HABITAT RELATIONSHIPS AND POTENTIAL ......69 Oak ecosystem restoration on Santa Catalina Island, California: Proceedings of an on-island workshop, February 2-4, 2007. Edited by D.A.

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Oak ecosystem restoration on Santa Catalina Island, California: Proceedings of an on-island workshop, February 2-4, 2007. Edited by D.A. Knapp. 2010. Catalina Island Conservancy, Avalon, CA.

HABITAT RELATIONSHIPS AND POTENTIAL RESTORATION SITES FOR QUERCUS

PACIFICA AND Q. TOMENTELLA ON CATALINA ISLAND

Janet Franklin

School of Geographical Sciences & Urban Planning

Arizona State University, Tempe AZ 85287-5302 [email protected]

and

Denise A. Knapp

University of California, Santa Barbara Ecology, Evolution, and Marine Biology Department

Santa Barbara, CA 93106-9610

[email protected]

ABSTRACT: The goals of this project were to develop statistical models determining environmental

factors important to two endemic oak species on Catalina Island, Quercus pacifica (Island scrub oak), and Q. tomentella (Island oak), and identify suitable habitat for restoration. Both presence/absence and

abundance data were used to develop these models. Three types of models were used: Classification and

Regression Trees (CT, RT), Generalized Additive Models (GAM), and Generalized Linear Models (GLM), and predictive maps were produced in ArcView Geographic Information System (GIS).

Presence/absence modeling was found to be much more robust than modeling of species parameters such

as cover, density, and size. Models of Q. pacifica presence/absence predicted the highest probability of

species presence at intermediate values of elevation, slope steepness, topographic moisture, radiation, and distance from the coast (flow length). Greater Q. pacifica abundance (cover) was associated with

elevations above 400 m, low A-horizon pH, shallow and silty soils, north-facing slopes, and locations in

the island‟s interior (greater distance to the coast). Models of Q. tomentella presence/absence indicate that this species tends to be present on igneous and sedimentary-derived substrates with a medium and

gravelly texture, at high values of topographic moisture (TMI), intermediate slopes and flow lengths

(distance to coast) and low to intermediate levels of solar radiation. Q. tomentella tree density was highest

at moderate elevations (300-350 m), high values of winter radiation, steeper slopes, and lower flow accumulation, while reproduction is highest on concave slopes and at higher elevations; the strong spatial

structure of the extant Q. tomentella populations on the island, with two metapopulations significantly

larger in size, has likely confounded these relationships, however. Resulting maps of predicted habitat suitability can be used to assist in restoration efforts.

KEYWORDS: Classification Trees; Generalized Linear Models; Generalized Additive Models; Quercus

pacifica; Quercus tomentella; rare species; restoration; species distribution modeling.

INTRODUCTION

Poor regeneration, invasive animal and plant species, and large-scale dieback (in the case of Quercus

pacifica) threaten Catalina‟s oaks and related ecosystems, which cover nearly one quarter of the island. The Catalina Island Conservancy has sought the collaborative help of a wide range of scientists to gather

the data necessary to restore this endemic oak habitat. One of the key objectives identified by a group of

these experts in 2003 was to describe the relationship of oak distribution to environmental patterns.

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The goals of this project were to develop statistical models that would identify environmental factors important to two endemic oak species of interest on Catalina Island, Q. pacifica (Island scrub oak), and Q.

tomentella (Island oak), and identify suitable habitat for restoration. Both presence/absence data and

abundance variables such as cover, density, size and reproduction were used as the response variables.

Three types of models were used for continuous response variables: Regression Trees (RT), Generalized Additive Models (GAM), and Generalized Linear Models (GLM). Classification trees (CT) were used for

categorical variables. GAMs are useful for exploring the shape of the response functions, GLMs allow

those response functions to be parameterized and their significance tested, and classification and regression trees are particularly useful for exploring the effects of categorical predictors (Franklin 1998).

In this study we compared the results of multiple types of models in order to gain insight into complex

ecological relationships between oak distributions and environment.

The distribution of oak woodland, chaparral, and other California habitat types found on Catalina Island

such as coastal sage scrub, grassland and Catalina ironwood (Lyonothamnus floribundus ssp. floribundus)

groves have been found to be directly influenced by substratum characteristics, moisture availability, temperature and precipitation, and disturbance history (such as fire and grazing) (Wells 1962, 1968;

Harrison et al. 1971; Kirkpatrick & Hutchinson 1980; Ng & Miller 1980; Poole & Miller 1981; Westman

1981, 1991; Miller et al. 1983; Davis et al. 1986; Davis & Goetz 1990; Franklin 1998; Davis et al. 2007; Keeley and Davis 2007; reviewed in Franklin et al. 2000). Moisture availability is, in turn, influenced by

slope angle, slope aspect, soil depth, solar radiation, and climate (precipitation, fog, and temperatures

during the winter and spring growing seasons). Variables that have been particularly successful at explaining patterns of chaparral distribution, diversity, and species composition include elevation and

potential solar insolation (Franklin et al. 2000, Keeley et al. 2005, Keeley and Davis 2007). The oak

woodland community in California and its various dominant oak species appear to have strong

associations with soil depth and type (Griffin 1988). Wells (1962) noted the importance of depth and texture of soils to the distribution of different physiognomic types of vegetation, reporting a dominance of

woody types on sandy or shallow, rocky soils in central coastal California.

Quercus dumosa, the former classification of the Channel Islands endemic scrub oak Q. pacifica (Nixon

& Muller 1994), has been found to be “sensitive to slight differences in environment,” preferring

northerly exposures and almost never being found on westerly or southerly slope aspects (Bauer 1936). A

study of oak distributions on Santa Cruz Island in relation to slope, aspect, and substrate (Jones et al. 1993) revealed that oaks such as Q. pacifica (then classified as Q. dumosa) were not found to correlate

strongly with slope and aspect, although exhibiting some preference for north-facing slopes. Q. dumosa

has also been shown to be deeply rooted and limited to deep soil conditions (Kummerow & Mangan 1981). Poole & Miller (1981) found moderately high (less negative) xylem pressure potentials for Q.

dumosa when compared with other chaparral species Rhus spp., Adenostoma fasciculatum,

Arctostaphylos spp., and Ceanothus greggii, indicating that these other species are less affected by soil drought. Q. dumosa was also found to occupy areas with intermediate to low precipitation and

temperatures throughout its range in California (Westman 1991).

