Bennett C. Sandler, Peter B. Pearman, Mauricio Guerrero G.,
and Karen Levy.
ABSTRACT
Multi-taxonomic sampling is a common tool for quantifying effects
of disturbance on biological diversity. Results and subsequent
management implications are significantly affected by the spatial
configuration of the sampling design employed. To assess disturbance
effects, sampling should control for variability inherent in species
counts as well as variation due to natural environmental gradients.
The Center for Conservation Biology at Stanford University has
developed a methodological approach using GIS to determine a scale-sensitive
sampling scheme for measuring changes in taxonomic diversity along
a disturbance gradient. Sample data were collected at Jatun Sacha
Biological Station in Napo Province, Ecuador where primary tropical
forest conversion is advancing at an alarming rate due to human
colonization. Workstation ArcInfo was used to extract and model
a variety of landscape variables, including "imbeddedness"
in primary forest, land cover diversity, elevation, and solar
insolation, at varying neighborhood sizes around sample sites,
to test their value for predicting species richness. The GRID
module in ArcInfo was used to extract a series of landscape metrics
at varying distances from sites where intensive species surveys
were conducted. In addition, A/I was used to generate a digital
elevation model from which many of the landscape variables were
derived. PCI image processing software was used to classify land
cover from Landsat Thematic Mapper data. Pfinder software was
used to differentially correct GPS data. SAS and JMP were used
for statistical tests. Results from this research offer explicit
spatial decision-rules for designing monitoring studies to test
predicted effects of human disturbance on taxonomic diversity.
The current human-induced trend of increasing species extinction (Wilson 1988) presents a mandate to conservation biologists to elucidate natural patterns of species distribution and assemblages and the influence of perturbation on these patterns. Patterns indicate processes at work which order ecological communities (Allen and Starr 1982, Noss 1990). The critical, and often difficult task, of finding order in species distributions and assemblages is identifying the spatial scale at which those patterns occur (ESA 1991).
Community dynamics are further complicated by a lack of understanding of how different taxonomic groups, which may exhibit distribution patterns at different scales, covary in their response to disturbance (Pearson and Cassola 1992, Prendergrast et al. 1993, Gaston 1996). Ideally, resource managers would like to monitor a select set of indicator species with a minimum of sampling effort as a means for evaluating overall species richness or the impact of different management regimes (Noss 1990, Gaston 1996).
Geographers and geo-statisticians offer conservation biologists a suite of analytical tools to help assess scale and design effective inventory and monitoring schemes that capture important distribution patterns. These tools include variogram analysis (Davis et al. 1991, Isaaks and Srivastava 1989) and spatial autocorrelation indices (Isaaks and Srivastava 1989, Webster and Oliver 1990). As these tools become more commonly used in ecological research, we should gain a greater understanding of both the environmental constraints and intra- and inter-specific interactions that shape biotic assemblages.
This paper describes methods used to qualitatively and quantitatively
assess spatial distribution patterns of bird, butterfly and understory
bird species richness in Amazonian Ecuador and correlate richness
to underlying environmental gradients.
Study Area
Taxonomic surveys were conducted at Jatun Sacha Biological Station
and Reserve in the Napo Province of Ecuador. The 3600 ha reserve
is situated 30 km east of the Andean Cordillera at approximately
450 meters above sea level (7736íW, 104íS) and abuts
the Napo River. Natural habitats range from floodplain forest
and Heliconia swamp to terra firma forest on steeply dissected
ridges and swales. Approximately 70 percent of the reserve is
covered in primary forest, the balance consisting of selectively
logged forest, clear-cut regrowth of varying age, or abandoned
orchard. Species richness within the reserve is high including
over 1100 species of trees, 500 species of birds and 69 species
of frogs (Pearman et al, 1995).
Sampling Design
Between January 1994 and February 1995, understory birds, amphibians, and butterflies were surveyed at four different times at all 23 sites throughout the reserve. Sites were located in either pristine forest, selectively logged forest, or abandoned orchard at varying distance from pasture (between 15 and 1300 meters). Sites were minimally located 150 meters away from each other.
Different sampling methods were used for each taxonomic group. Birds were captured using mist nets and amphibians were sampled using visual encounter surveys. Over the duration of the study period, each site was randomly sampled twice for birds (220 mist net hours /site) and four times for amphibians. Butterflies were sampled once in January, February, and September using baited tube traps (see Pearman et al., submitted) and twice between August and September using sweep nets (Levy 1995). Sampling effort was equal among sites. Sampling order was randomized across sites within each sampling period.
Sites were georeferenced using a Trimble GeoExplorer global positioning system, differentially corrected to 5 meters. Site centroids were calculated from geo-referenced transects within each site.
Input Spatial Data
Two classes of landscape, or environmental, variables were generated
for input in this study. Physiographic variables, including elevation,
slope, aspect, and insolation were all derived from a digital
elevation model (DEM) of the area. Land cover statistics such
as proportion of primary forest and land cover diversity index
were derived from a supervised classification of Landsat Thematic
Mapper (TM) imagery.
