruentum.

Select species, (Aspidosperma cruentum andBactris coloniata at Sherman, andAlibertia edulis andProtium tenuifolium at Cocoli) were cross-analyzed for differences inspatial distribution, (Figures 8 and 10).Aspidosperma cruentumis a canopy to emergent-layer tree that produces light-pink natural latex. It produces large, heavy seed podscontaining winged, wind-dispersed, seeds (Missouri Botanical Gardens Staff 2005).Bactris coloniatais an understory-occupying palm-family plant which grows in patchesor clusters, occurring as a result of adventitious shoots, and propagates via palm seeds(which have a fleshy outer portion, likely consumed and distributed by some birds ormammals), (Henderson 2000). Provided an understanding of growth form andpropagation mode, we can make assumptions about spatial distribution. We would expectthatAspidosperma cruentumis relatively randomly distributed, but generally someminimum threshold distance apart, when considering the Janzen-Connell hypothesis,(Hyatt, et al. 2003). When viewing the data, (Figure 9), these assumptions generally holdtrue. At distances < 100 m,Aspidosperma cruentum andBactris coloniata had a muchlesser degree of regularity than at distances >100 m, (Figure 8). Although Bactriscoloniatawas distributed in clusters, those clusters were not clusteredrelative to the


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spacing of theAspidosperma cruentum individuals, (Figure 9). This was the reason the L-hat(h) function never crossed above the upper bound of the random-relabelingsimulations, (Figure 8).

Figure 10. Cross-K function analysis, (L-function plot), of two selected species at the Cocoli research plot. Number of distance bins is 60, numberof random relabeling simulations was 100. The two selected species were;Alibertia edulis andProtium tenuifolium.


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Figure 11. Spatial locations of individuals of the two selected species at the Sherman research plot. Red hatches represent individualtrees of species;Protium tenuifolium, while blue hatches represent individuals of the species;Alibertia edulis.

Alibertia edulisis a shrub-form coffee plant relative which makes large ediblesoft-bodied fruits, each containing many seeds, (Silva, Pereira and dos 2008).Protiumtenuifolium is a canopy-level tree which has loculoidal-form seeds, (Mitr 1998). Onemight expect that the both of these species would behave relatively randomly indistribution. Visualization roughly confirms this, although the two are slightly spatiallydis-similar, (Figure 11).At distances < 200 m,Alibertia edulisandProtium tenuifoliumexhibited a moderate degree of regularity, and were randomly distributed at distances >200 m, (Figure 10).

Conclusions

Matlabis an efficient means for carrying out spatial statistical analysis ondatasets of all sizes and formats. Using a coding environment such as this enabledflexibility for modifying existing procedures, as well as tweaking models and modifyinga multitude of graphical output components. Using kernel density estimation allowed forrapid generation of high-resolution, smoothed stand density rasters, even with very largedatasets, such as the 25,000 data point Sherman dataset. The kernel function becameinefficient when computing the species diversity, because of the additional unique-ID


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computations to be computed for each analysis point. This inefficiency limited resolutionof the graphics generated for this time-constrained study.

Using Ripleys cross-k function, along with random relabeling was an effectiveway at comparing variance from expected density of two datasets within various radiusbin distances from a matrix of analysis points. The species selected at the Sherman plot,

(Figure 8) provided a great opportunity for quantification of relative degree of regularity,(Figure 9). The species selected for comparison at the Cocoli plot, although notdrastically different in spatial distribution, still generated informative results on degree ofregularity. This study generated a number of research questions to be resolved in futurestudies.

Future WorkThroughout the process of this study, a number of questions were raised. When

carrying out the simple kernel density estimation of tree spacing, there were localizedregions of substantially higher density (more trees per unit area). On the Cocoli plot, the

high density regions appeared to be scattered randomly throughout the plot, whereas atthe Sherman site, these high density regions appeared to occur in a linear pattern. Ifprovided more time to work on this project it would be interesting to quantify the degreeof trending, and see if there are mechanisms or rationale behind trends in high densityareas.

It would be interesting to work with a tropical dendrologist or ecologist to look atconnections between spatial dynamics of succession. The ecologist would play a role inidentifying species to analyze which would be representative of an ecosystem type, orthose which are indicators of certain ecosystem characteristics. Additionally, an ecologistwould be able to assist in classifying the health and successional stage of a stand. Futurestudies could test coherence between observed tree species-spread and their formallyunderstood seed dispersal mechanisms and fundamental niche. This could be tested suchthat the species and location of individuals are recorded at multiple year increments foran extended duration on three or four stands of the same forest type in distinctsuccessional stages. The same forest type could be defined as having roughly the same:soils, moisture regimes, climate, and comparable species diversity (in a nearby late-successional stand)). Over the past few hundred years, many silviculturists have carriedout long term studies on plots such as these, and would likely be a good source of data forinitial testing, (The Division of Silvics, Branch of Research 1935), (Susan L. Stout 2006).

Following in the theme of successional stages, it would be interesting to developmain-stem size distribution curves (similarly to the way that particle size distribution isdepicted in soils, using cumulative histograms) and see if there are connections betweensuccessional stages and the distribution characteristics of the developed curve. Therecould be much potential in finding connections between the DBH size distributions ofindividual species and other ecological factors. One study generated main-stem sizedistributions ifPinus sylvestrisplots in Finland, although only used them to look atpredictability of DBH as age of stand increased and how it affected net primaryproductivity of a stand, (LANDSBERG, et al. 2005).


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