Q. tomentella occupies a wide range of geologic substrates and soils on Santa Cruz and Santa Rosa Islands, while preferring steep northwest- and north-facing slopes at higher elevations, canyon bottoms,

and foggy locations (Kindsvater 2006). Oak species in general on Santa Cruz Island were found only on

the “less faulted highly weathered substrates, especially the Santa Cruz Island schist and Willows diorite,” and were “absent from all disturbed substrates” (Jones et al. 1993). Maritime influences (such as fog),

elevation, and exposure appeared to produce patterns found in vegetation cover there (Jones et al. 1993).

On Santa Rosa Island, significant differences were found in soil factors for Q. tomentella stands with and

without seedlings, with regenerating sites having soils with higher pH, higher exchangeable potassium,

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lower percent clay, higher percent sand, and lower phosphorous (Kindsvater 2006). The current, limited

distribution and recruitment of Q. tomentella on Santa Rosa and Santa Cruz Islands is expected to be the result of heavy grazing by introduced herbivores (Kindsvater 2006).

Environmental predictor variables used in the statistical modeling were selected based on these known

correlations and either measured in the field or derived from other sources, as described in the following section.

METHODS

Species data used for these analyses included presence/absence information for each oak species, as well

as cover, density, size, recruitment and mortality information derived from plot data for Q. pacifica and survey data for Q. tomentella. Data collection methods are described below.

Species data collection

Species presence/absence A Q. pacifica distribution map was produced in 2004 using aerial photographs taken in 2000, and is a

refinement of a vegetation map identifying more general Island Chaparral and Island Woodland

communities (Knapp 2005). Mean polygon size is 3.2 ha (1353 polygons, range 0.08-318.5 ha). A Q. tomentella occurrence map was produced based on populations mapped in the field using a Trimble

GeoExplorer III Global Positioning Systems (GPS) unit (differentially corrected for sub-meter accuracy;

Trimble Navigation Limited, Sunnyvale, CA) as part of a full census and survey of this species on the island completed in 2005 (McCune 2005).

Quercus pacifica plots

The Q. pacifica survey was designed for the purposes of vegetation association classification, species

modeling, and mapping. Q. pacifica plots were surveyed using the relevé method (Mueller-Dombois &

Ellenberg 1974), which involves detailed plant and substrate information collected within a large plot. Plots were 40 x 10 m (400 m

2), a size found to be appropriate on other islands (K. McEachern, pers.

comm.), and which is near the larger end of the range (200-500m2) recommended for tree plots by

Mueller-Dombois and Ellenberg (1974). Care was taken to ensure that, with regard to environmental

variables, sampled plots were homogeneous and were laid out, where possible, with the long axis parallel to the slope. Data were collected between January and July of 2005. A sample of 200 point locations was

randomly generated within the mapped distribution of Q. pacifica recorded in a GIS (Geographic

Information Systems). Points that were accessible (roads to the hiking access point were drivable) were located using a GPS and surveyed first in the spring months; when the roads dried out, the number of

plots that could be feasibly completed were randomly chosen from among the remaining points. A total of

131 relevé plots were sampled.

Variables measured in the plots (Table 1) included species composition, relative and absolute plant cover

(determined visually using teams of two calibrated observers), substrate cover (e.g. soil, rock, litter),

vertical structure (cover in ground, low shrub, high shrub, and tree layers), number of dead shrub and tree individuals, and observed disturbance type (animal trailing, animal scat, human sign) and level (high,

medium, or low). Basal diameter was recorded for all trunks of up to eight selected tree individuals (up to

two within each quadrant) at each plot. In addition, soil samples were taken at both 0-10 cm (“A” horizon) and 10-20 cm (“B” horizon) depths, and were analyzed by the Soil Ecology and Restoration

Group (SERG, San Diego State University, California). Soil samples were taken under the largest

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Table 1. Field data available for modeling Quercus pacifica. Plots were 40 x 10 m (400 m2)

Type of Variable Name Description

Terrain Variables

TOPOPOSI Topographic position, determined in the field:

1=Bench (flat or nearly flat area on a slope); 2= Ridge

top; 3= Upper 1/3 of slope; 4= Middle 1/3 of slope; 5= Lower 1/3 of slope; 6=Slope bottom; 7= Stream or draw

bottom; 8= Stream terrace; 9= Other

SOILDEPT Soil Depth, estimated in the field when taking soil

samples: 1= 0 cm; 2=1-10 cm; 3=10+ cm

Disturbance Variables

TRAILING Trailing observed (low, medium, high, or none - 0-3)

SCAT Animal scat observed (low, medium, high, or none - 0-3)

RTSXPSD Roots exposed (low, medium, high, or none - 0-3)

ANTHDIS Anthropogenic disturbance evident, including fence, road, trail, cultivation, building site, corral, or other

(low, medium, high, or none - 0-3)

Cover Variables

%SOIL % cover of soil in releve plot

%ROCK % cover of rock in releve plot

%LITTR % cover of litter in releve plot

%CRUST % cover of crust in releve plot

%MOSS % cover of moss in releve plot

%LICHEN % cover of lichen in releve plot

%PLNTCOV % cover of plants in releve plot

%GRNDCOV % cover of ground-level (0-0.5 meter) plants in releve

plot

%LOWSHRU % cover of low shrubs (0.5-2 meters) in releve plot

%HISHRU % cover of high shrubs (2-4 meters) in releve plot

%TREE % cover of trees (4-6 meters) in releve plot

Soil Variables

A horizon (0-10 cm

depths); Analyzed by Soil

Ecology & Restoration

Group, SDSU.

B horizon data not used in

modeling.

PO43 (PO43-) from soil sample, ug/g sample

NH4 (NH4+) from soil sample, ug/g sample

NO3 (NO3-) from soil sample, ug/g sample

pH pH, from soil sample

ORGCON Organic content from soil sample, (g/g soil)

ORG % organic content from soil sample, by weight

CLAY % clay, from soil sample

SAND % sand, from soil sample

SILT % silt, from soil sample

CLASS classification, from soil sample

Quercus pacifica

Variables

QUPAcov cover of Quercus pacifica

50DEAD value of 1 if greater than half of the Quercus pacifica

trees are dead

DEAD number of dead Quercus pacifica in plot

SAPLS number of Quercus pacifica saplings in plot

SDLS number of Quercus pacifica seedlings in plot

REPRO sum of SAPLS & SDLS, above

AVELDIAM Average diameter of up to 8 live trees in the releve plot

AVEDDIAM Average diameter of up to 8 trees in the plot including

dead trees

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measured living oak tree; if there were no live oaks in the plot, soil was collected from beneath the largest

measured dead tree. Soils data obtained include PO43-

, NH4+, NO3

-, pH, organic content, and texture.