The digital elevation model was generated from twenty meter contours and hydrologic features digitized off of the Puerto MisahuallÌ map sheet (Instituto Geogr·fico Militar, 1988) of the Ecuadorian national 1:50,000 base map series. Contour and hydrologic vectors from the reserve area were manually digitized using a Calcomp 9500 tablet attached to a Sun SPARCII workstation running ArcInfo 7.0.4. Error associated with the transformation of vectors from digitizer units to UTM projection coordinates (Zone 18, South America 1956) was 5.428 meters (rmse). The DEM was then constructed using ArcInfoís TOPOGRID command, using both the contour data and hydrology. Cell size was set to 28.5 meters to match the resolution of the Landsat TM imagery. The DEM was visually assessed for errors by inspecting a rendered hillshade surface as well as by inspecting output sink and drain diagnostic coverages. Several attributes of the input hydrology vectors required editing before an acceptable DEM was completed.
Slope and aspect surfaces were easily calculated in GRID from
the completed DEM. Solarflux, an ArcInfo AML program written (Hetrick
et al. 1993) was run to generate a surface model of incoming solar
insolation for June 21. Insolation is a more direct measure of
temperature variation than using slope and/or aspect as a surrogate.
We were unable to find cloud free TM imagery in the archives of EOSAT for any time during the two years field work was being conducted (1994-1995) so a 100 kilometer by 100 kilometer subscene of Path 9, Row 60 from July 14 1992 was purchased from EOSAT. A smaller window of this image was extracted and geocoded to the UTM projection (Zone 18, South America 1956 Datum) using eight ground control points selected within the Puerto MisuhuallÌ map (1:50,000). Error (rmse) associated with rubbersheeting the imagery was less than one half pixel (approximately 15 meters). All image processing was performed using PCI image processing software on a Silicon Graphics Indigo II Extreme workstation.
Once the imagery was geo-coded, a supervised classification was
performed using a very simple information class scheme: primary
forest, secondary forest, agriculture, bare ground/sand, and water.
Classes were trained visually with the assistance of Pearman,
who lived at Jatun Sacha for two years while supervising the field
research. Classification accuracy was also evaluated through visual
inspection. Spectral separation of information classes was robust
except for slight confusion between water and primary forest biased
by topographic shading, and between primary forest along high
reflectance ridge tops that were misclassified as secondary forest.
Classification errors were not thought to be significant enough
to warrant systematic post-classification processing. Only one
of the twenty three sample sites was misclassified. The land cover
assignment at this site (represented by four pixels) was manually
reclassified from secondary forest to primary forest to ensure
accuracy.
Analysis
Once the survey data, DEM and derived coverages, and land cover
classification were entered into the GIS and checked for accuracy,
spatial analysis was initiated. The scope of this analysis was
limited to 1) qualitative assessment of species richness patterns
in the survey data, 2) scale dependency analysis of richness and
a suite of landscape variables, and 3) correlation analysis between
richness and landscape variables. We define richness as the total
number of unique species collected at each survey site.
Our first step in assessing spatial patterns of species richness
was to plot, for each taxonomic group and subgroup, site richness
over land cover. Graduated circles, proportional to species numbers,
help visualize richness at each site (see Figure 1 for
example). These maps provided a quick analysis of spatial patterns
of species richness and whether these patterns appeared coincident
to land cover.
The second step of our analysis focused on defining spatial scale dependencies of both the response variable, richness, and the independent variables; slope, aspect, insolation, elevation, and land cover. Species richness was analyzed by plotting the variogram of richness at increasing distance using the UTM coordinates of site centroids. MGAP geostatistical software was used to calculate the variogram. (See Figure 2a). The variogram is a powerful tool for looking at the change in variability of a measured attribute with increasing distance, or its degree of spatial autocorrelation. When a collection of samples taken close together are more similar than samples taken at greater distances, the closer samples are said to be positively autocorrelated. Spatial autocorrelation is believed, therefore, to introduce statistical bias (pseudo replication) if sample sites are not far enough apart. Samples are not spatially autocorrelated when the variogram shows an asymptotic response to increasing distance (lag).
Landscape variables were checked for spatial autocorrelation using
a different statistic, the Moran Index (Goodchild 1986), which
is provided as a function within GRID. This index ranges between
-1 (strongly negatively autocorrelated) and 1 (strongly positively
autocorrelated). Since the Moran Index is calculated only from
adjacent grids (lag equals cell resolution), we calculated, for
each variable, a series of grids at incrementally larger cell
resolutions (35m, 50m, 100m, 200m, 300m,Ö1000m). Each series
of grids was generated by resampling the original raster (28.5
meter resolution) at the specified resolution. Results are presented
in Figure 3.
Quantitative analysis used buffers and overlays to generate landscape variables at incremental distances around each site. Most GIS processing was performed in GRID. A zonal grid defining distance class was generated for each site by rasterizing a multi-buffer polygon coverage containing concentric circles around each site centroid at 100 meter intervals out to one kilometer as well as smaller buffers at 35 and 50 meters. Zonal grid cells were 28.5 meters on a side to maintain spatial parity among inputs.
