- Email: [email protected]
A multivariate study of mangrove morphology (Rhizophora mangle) using both above and below-water plant architecture
Estuarine, Coastal and Shelf Science 65 (2005) 440e448 www.elsevier.com/locate/ECSS
A multivariate study of mangrove morphology (Rhizophora mangle) using both above and below-water plant architecture R. Allen Brooks a,*, Susan S. Bell b a
Florida Integrated Science Center, U.S. Geological Survey, 7920 NW 71st Street, Gainesville, FL 32653, USA Department of Biology, University of South Florida, 4202 E. Fowler Avenue, Tampa, FL 33620-5150, USA
b
Received 23 February 2004; accepted 22 June 2005 Available online 1 September 2005
Abstract A descriptive study of the architecture of the red mangrove, Rhizophora mangle L., habitat of Tampa Bay, FL, was conducted to assess if plant architecture could be used to discriminate overwash from fringing forest type. Seven above-water (e.g., tree height, diameter at breast height, and leaf area) and 10 below-water (e.g., root density, root complexity, and maximum root order) architectural features were measured in eight mangrove stands. A multivariate technique (discriminant analysis) was used to test the ability of different models comprising above-water, below-water, or whole tree architecture to classify forest type. Root architectural features appear to be better than classical forestry measurements at discriminating between fringing and overwash forests but, regardless of the features loaded into the model, misclassification rates were high as forest type was only correctly classified in 66% of the cases. Based upon habitat architecture, the results of this study do not support a sharp distinction between overwash and fringing red mangrove forests in Tampa Bay but rather indicate that the two are architecturally undistinguishable. Therefore, within this northern portion of the geographic range of red mangroves, a more appropriate classification system based upon architecture may be one in which overwash and fringing forest types are combined into a single, ‘‘tide dominated’’ category. Ó 2005 Elsevier Ltd. All rights reserved. Keywords: forest type; fringing; habitat architecture; habitat classification; overwash; prop root; red mangrove; Rhizophora
1. Introduction Many studies addressing habitat classification have been conducted in terrestrial systems using dominant vegetation cover as indicators of habitat type (e.g., Hix and Pearcy, 1997; Diaz et al., 1998). Habitat classification in marine and coastal environments has been wide ranging with attempts to erect categories over orders of magnitude from the meter scale up to tens of kilometers. Lugo and Snedaker (1974) devised the first classification
* Corresponding author. ENSR International, 9700 16th St. North, St. Petersburg, FL 33716, USA. E-mail address: [email protected] (R.A. Brooks). 0272-7714/$ - see front matter Ó 2005 Elsevier Ltd. All rights reserved. doi:10.1016/j.ecss.2005.06.019
of mangrove forests in South Florida based upon criteria similar to that of terrestrial systems. They proposed five major categories based upon the presence of specific taxa and general plant morphology: fringe, riverine, overwash island, basin, and dwarf communities (Table 1). This classification system, originally proposed for a carbonate setting, is widely used but has yet to be rigorously tested statistically. Rhizophora mangle, the red mangrove, is one of the most prominent features along the coastline and islands of South Florida (Gilmore and Snedaker, 1993). Rhizophora mangle has the highest importance value among the different mangrove species in Florida overwash, fringe, and dwarf forests (Pool et al., 1977) and studies attempting to distinguish among forest types therefore
R.A. Brooks, S.S. Bell / Estuarine, Coastal and Shelf Science 65 (2005) 440e448 Table 1 Description of the five forest types described by Lugo and Snedaker (1974) Forest type
Location
Hydrodynamics
Fringing
Daily tidal flushing
Riverine
Sloping shorelines: ecotone between intertidal and terrestrial communities Small Islands (or land extensions) Freshwater Inlets
Basin
Interior
Dwarf
Stunted Trees
Overwash
Completely flooded during most high tides Daily tidal influence and experience seasonal fluctuations in salinity Tidal flooding only during extreme high tides or storm events May experience, high salt stress, water logging, and nutrient limitation