Quercus tomentella survey

Field survey information for Q. tomentella was collected during a 2004-2005 census and survey of all groves on the island. Variables describing the abundance and vigor of Q. tomentella and used in modeling

included number of mature trees, area of each grove, density of mature individuals, maximum DBH,

average DBH, number of dead stems, health (by category) and reproduction (Table 2). Representatives of average-sized stems were assessed visually; if the stems varied too much in size to easily pick an

“average sized” tree, then the largest and the smallest stems were each measured and recorded, and the

average calculated. Size class was assessed as follows: mature = DBH 3 cm; sapling = basal diameter >1cm and <3cm; seedling = basal diameter <1cm (the “seedling” size class, as defined, includes more that

just first year recruits). In order to take into account the size of a grove when comparing the number of seedlings and saplings between groves, a Reproductive Index was calculated for each mature grove by

dividing the total number of saplings and/or seedlings within a grove by the total number of stems, then

multiplying by 100 to give the percentage of stems which have „replacement‟ seedlings or saplings.

Table 2. Field data available for modeling Quercus tomentella, collected in stands or groves

Variable Description

MATUR # of mature individuals (distinct groups of stems, although some are likely the same genet).

Area Area of the grove in square meters

DNSITY Density: # of mature individuals divided by area of grove

MaxDBH Diameter at breast height for the largest stem observed

AveDBH Average diameter at breast height, either by averaging maxDBH and minDBH or by

measuring an average-sized individual in the field

STSD % stems dead, not resprouting

Hlth Health by subjective categories, based on observed impacts of the noted threats: poor=1,

fair=2, good=3, very good=4, excellent=5. Poor = “the demise of the grove appears

imminent,” while Excellent = “no or almost no imperfections or impacts could be found.”

Repro Reproductive index, calculated by taking the number of seedlings + saplings, dividing by the

number of mature stems, and multiplying by 100

All Q. tomentella groves on the island were surveyed and sampled, and all mature stems (ramets) were

counted and assessed at each grove (because these trees are clonal, the number of true individuals is

difficult to determine). A total of 95 groves were surveyed (for the purposes of this survey, a grove is defined as a group of trees whose canopies were not separated by more than 15 meters), clustered in

seven occurrences on the island. Occurrences are defined by the California Native Plant Society as

populations occurring more than one quarter mile from each other (Bittman 2001).

Mapped explanatory variables

Environmental variables representing factors important to oak distributions and abundances (discussed in the introduction) were used as explanatory variables. Although weather stations were recently installed on

the island, sufficient data had not yet been collected to use in this analysis. Fire distribution data were also

not available, although no oak plots were placed in areas known to have burned within the last 20 years, and fires larger than two ha have been uncommon on Catalina Island over the last century (DK, unpubl.

fire history data). GIS-derived variables used as predictors were the same for both species (Table 3), and

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Table 3. GIS data and derived mapped variables available for modeling Quercus pacifica and Q

tomentella

Variable Description

ELEV Elevation in meters, derived from a 5-m DEM (maximum island elevation: 670 m)

SLOPE Slope in degrees, generated from the 5-m DEM using ArcView

ASP Aspect in degrees, generated from the 5-m DEM using ArcView, converted to “northness,”

an aspect index (=cos(ASP+45)), for modeling. CURV Curvature, generated from 5-m DEM using ArcView (negative for convex, positive for

concave slopes) calculated as the average of planform and profile curvature

GEOL Geology: Metam = Metamorphic; IgnIn = Igneous Intrusive; IgnEx = Igneous Extrusive;

Sed = Sedimentary; SedYn = Young Sediments; Unkn = Unknown

TEXTUR Soil texture; Soil Conservation Service data 1955:

H=Fine; M=Medium; cM= Cobbly, flaggy, Medium; gM= Gravelly, medium; rM= Very

gravelly, medium; L=Coarse; X=undifferentiated; NA=not given; plus combinations of

letters above

PERME Soil permeability; Soil Conservation Service data 1955:

2= slow, 0.05-0.20 inches per hour; 3= moderately slow, 0.20-0.80 inches per hour; 4=

moderate, 0.8-2.5 inches per hour; 5=moderately rapid, 2.5-5 inches per hour; NA= not

given; plus combinations of numbers above UNDRMAT Underlying material; Soil Conservation Service data 1955: A= Acid crystalline rock; B=

Basic crystalline rock; Y= Claypan; NA= not given (plus combinations of these)

DEPTH2 Soil depth; Soil Conservation Service data 1955:

1= very deep, over 60 inches; 2= deep, 36-60 inches; 3= moderately deep, 20-36 inches; 4=

shallow, 10-20 inches; 5= very shallow, less than 10 inches; NA= not given; plus

combinations of these

FLWACC Flow accumulation (upslope contributing area), generated from 5-m DEM in ArcView. (If

FLWACC = 0 a small constant (0.01) is added because the natural log of zero is

undefined.)

TMI Topographic Moisture Index (ln [FLWACC/pixel width]/[tan(SLOPE)] where FLWACC >

0) FLWLEN Flow length (distance from point to ocean along stream channel), generated from 5-m

DEM in ArcView

RADGL0x Solar radiation: produced using Solar Analyst Extension. See the Solar Analyst Extension

documentation for details. Values used for Diffuse Proportion = 0.3, Transmissivity = 0.5.

radgl00 = summer solstice; radgl01 = equinox; radgl02 = winter solstice.

were primarily derived from a five-meter gridded Digital Elevation Model (DEM) developed photogrammetrically for the Conservancy in 2004 by Vexcel Corporation (Boulder, Colorado). This

spatial resolution of the DEM correlates well with the scale of mature oak canopies on Catalina, which

typically are five to ten meters in diameter. At the scale of the DEM, the Topographic Moisture Index (TMI) showed consistent fine-scale patterns of variability throughout the island, and thus represents

microtopographic moisture changes rather than broad landscape-scale changes. TMI is calculated by

scaling the flow accumulation (number of uphill grid cells) by the slope of each cell, if subsurface flow rate is assumed to be constant (Franklin et al. 2000).

Island-wide soil data were obtained from Soil Conservation Service maps produced in 1955, digitized by

ESRI (Redlands, CA) for the Conservancy. Within this dataset, however, permeability and soil depth had very low variability among the oak plots. General geologic variables were derived by GIS consultant

Mike Klinefelter with geologists at University of California Riverside, starting with the Geology/bedrock

GIS layer produced by the Center for Natural Areas (based on work by E. Bailey in 1940's), and combining these types into five coarser categories of geologic substrate (with 2.6% of the island mapped

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as Unknown); mean polygon size of the resulting map was 294 ha. Flow length (distance from point to

ocean along stream channel) was explored as a surrogate for coastal fog, in the absence of fog distribution data, as fog is often observed to travel up drainage basins (Figure 1). Presumably, areas farther from the

coast would have less of the moderating influence of fog, and would thus be drier.