The zonal distance grids were then ìlayered overî terrain and land cover rasters using zonal functions to derive new grids relating neighborhood size with landscape attributes. The value attribute table (VAT) from these zonal overlays were first converted to integer format, if required, then unloaded into a spreadsheet for additional data manipulation and statistical analysis.
Initial analysis of Ithomiine butterfly data suggested that species richness was related more to the habitat, specifically, the proportion of primary forest, surrounding a sample site (neighborhood) than the habitat type in which the sample was taken (Levy 1995). This observation prompted us to generate the area of primary forest within each distance class around each site. Two other derivatives of land cover were calculated to explore the influence of landscape heterogeneity (landscape diversity index) and fragmentation (edge/area). Landscape diversity was calculated using Shannon-Wienerís diversity index (Ricklefs 1979:686). Cumulative proportions of each land cover class at each distance interval were used to calculate landscape diversity. Edge per unit area was calculated by first converting the overlay output of zonal distance and land cover into a polygon coverage. An automated routine was scripted in AML to extract vectors out to each distance class and dissolve by land cover type. A frequency count was run on the resulting vector coverage providing both the number and total length of edge between land cover classes. Total edge per distance class per site was divided by the total area calculated in the zonal distance grid.
Of all the terrain variables calculated, we only tested insolation range as a predictor of species richness. Insolation captures variability in slope and aspect suggesting it may be a better predictor of habitat heterogeneity than either of the individual terrain variables.
To explore the spatial scale of landscape and physiographic variables
that best predicted species richness, we regressed percent primary
forest, landscape diversity, edge/area, and insolation range against
species richness within each taxonomic group at each distance
class. The taxonomic groups tested included all butterflies, Satyrine
butterflies, Ithomiine butterflies, Nymphalid butterflies, Eluethrodactylid
frogs, Hylid frogs, understory birds and forest interior shrub-layer
birds. Several transformations of the input data were employed
to reduce estimate bias and increase normality. Species richness
was log transformed while percent primary forest was transformed
by taking the square root of the arcsine. JmpIn statistical software
was used to calculate all simple regressions. R2
values were plotted against neighborhood sizes to determine what
size best explained observed richness patterns.
The maps of species richness symbolized over land cover confirmed field observations that overall richness of butterflies and understory birds exhibited non-random distributions relative to land cover. Maps of taxonomic subsets which we wanted to assess as indicators of richness varied in the appearance of distribution patterns suggesting weak or insignificant correlations between indicator groups of butterflies and frogs and the larger taxonomic groups to which they belong. Forest interior shrub layer birds did show obvious distribution patterns similar to the larger sample of all non-canopy birds.
The variogram of overall butterfly richness presented in Figure 2a appears to sill between 400 and 500 meters. Semivariance starts to increase again at 1200 meters suggesting species richness may exhibit patterns at multiple scales. One might even speculate that the first sill represents the scale at which alpha diversity in butterflies in this region varies. With additional data from sites at greater distances we might expect to see a second sill in the variogram at lag distances beyond 3000 meters representing the spatial scale of beta diversity within this region. Such speculation is compelling but the results are tenuous given the unreliably small sample sizes used to calculate semi-variance (Figure 2b).
A detailed description and discussion of correlation results are presented elsewhere (Pearman et. al., submitted). However, we can report that a common trend did appear among all four independent variables tested for several of the taxonomic groups. Principally, species richness was found to be most significantly and strongly predicted at medium distances beyond the immediate zone of each sample site. The biological interpretation of these results is intuitive given the vagility of the organisms being studied.
In conclusion, our data show that the ìneighborhood countsî, e.i., the number of species found at any one site is more representative of the environment around the that site than by the environmental conditions where the sample is drawn. With specific data on neighborhood sizes, or spatial scale of taxonomic richness, for several different taxonomic groups, we arrive at simple guidelines for further analysis of our data set and for designing future studies of species distribution in this region.
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Bennett Sandler
GIS Lab, Center for Conservation Biology
Stanford University
Stanford, CA 94309
PH: (415) 725-5585
FAX: (415) 723-5920
email: bennett@bing.stanford.edu
Peter Pearman, PhD.
The Evergreen State College
Olympia, WA 98505
PH: (360) 866-6000
FAX: (360) 866-6794
email: pearmanp@elwha.evergreen.edu
Mauricio Guerrero G.
Centro para la Biologia de la Conservacion
Programa de Investigation Tropical - Ecuador
Av. Rio Coca 1734
Casilla Postal: 17-03-751 Quito - Ecuador
Telfax: (593-2) 441-592 / 250-976
email: mguerrer@jsacha.ecx.ec
Karen Levy
Environmental Defense Fund
Rockridge Market Hall
5655 College Ave.
Oakland, CA 94618
PH: (510) 658-8008
FAX: (510) 658-0630
email: karenl@edf.org