often use them as a descriptor species. Differences in R. mangle between forest types have been evaluated almost exclusively using above-water measures such as tree density, height, biomass (Pool et al., 1977), canopy architecture (e.g., leaf size, branching order: Araujo et al., 1997; Feller and Mathis, 1997), and physiology (e.g., water use efficiency, gas exchange, and nutrient uptake: Lin and Sternberg, 1992a,b; Lin and Sternberg, 1993). However, evaluations based solely upon abovewater characteristics ignore the above ground root system. Rhizophora mangle produces above ground roots (i.e., aerial roots) which have a thick periderm similar to tree branches (Gill and Tomlinson, 1977) and once attached to the substrate produce multiple above ground lateral branches. The above ground root system is not only important for tree structural support, nutrient provision, and gas exchange (e.g., Gill and Tomlinson, 1975; Carlson et al., 1983; McKee et al., 1988), but also plays an integral role in the mangrove ecosystem, by creating habitats for the fauna and flora (e.g., Taylor et al., 1986; Thayer et al., 1987; Bingham, 1992; Acosta and Butler, 1997; Primavera, 1997; Ronnback et al., 1999; Nagelkerken et al., 2000). Therefore, any variation in root architecture which is attributable to forest type may result in accompanying variation in habitat function. Given the importance of mangrove prop roots, it is curious that studies have not focused on below-water characteristics when classifying mangrove habitat. It is quite possible that either below-water characteristics alone or when combined with above-water characteristics would allow for a better discrimination among forest types than that of forest measures alone. The purpose of this study was to use a multivariate approach to compare the above and below-water architecture of Rhizophora mangle between two categories of mangrove forest type. Two questions regarding the architectural plasticity of the red mangrove and
441
habitat classification were addressed: (1) Do the abovewater and/or below-water architecture differ between overwash and fringing forest types? and (2) Does the whole tree architecture (i.e., above and below-water architecture combined) differ between overwash and fringing forest types? 2. Methods 2.1. Study site Eight sites were selected for sampling within Tampa Bay, Florida (27 46#N, 82 37#W; Fig. 1). Tampa Bay has carbonate sediments and experiences mixed-tides (Brooks and Doyle, 1998). Only two, fringing and overwash, of the five forest types described in Lugo and Snedaker (1974) are present within Tampa Bay. Fringe and overwash forests were visually identified within each location except one site, Pinellas Point, where no overwash forest was found. Two overwash island forests and proximal fringing forest were selected within each site that met the criteria outlined by Lugo and Snedaker (1974). Five sampling points were established haphazardly along the seaward edge within each forest type. Each sampling point consisted of a single Rhizophora
Fig. 1. Location of eight study sites around Tampa Bay (UTB Z Upper Tampa Bay, 4th Z Fourth Street, WI Z Weedon Island, CRB Z Cockroach Bay, PP Z Pinellas Point, SKY Z Skyway, FD Z Fort Desoto, and AM Z Anna Maria Island).
442
R.A. Brooks, S.S. Bell / Estuarine, Coastal and Shelf Science 65 (2005) 440e448
mangle tree that was tagged with forestry tape and all sampling points were no closer than 10 m of each other. It is important to note that only mature trees that appeared to be undisturbed were included in this study. 2.2. Above-water architecture On each selected tree (i.e., sampling point), several standard forestry measurements were made following other mangrove studies which have evaluated abovewater tree architecture (e.g., Pool et al., 1977; Cintron and Novelli, 1984; Araujo et al., 1997; Feller and Mathis, 1997). Diameter at breast height, the number of primary branches arising from the bole, and the number of secondary branches arising from the lowest primary branch were recorded. Additionally, on each tree, 25 leaves were selected from the distal end of the lower primary branches. Leaf length, width, and surface area were then determined for each leaf in the laboratory using Sigma Scan/Image Analysis Version 1.20.09 (Erickson et al., 2004). 2.3. Below-water architecture A 1-m2 collapsible quadrat was used to determine the root density at each sampling point. The front edge of the quadrat was aligned parallel to the forest edge. The edge of the mangrove forest in this study was defined as the furthest extending prop root at the land/water ecotone. Within the quadrat the spatial location (x, y coordinates within the quadrat) and diameter of each root was recorded to obtain density and complexity estimates (see below). Additionally, free hanging aerial roots over the plot versus those attached to the substratum were identified as two different root types. On each tree designated as a sampling point, the number of primary prop (i.e., aerial roots originating from the bole or other roots) and drop (aerial roots originating from tree branches) roots was recorded. Three of these primary prop roots were then selected for further measurements. Roots were selected to cover all sides of the tree such that one was oriented parallel to the forest edge and on the right side of the bole, the second was oriented parallel to the forest edge but on the left side of the bole, and the third was oriented perpendicular to the forest edge originating from the front side of the bole. The number of root branching events, and both absolute number of roots and specific number of each type of root (i.e., attached versus unattached) at the terminal end of the root were recorded from these three specified primary prop roots. 2.4. Statistical analysis on root complexity The complexity of the root system was evaluated at two different scales. First complexity was determined at