Mean values of gridded predictor variables for each relevé were obtained using the Grid Statistics

function within the Surface Areas and Ratios from Elevation Grid (v. 1.2) extension (Jenness Enterprises,

www.jennessent.com) in ArcView 3.2 (ESRI, Redlands, CA). Relevé values for categorical (polygon) variables such as geology and soils were determined using the XTools extension in ArcView 3.2

(www.arcscripts.esri.com).

Although grazing is hypothesized to have a negative effect on recruitment, no spatially-explicit measure

of grazing intensity or history was available for this modeling study (although see Stratton and Manuwal

& Sweitzer, this volume).

Data analysis

An overview of the data analysis and statistical modeling steps is given in Table 4. Q. pacifica plot location data were used to analyze factors correlated with overall abundance, size, recruitment, mortality,

and occurrence. Q. tomentella survey data were used to analyze factors correlated with density,

regeneration and occurrence. Exploratory analyses were conducted for both species using simple (single predictor) GAMs, CTs, scatterplots, and Spearman‟s rank correlations. On the basis of these preliminary

analyses, variables were selected for use in two multiple-predictor tree models, GAMs, and GLMs.

Variables were screened for multicolinearity using pairwise scatterplots and linear correlation coefficients. In order to derive uncorrelated (orthogonal) composite variables that capture the main

variance in the soil data, a principal components analysis was conducted of soil “A”-horizon variables

using the covariance matrix, in the package labdsv in the R software. The resulting composite variables are weighted sums of the original variables. Of the composite soil variables derived from principal

components analysis, PC1 is positively correlated with A horizon NO3-, PC2 is positively correlated with

silt and negatively with sand, and PC3 is negatively correlated with A horizon NH4+; these three principle

components explained 92% of the variance in the 9-variable soils dataset. Soil did not extend beyond 10

cm depth for many of the plots, therefore only the A horizon data were used for analysis. The same

Figure 1. Fog often travels up drainage basins to a level around

400-500 m. Photo shows Blackjack

Peak in the distance. (Photo by D.

Knapp.)

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Table 4. Modeling overview

Modeling Step Approach

Choose response variables Q. pacifica: cover, crown size, recruitment, dieback

Q. tomentella: density, reproductive index

Both species: presence/absence

Descriptive statistics Histograms (response), scatter plots (continuous

variables), box plots (categorical variables)

Preprocess soil variables Principal Components Analysis

Examine predictors for multicollinearity Bivariate scatter plots

Exploratory analysis of potential predictors Correlation, simple regression (GLM) or GAM, or

simple classification trees (CT) for categorical

predictors, to select variables to be included in multiple

predictor models for each

Generate absence data for presence/absence models

Random sample of points falling outside respective species maps

Multiple predictor models of each response

variable, each species

GAMs and GLMs, with AIC used for variable

selection; Classification trees (CT) for categorical

responses and regression trees (RT) for continuous

responses.

Model evaluation R2 and p-values used to assess models of continuous

response variables;

AUC used to assess predictive performance of binary

presence/absence models

Spatial prediction using presence/absence

models

GLMs implemented in GIS as weighted sums of

predictor maps; two alternative models compared

variables tended to be correlated for the two soil depths, therefore A horizon data alone is felt to be

representative of soil characteristics.

In order to develop robust models of those factors associated with likelihood of oak species‟ occurrence,

we generated samples of absences (797 for Q. pacifica, 1000 for Q. tomentella) by randomly sampling the island but excluding those points falling within the species maps. The total number of points used for

modeling was 928 for Q. pacifica (131 present, 797 absent; sample prevalence is 14% presences) and

1095 for Q. tomentella (95 present, 1000 absent, sample prevalence is 9% presences). Models using

multiple predictors were developed using GAMs, GLMs, and RTs (for continuous response variables describing abundance) or CTs (for species presence/absence, a binary, categorical response). The Akaike

Information Criterion (AIC) was used for model selection. Manual variable selection was compared to

stepwise (automated) backward elimination.

For models of continuous responses, the explained variance and significance of the models were used for

evaluation. For the models of species presence/absence the Area Under the Curve (AUC) of the ROC

(receiver operating characteristic) plot was used as a measure of the model‟s ability to correctly discriminate between presences and absences. In this study we did not have independent data on species

presence to evaluate the models, so AUC for each presence/absence model was calculated from the same

data used to develop the models; this likely gave an overly-optimistic estimate of prediction accuracy for new data. Therefore, for the best model for each species, we also calculated a bootstrapped AUC to

estimate what that lower (penalized) AUC would probably be if new data were available.

Using the GLMs, syntax was then developed for use in the Arc View Map Calculator (ESRI, Redlands,

CA) to predict the log-likelihood ratio of species presence/absence from a weighted sum of all of GIS

layers representing the predictor variables used in each model, and to transform this to a predicted

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probability of presence, on a scale of 0-1. Predictive raster layers were converted to binary habitat

suitability maps, and then to point layers, and overlain with a vegetation map for the island (Knapp 2005) to determine the distribution of existing vegetation in areas predicted to be suitable habitat.

RESULTS

Quercus pacifica cover

Plots varied greatly in percent cover of island scrub oaks, a measure of species abundance in the plot, from one to 80 percent (mean 34%, median and mode 33%; Figure 2). Exploratory analyses of predictors

of abundance using Q. pacifica cover as the dependent variable (scatterplots and boxplots) suggested that

cover was negatively related to A-horizon pH (r=-0.25) and soil depth (r=-0.17). Cover was positively related to flow length (distance from the coast; r=0.20) and soil PC2 (high silt, low sand; r=0.22). Q.

pacifica trees occurred somewhat more often on igneous intrusive substrates than expected given their

extent on the island (27% occurrence, 16% available). No relationship was found with the remaining

variables. Based on this exploratory analysis, candidate variables were selected and models were fit using multiple predictors, described below.

The RT model explained 38% of the variance in cover, and included the following predictor variables:

ELEV, A-horizon pH, PC2, FLWLEN, the “northness” aspect index (=cos(ASP+45)), and GEOL (refer

to Table 1 and 3 for definitions of these variables). Higher cover values are found at elevations >418 m, or lower elevations if A-horizon pH<5.49 (especially at higher flow length [farther into the island‟s

interior]), OR if A-horizon pH is higher, then at higher values of PC2 (silty, not sandy).

The GAM of cover suggested non-linear relationships between cover and PC2, elevation, flow length and

northness (Figure 3), although none of these non-linear responses had significantly greater fit than linear

responses (details not shown). Higher cover values were associated with low values of soil depth and A-horizon pH, moderately high values of PC2 (silty textured soils) and northness, high values of flow

length, and extreme (high and low) values of elevation.

In the GLM of cover, only ELEV (2nd

order polynomial; p=0.003), A-horizon pH (p=0.095) and soil PC2 (p=0.041) were significant or nearly so; the model was significant (p=0.005) but explained a modest

amount of variance (R2 =

0.237). Higher cover values were associated with high values of soil PC2 (silty

textured soils), but low values of A-horizon pH, low values of soil depth, high northness, extreme values of elevation and high flow length, as in the GAM (but with less variance explained, as noted above).

Figure 2. Histogram of Q.

pacifica cover values in the

131 plots.

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Figure 3. Smoothing function fit for Q. pacifica GAMs. X-axes are the explanatory variables, y-axis represents the predicted value of cover based on

the smoothing function.

Quercus pacifica size

Average size may indicate conditions appropriate for growth and survival. Exploratory GAMs, scatterplots and Spearman‟s rank correlations suggested that size (average diameter) is slightly positively

related to elevation (r=0.17), flow accumulation (r=0.12) and slope (r=0.09) (the nonlinear terms not

significant), and slightly negatively related to northness, flow length, and cover of rocky substrate (r=-0.13). Size was also slightly positively related to soil depth and animal scat, and unimodally related to A-

horizon NH4+ and organic matter. Based on this exploratory analysis, RT and GLM models were fit using

multiple predictors, described below.

Key variables selected for use in a RT model included A-horizon NH4+ (high diameter at high values),

slope (high diameter at higher values), PC1 (A horizon NO3-), and northness (Figure 4). Although the 12-

node model used explained 41% of the variance in size, cross-validation results suggest that the full model is overfit to the training data; only a 2- to 6-node model would be necessary to make robust

predictions (11-29% of variance explained).

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Figure 4. Regression tree for Q. pacifica size. Graphical representation of tree decision rules.

Values at terminal nodes are average values of AVELDIAM (stem diameter in cm) for observations at that node (see Tables 1 and 2 for variable descriptions).

Significant predictors of tree size in the GLM included quadratic terms for A-horizon NH4

+ (p=0.046) and

slope (largest size at intermediate values), and positive linear terms for flow accumulation (p=0.015) and

disturbance (p=0.062) (largest average tree size at higher values of these variables, although disturbance

is likely the effect, not the cause). This model, although significant, only explained 12% of the variance in size (based on adjusted R

2).

Quercus pacifica recruitment

Exploratory analyses indicated that there was a slight tendency for juvenile abundance (count of seedlings plus saplings) to be higher in intermediate canopy cover (20-40%), at low values of PC1 (low A-horizon

NO3-) and flow length (nearer to the coast), and in plots with fewer dead trees (Figure 5). However, in a

multiple linear regression model (GLM) only flow length and PC1 were significant, and the model, while significant at the 0.05 level, only explains 7% of the variance in recruitment among plots. These same two

variables were used in a RT model but with similarly low explanatory power.

Figure 5. Boxplot showing number of dead trees (x-axis)

versus number of juveniles in Q.

pacifica plot (y-axis).

80


Quercus pacifica dieback

Correlates of extensive tree mortality (“dieback”) were difficult to identify using these data because dead trees only occurred in 32 of the 131 plots, 19 of those having only one dead tree. Fewer than ten plots

contained 20 or more dead trees. Raw number of dead trees was slightly correlated with PC1 (high A-

horizon NO3-;r=0.177) and there was a slight tendency for plots with dead trees to be found at higher

elevation (r=-.146) and lower flow lengths (r=0.134) or high on the hillslope (upper or middle third of slope).

Quercus pacifica presence/absence

A classification tree model, pruned to the best ten nodes, indicated that Q. pacifica is more likely to be

present if:

Winter radiation is low (steeper, north-facing slopes), flow accumulation >2.54 (not right on a

ridge where FLOWACC=0), slope is not extremely steep (<26) AND elevation >189 m (58% of 108 observations);

OR if {elevation <189 m AND north facing slope} (node 41, 34% of 29 observations);

OR if {winter radiation is low, flow accumulation >2.54 (not right on a ridge), slope is extremely

steep (>26) AND texture class is gravelly-medium} (70% of 10 observations);

OR if {winter radiation is higher (>1169), but flow accumulation is higher than 1.26 (not right on

ridge), Geology is either igneous extrusive, a mixture of metamorphic and unknown, a mixture of

young sedimentary and igneous extrusive, or unknown, AND flow accumulation is >25 (mid

slope or lower slope} (57% of 14 observations).

To summarize the CT model results, Quercus pacifica tends to be present on low-radiation, steep, north-

facing slopes, or in higher radiation environments if lower on the hillslope or on certain substrates. Bivariate GAMs suggested a unimodal response curve of this species to radiation, elevation, slope, flow

length and topographic moisture index (TMI) (Figure 6); in other words, the highest likelihood of

presence is at intermediate values and the lowest likelihood is at extreme values of these variables.

The data for Q. pacifica show spatial structure, with species presences appearing clustered relative to the

randomly-located pseudoabsences. Join-count statistics showed that, at a spatial lag distance of 400 m, the

errors resulting from the GLM predictions (probability threshold of 0.15) were positively associated. False positives (FP) are near other FPs more often than expected by chance, and the same is true for false

negative predictions (FN) – the z-values are greater than 1.96. This suggests that future work should use

one of the existing methods to incorporate spatial dependence into species distribution models in order to improve predictions.

Models using multiple predictors were developed with aggregated geology and texture classes, and TMI.

A pruned 9-node CT model used five variables and had an AUC of 0.85. All GAMs and GLMs had AUC values >0.9, meaning that, for a randomly selected pair of presence-absence observations, the model

prediction for presence will be greater than the prediction for absence over 90% of the time. Stepwise

selection tended to produce more accurate models than manual, and GAMs better than GLMs, although these differences were negligible (Table 5). GLMs were chosen to produce predictive maps because they

are easy and intuitive to use for prediction.

81


Figure 6. Shape of response function between the log-likelihood ratio of Q. pacifica

presence (y-axis) and environmental variables (x-axis) shown by bivariate GAMs.

Table 5. Summary of generalized linear models of Q pacifica and Q. tomentella probability of

presence used for spatial prediction of habitat suitability shown in Figures 7, 8, 13 and 14. * - bootstrap AUC. Refer to Tables 1 and 3 for variable descriptions.