the level of an individual prop root using the information taken from the primary roots and converting it into the maximum root order observed and the average number of roots which originated from a single primary root. Next, root complexity was also evaluated at the 1 m2 scale by determining root spatial arrangement (not biased by density), density, and total area of cover. Tessellation (e.g., Byers, 1992) was used to estimate prop root spatial arrangement. Input data for the analysis were the x, y location recorded within each quadrat as the point of insertion for all attached roots and a point representing a straight vertical drop for all free hanging aerial roots. Tessellation was performed using the Tessellation Analysis Program Ver 1.0.0 developed by the Department of Engineering Materials, University of Southampton. Tessellation involved dividing the sample quadrat into various sized cells such that each root (point) was placed within the centroid of a cell. Edge cells were excluded to avoid any bias. Tessellation was chosen as it retains the x, y coordinate structure and allows for analysis of different spatial relationships among cells (Madley, 1997; Perry et al., 2002). The following parameters were recorded for each quadrat: the average cell area, local area fraction, and the mean nearest neighbor distance. Additionally, the coefficient of variation was used in lieu of the mean quadrat value for each parameter as it is less biased by root density (Brooks, 2002). Although root density could be considered a component of complexity, the goal of this analysis was to evaluate the spatial arrangement of roots. Therefore, root density was entered into statistical analyses as a separate variable. Additionally, the total area of root coverage/m2 was calculated. Root diameters were used to estimate root areas within a two-dimensional plane (Bartholomew et al., 2000). The area of all roots within each 1 m2 quadrat was then summed. 2.5. Statistical analysis on mangrove architecture The objective of this study was to determine if fringing and overwash forest types exhibited detectable differences in architecture and, if so, what architectural characteristics provided the best discriminatory ability. Canonical discriminant analysis (CDA), a multivariate procedure, was used to discern any differences between forest types based upon the measured characteristics (i.e., 7 above and 10 below-water features). CDA computes canonical discriminant functions using class variables to explain the total variance of the data set. The CANDIS procedure in the SAS System for Windows (Release 8.01 e Windows Version 4.0.1111) was used. The specific categories that were assigned a priori as required for this analysis were fringe and overwash forest type. Only two categories (overwash versus
443
R.A. Brooks, S.S. Bell / Estuarine, Coastal and Shelf Science 65 (2005) 440e448
fringing) were entered into the analysis so only one canonical discriminant function (CDF) was derived. The class variables used for the analysis are listed in Table 2. Standard statistical transformations (i.e., log 10, square root, arcsine square root) were applied to all class variables which were not normally distributed. The number of primary drop roots was not entered into the analysis as normality could not be met using standard transformations. However, the number of primary drop roots was not significantly different between forest types (P Z 0.796, ManneWhitney Rank Sum Test). Total canonical structure values were used to indicate the correlation coefficient between individual variables and canonical scores (i.e., canonical loadings). This particular structure was selected as it is the least biased by multicolinearity (Momen and Zehr, 1998). Three multivariate analysis models were made: Model (1) above-water architecture (7 class variables), Model (2) below-water architecture (9 class variables e i.e., number of primary drop roots omitted), and Model (3) whole tree architecture (Models 1 and 2 combined) with 16 class variables. MANOVA test results were used to determine significant differences between forest types. The crossvalidation technique (Everitt and Der, 1996) was used to estimate the ability of the derived discriminant function to correctly classify forest type. Cross-validation, a preferred technique, involves deriving the discriminant function by leaving out one sample and then applying this classification rule to the sample that was omitted. Discriminant analysis using the DISCRIM procedure in SAS was used to estimate the discriminatory ability of the different models.