Model Pred Var AIC AUC Notes

Q. pacifica

Qupapred1

-3.806 - (0.8826 * Iginsed) + (1.852 * Unk) + (0.000623

* Flwlen) - (0.00000006083 * Flwlen2) + (0.0003217 *

Radgl01) - (0.0000003189 * Radgl012) + (1.693 * Tmi) -

(0.4438 * Tmi2) + (0.01686 * Elev) - (0.00002893 *

Elev2) + (0.1922 * Slope) - (0.006794 * Slope2)

517 0.894 Manual

selection

Q. pacifica

Qupapred2

-4.674 + (0.02491 * Elev) - (0.00004277 * Elev2) +

(0.3373 * Slope) - (0.008264 * Slope2) - (0.041 * Curv) -

(0.008949 * Curv2) + (0.5598 * Tmi) - (0.002177 *

Radgl01) - (0.7437 * Iginsed) + (1.269 * Unk)

452 0.924

0.906*

Stepwise

back

Q. tomentella

Qutopred2

-4.036 - (2.878 * Ignin) - (5.059 * Metam) - (6.935 *

Sed) - (1.161 * Sedyn) - (16.28 * Unkn) + (0.001557 *

Flwlen) - (0.0000001502 * Flwlen2) + (0.001548 *

Radgl01) - (0.0000004163 * Radgl012)

257 0.968 Manual

selection

Q. tomentella Qutopred3

-5.012 - (2.616 * Ignin) - (6.53 * Metam) + (0.088 * Sed) - (1.814 * Sedyn) - (15.41 * Unkn) + (0.001926 *

Flwlen) - (0.0000001776 * Flwlen2) + (0.0008342 *

Radgl01) - (0.0000001976 * Radgl012) + (0.1348 * TMI)

+ (0.05672 * TMI2)

233 0.977 0.967*

Stepwise back

82


Elevation, slope, curvature, TMI, solar radiation, and geology (igneous intrusive, sedimentary, and

unknown) were used in the ArcView Map Calculator to predict the log-likelihood ratio of species presence/absence using the GLMs based on both manual (Qupapred1; Figure 7) and stepwise

(Qupapred2; Figure 8) variable selection (Table 5). This likelihood ratio was transformed to a predicted

probability of occurrence. Note that the colors in Figures 7 and 8 are scaled so that positive predictions

(high likelihood of presence, shown in white to red) are those probabilities greater than 0.15 (approximately equal to prevalence of the species in the sample, which was 14% in this case). The

prevalence of species presence in the sample can be an appropriate threshold to use for a binary prediction

of suitable versus unsuitable habitat according to the literature, although other threshold criteria can be used, such as average predicted probability.

A cross-tabulation of the thresholded binary maps of predicted suitable habitat based on these two models with the map of existing vegetation is presented in Table 6. The majority of the area of predicted suitable

habitat is found in locations currently mapped as Island chaparral; less area is located in grassland, and

still less in coastal sage scrub. These areas of predicted suitable habitat are not located randomly with

respect to existing vegetation (Chi-square, p<0.001), and disproportionately occur in Island chaparral and grassland. There are differences between the manual and stepwise GLMs, including the variables selected

and the form of the response (linear versus unimodal). The manual selection method predicted a larger

area of potential habitat (Qupapred1; 17%) than the stepwise method (Qupapred2; 4%, which is suspiciously low), however suitable habitat is predicted to occur in roughly the same area.

Figure 7. Spatial predictions of Q. pacifica based on the best GLM using manual variable selection (Qupapred1). Sampled oak points are also shown in blue (Qupapoint.shp).

83


Figure 8. Spatial predictions of Q. pacifica based on the best GLM using stepwise (backward

elimination) variable selection (Qupapred2). Sampled oak points are shown in blue (Qupapoint.shp).

Table 6. Existing vegetation (from vegetation map) found in areas of predicted suitable habitat for Q.

pacifica. Only values of 0.5% or higher are presented.

Vegetation Type (percent

area of island)

Percent of predicted area Area predicted (ha)

Qupapred11 Qupapred22 Qupapred1 Qupapred2

Island Chaparral (29.4%) 42.0% 48.8% 1,369 388

Grassland (19.5%) 29.8% 28.2% 972 224

Coastal Sage Scrub (38.1%) 12.0% 17.1% 746 136

Bare (9.4%) 3.7% 3.2% 121 25

Island Woodland (0.5%) 0.6% 1.5% 22 12

1 GLM manual selection, probability of presence 0.15 or higher 2 GLM stepwise selection, probability of presence 0.15 or higher

Quercus tomentella density

Tree density shows strong spatial structure, with high values in the most northwest cluster of groves (in the Mount Orizaba area) and low values in the southwest cluster (Figure 9). Preliminary screening of Q.

tomentella density data using scatterplots indicated that solar radiation, flow accumulation, elevation,

geology, soil texture, slope, and curvature appeared to be related to abundance (density). The relationship

with elevation appeared to be unimodal with a peak at about 350 m, while the relationship with slope, solar radiation, and curvature appeared to be nonlinear but monotonic-increasing (positive) (Figure 10).

84


Figure 9. Q. tomentella density proportional to size of circles in the 95 groves, showing high values in the northwest cluster of groves and low values in the southwest cluster.

Figure 10. Shape of response function between Q.

tomentella density (y-axis) and environmental variables (x-

axis) shown by bivariate GAMs.

Quercus tomentella density

3687000.00

3688000.00

3689000.00

3690000.00

3691000.00

3692000.00

3693000.00

3694000.00

3695000.00

3696000.00

364000.00 366000.00 368000.00 370000.00 372000.00 374000.00 376000.00 378000.00 380000.00

Easting

No

rth

ing

85


A multiple regression tree (RT) model with four nodes explained 24% of the variability in density.

Density was predicted to be higher at lower values of flow accumulation (higher on the slope or in the watershed), where values of radiation were relatively higher but slope curvature was low, and also where

radiation was low (north-facing slopes). Cross-validation of this model suggested that it was not

particularly robust.

A GAM using six variables explained 31% of the variance in density and predicted higher density at

higher winter solar radiation, lower values of flow accumulation, higher values of curvature, and

intermediate values of elevation (Figure 11). Only the non-linear term for radiation was significant.

Quercus tomentella reproductive index

Exploratory variable selection using bivariate GAMs indicated a significant and positive relationship

between Q. tomentella reproductive index and elevation (D2 = 0.06) and curvature (unimodal with

optimum at 20 or moderately concave; D2 = 0.19). A four-node regression tree (RT) model predicted

higher reproduction on more concave slopes or, if on less concave slopes, at elevations around 400 m. While the full model explained 33% of the variance in reproduction, cross-validation suggests that

curvature yields the only significant split. Similarly, a multiple GAM (adjusted D2 = 0.25) revealed

higher reproduction at curvature values around 20 and elevation around 400 m, as well as lower winter radiation, with a slight preference for certain substrates (igneous) and soil texture classes (gravelly,

medium) (Figure 12). The relationship with flow accumulation was complex. A GLM (Gaussian link

function) using the same variables explained 17% of the variance in reproduction, however the only significant coefficient was for curvature.

Quercus tomentella presence/absence

Both single- and multi-variable classification trees were used to identify groups of categorical data classes

associated with presence or absence of Q. tomentella. Geologic substrate (igneous extrusive and intrusive,

sedimentary) and soil texture (gravelly, medium) had a positive association with species presence, and were aggregated for use in further analysis. Bivariate GAMs were used for variable selection and to

examine response curve shapes for continuous variables. Flow length, radiation, and slope showed

unimodal responses with highest likelihood of presence at intermediate values (Figure 13). High

prevalence was also found on the most extreme (concave and convex) slope curvatures, which does not make ecological sense.

A multivariate classification tree, pruned to the best 12 nodes (not shown), used geology, slope, flow length, TMI, and solar radiation as predictors, and had an AUC of 0.998. This model indicated that the

probability of finding Q. tomentella is high if:

Geology is igneous extrusive, sedimentary, or sedimentary young AND texture class is

gravelly/medium AND flow length (distance to coast) is intermediate (7519 > FLWLEN > 4843)

AND radiation in winter is low (< 5548) (94% of 72 points where Q. tomentella is present);

OR if geology is igneous intrusive, metamorphic, or unknown (98.4% of 625 points where Q.

tomentella is absent);

BUT exceptions are found where TMI is high (>4.28), texture is gravelly/medium or medium,

slope is greater than 10 degrees and flow length is high (> 1744) (100% of 7 points where Q. tomentella is present).

86


Figure 11. Shape of response function between Q. tomentella density (y-

axis) and environmental variables (x-axis) shown by a multivariate GAM.

Figure 12. Shape of response function between reproduction (y-axis)

and environmental variables (x-axis) shown by a multivariate GAM.

87


Figure 13. Shape of response function for Q. tomentella between log-likelihood of species

presence (y-axis) and environmental variables (x-axis) shown by bivariate GAMs.

Stepwise selection tended to produce more accurate models than manual (Table 5), and GAMs better than GLMs, although these differences were negligible. GLMs were chosen to produce predictive maps

because they are easy and intuitive to use for prediction. The best GLM based on manual variable

selection (Qutopred2) included geology (igneous intrusive, metamorphic), TMI, summer solar radiation

(p=0.033), and flow length as significant predictors (p < 0.001 unless otherwise noted), the last two as second-order polynomials approximating a gaussian response function. Stepwise variable selection

yielded the final GAM and GLM (Qutopred3) models, which included elevation (p=0.017), slope

(p=0.002), TMI (p=3.0e-07), flow length (p=1.75e-05), winter solar radiation (p=0.025), and aggregated geology (p=9.77e-13) as significant predictors (TMI and geology as second-order polynomials).

Binary geology (igneous intrusive, metamorphic, sedimentary, and sedimentary young) maps were

created, and from these flow length, TMI, and solar radiation at equinox were used in ArcView Map Calculator to predict the log-likelihood ratio of species presence/absence. This was transform to a

predicted probability of occurrence. The first prediction map, Qutopred2, derived from manual variable

selection (Figure 14; 14%), produced a smaller area of high habitat suitability than Qutopred3 (Figure 15; 29%), which was based on stepwise variable selection. However, suitable habitat is predicted to occur in

roughly the same locations.

A cross-tabulation of the thresholded binary maps of predicted suitable habitat based on these two models

with the map of existing vegetation is presented in Table 7. Raster cells with probabilities greater than the

threshold value of 0.10 (approximately equal to prevalence of the species in the sample which was 9%)

were assumed to be suitable habitat. Fourteen and twenty-nine percent of the island was found to be suitable using the manual and stepwise results, respectively, at this threshold. The majority of the area

predicted to be suitable for Q. tomentella currently supports coastal sage scrub, fewer are in Island

88


Figure 14. Spatial prediction for Q. tomentella from the GLM based on manual variable selection. Mapped oak stands (polygons) are also shown in blue (Qutopoly.shp).

Figure 15. Spatial prediction for Q. tomentella from the GLM based on stepwise variable

selection. Mapped oak stands (polygons) are also shown in blue (Qutopoly.shp).

89


Table 7 Existing vegetation (from vegetation map) found in areas of predicted suitable habitat for Q.

tomentella. Only values of 0.5% or higher are presented.

Vegetation Type (percent area of

island)

Percent of predicted

area

Area predicted (ha)

Qutopred21 Qutopred32 Qutopred2 Qutopred3

Coastal Sage Scrub (38.1%) 35.4% 38.8% 990 2,197

Island Chaparral (29.4%) 30.3% 30.0% 846 1,695

Grassland (19.5%) 25.8% 19.1% 721 1,082

Bare (9.4%) 5.4% 7.9% 152 445

Non-native herbaceous (0.5%) 0.8% 0.9% 24 52

Developed (1.1%) 0.0% 0.8% 0 47

Island Woodland (0.5%) 0.7% 0.7% 20 38

Southern Riparian Woodland (0.3%) 0.6% 0.6% 18 34

1 GLM manual selection, probability of presence .15 or higher

2 GLM stepwise selection, probability of presence .15 or higher

chaparral, and still fewer are in grassland, according to the map of existing vegetation. The areas of

predicted suitable habitat appear to be located randomly (proportionally) with respect to existing

vegetation. Both occur in similar proportions and a chi-squared test shows the difference being non-

significant (p=0.904 and .994; quotopred2 and qutopred3, respectively).

DISCUSSION

The fine resolution of the DEM used to generate explanatory variables, the mapping accuracy of the plots

and distributions, and the random location of the plots makes this an ideal data set with which to perform

modeling (Franklin et al. 2000). Presence/absence modeling was found to be much more robust (optimistic AUC between 0.85 and 0.998) than modeling of species parameters such as cover, density,

and size (7% to 38% variance explained). In other words, multiple regression models of the species

abundance and fitness variables, although they identified significant predictors, did not explain much of

the variability in those variables, as is often the case with ecological datasets. However, predictive models of species occurrence performed well in discriminating known locations of species presence and

absence. The two oak species are discussed separately below.

Quercus pacifica models

The two most explanatory models for Quercus pacifica were for presence/absence and, to a lesser extent,

cover. Greater abundance (cover) was associated with moderately high elevations (above 400 m), low A-horizon pH, shallow and silty soils, north-facing slopes, and locations in the island interior (greater

distance to the coast). Larger average tree size within plots was associated with intermediate values of A

horizon NH4+, steeper slopes, and high flow accumulation (lower drainage basin position), but very little

variation in size was explained.

Classification tree models of Q. pacifica presence/absence data (AUC 0.85) indicate that this species tends to be present on low-radiation north-facing slopes, or sometimes in higher radiation environments if

lower on the hillslope or if on certain geological substrates. GLMs of suitable Q. pacifica habitat based on

multiple variables (AUC 0.89 and 0.92 for manual and stepwise variable selection) predict the highest

probability of species presence at intermediate values of elevation, slope steepness, TMI, radiation, and

90


distance from the coast (flow length). Less suitable habitat is associated with igneous intrusive and

sedimentary substrates.

These results, taken together, indicate that Q. pacifica dominates at intermediate levels of moisture

(represented by intermediate TMI, radiation, and soil texture), and prefers north-facing slopes. This is

supported by the correlation of larger size at higher flow accumulation and northness, and is consistent with the findings of other researchers (Bauer 1936, Jones et al. 1993, Westman 1991). Yet, Q. pacifica

also reaches high cover at higher elevations and greater distances from the coast; this higher (400-600 m)

elevation zone may be particularly dry because it is above the fog layer, but not high enough to benefit from orographic precipitation (Miller 1979).

Abundance of juveniles in Q. pacifica plots was positively associated with intermediate canopy cover, low A-horizon NO3

-, and low flow length (nearer to the coast) and negatively correlated with number of

dead trees, but very little variance was explained. Dead trees were more abundant in stands with higher

A-horizon NO3-, but this correlation was weak. The associations with higher nutrients may be indirectly

linked with competition from invasive annual grasses, which thrive in high nutrient environments (Brooks 2003), and have been implicated in poor oak recruitment in California (Gordon et al. 1989, Danielson and

Halvorson 1991, Gordon & Rice 2000). Additionally, higher recruitment at intermediate canopy cover

levels has been found with blue oak (Quercus douglasii) saplings (Swiecki et al. 1997), and may reflect a trade-off between greater success in open areas (Stratton, this volume) and the presence of nearby

propagule sources.

The predictive map produced using a GLM based on stepwise variable selection covered only four

percent of the island; this is suspicious given that a recent, refined Quercus pacifica map (D. Knapp,

unpubl. data) shows it covering 23% of the island. Manual variable selection produced a closer, more

reasonable estimate (17%), therefore Qupapred1 should be used for management planning rather than Qupapred2.

Quercus tomentella models

Models of Quercus tomentella presence/absence had high discriminatory power (bootstrapped AUCs >

0.95). They indicate that this species tends to be present on igneous and sedimentary-derived substrates

with a gravelly and medium texture, at high values of TMI, intermediate elevations, slope angles and flow lengths (distance to coast) and low to intermediate levels of solar radiation. The preference of low-

radiation (north-facing) slopes corresponds well with the findings of other researchers (Jones et al. 1993;

Kindsvater 2006).

Q. tomentella tree density was highest at moderately high elevations (300-350 m), high values of winter

radiation, steeper slopes, and lower flow accumulation. However, explained variance was only 20-30% in the best (non-linear and non-parametric) models. All multiple predictor models for Q. tomentella

reproduction suggest that regeneration is highest on concave slopes, and at higher elevations. Model

results showing a trend for higher reproduction on gravelly, medium-textured soils correlates well with

results of Kindsvater (2006), who found a higher percent sand content in sites with Q. tomentella seedlings, along with higher pH, higher exchangeable potassium, and lower phosphorous contents.

Explained variance was 17-34% and was higher for nonlinear models. The strong spatial structure of the

oak populations on the island, with two metapopulations much larger in size and with greater reproduction than all others, has likely confounded these relationships, however.

Predicted locations for Q. tomentella were found to be currently dominated by coastal sage scrub, grassland, and Island chaparral, in descending order (Table 7). This is in contrast with the results of a

study on Santa Rosa Island, where the three habitat types most frequently overlapping with a predictive

91


GIS model of core Q. tomentella habitat were closed-cone pine woodland, grassland, and bare ground

(Kindsvater 2006). These differences may reflect differences in current vegetation cover, island geology and a higher prevalence of bare and degraded habitat due to overgrazing on Santa Rosa Island. This is a

relict species, more widely distributed in a cooler, wetter periods (Muller 1967). Its current distribution,

as well as that of the other vegetation types, is likely the complex result of changing climate, severe

overgrazing, impacts of introduced species, and soil loss.

Suggestions for future study

Q. pacifica dieback remains an unexplained phenomenon, although hypothesized factors include: reduced

moisture (exacerbated by groundwater reduction, and expressed in drier microtopographic areas); smog

drift from the mainland; a pathogen; or senescence (or any combination of these). If moisture relations are significant, presence/absence modeling of dieback areas should indicate this; such an analysis would be

helpful in elucidating potential causes and solutions.

Incorporating mapped temperature and precipitation variables, which have been found to be important in analyses of other chaparral species, would likely improve the models. The final weather stations were

installed in 2007, in an array that should be adequate to interpolate climate variables for the entire island

once sufficient data is collected. Models can then be developed using more direct or proximal bioclimatic variables instead of their topographic proxies, elevation and distance to the coast. Maps of feral and

managed animal disturbance intensity would also be useful, which could be produced using grid layers of

numbers of feral animals removed, as well as management history data for various fenced zones on the island.

It would be particularly informative to conduct an experimental outplanting using predicted locations for

Quercus tomentella, distributed among the coastal sage scrub, grassland, and Island chaparral vegetation types. Differential success among these sites would contribute to our understanding of the environmental

preferences of this species.

ACKNOWLEDGMENTS

We would like to thank the Seaver Institute for funding this work and supporting the environmental

variable generation, Jenny McCune and Lauren Danner, who tackled the dense oak plots (and Q. tomentella survey, by Jenny) with attention to detail and good humor. Mike Klinefelter was invaluable

for help in the environmental variable GIS-layer generation, and UCR geologists aided him with

generalization of the island‟s geology map. All participants in the workshop provided useful feedback on this project. Comments by Kathryn McEachern greatly improved the clarity of the manuscript.

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