3. Results 3.1. Above-water architecture CDA results, for variables related to above-water architecture, indicated poor separation of overwash from fringing forest except for the Upper Tampa Bay (UTB) site (Fig. 2A). Cross-validation results indicated a 65% error rate in forest type classification (Table 3) and MANOVA results did not indicate a significant difference between forest types (Wilk’s l, P Z 0.82, Appendix I). Classification error was higher for overwash (91%) compared to the fringing forest type (38%). Univariate analysis indicated that none of the above-water architecture variables were significantly different between overwash and fringing forests (Table 2). The canonical loadings for the CDF axis are shown in Appendix I. 3.2. Below-water architecture A plot of the canonical discriminant function (CDF) using 9 below-water architecture variables (i.e., number of primary drop roots omitted) resulted in scores for overwash forest oriented in a positive direction along the axis (Fig. 2B). Maximum root order and the average number of terminal roots arising from a single prop root had the highest positive canonical loadings (Appendix I). Fringing forest was located in the negative direction along the axis with root complexity, based upon variation in nearest neighbor distance, and the ratio of unattached to attached roots having high canonical loadings. Although separation between forest types was found, the location of the forest scores along the axis
Table 2 Results of univariate analysis comparing overwash versus fringing forest for both above and below-water architecture features. c.v. Z Coefficient of variation Class variable (A) Above-water architecture Tree height (m) Diameter at breast height (cm) Leaf area (cm2) Leaf length (cm) Leaf width (cm) Number of primary branches Number of secondary branches (B) Below-water architecture Avg. number of roots originating from a primary root Maximum root branching order Number of primary drop roots Number of primary prop roots Root density (m2) Ratio of unattached to attached roots Root complexity: cell area (c.v.) Root complexity: local aspect ratio (c.v.) Root complexity: nearest neighbor distance (c.v.) Root complexity: total area of cover
Fringing forest mean (Cs.e.)
Overwash forest mean (Cs.e.)
Univariate F statistic F-value (P-value)
5.72 6.27 24.9 8.3 4.3 16.6 22.7
(0.35) (0.39) (0.68) (0.11) (0.44) (0.86) (1.67)
5.5 6.46 24.8 8.2 3.9 18.3 23.8
(0.33) (0.49) (0.79) (0.16) (0.07) (1.05) (1.99)
0.11 0.40 0.20 0.02 0.32 2.81 1.18
(0.74) (0.53) (0.66) (0.87) (0.57) (0.10) (0.28)
8.5 3.6 5.0 12.8 60.1 0.51 0.70 0.64 0.44 167.6
(0.63) (0.15) (1.10) (0.49) (2.78) (0.07) (0.03) (0.02) (0.01) (9.77)
10.9 4.6 4.4 13.6 62.5 0.42 0.71 0.66 0.43 171.5
(1.08) (0.21) (1.03) (0.58) (3.08) (0.07) (0.03) (0.02) (0.01) (9.32)
3.56 16.40 e 1.14 0.04 0.31 0.01 0.53 0.29 0.08
(0.06) (0.001) (0.29) (0.83) (0.58) (0.93) (0.47) (0.59) (0.78)
444
R.A. Brooks, S.S. Bell / Estuarine, Coastal and Shelf Science 65 (2005) 440e448
were significant (Wilk’s l, P Z 0.008, Appendix I) but the misclassification error rate was high at 38% (Table 3). Univariate analysis indicated that only 1 class variable, maximum root order, was significantly different between forest types (F-test, P Z 0.001, Table 2). 3.3. Whole tree architecture All 16 class variables (i.e., number of primary drop roots omitted) were entered into a CDA analysis and the resultant canonical discriminant function (CDF) indicated separation of overwash from fringing forest within each site (Fig. 2C) and overwash scores were oriented in a positive direction along the CDF. Maximum root order had the largest positive canonical loading (Appendix I). Leaf length had the largest negative canonical loading. Complete separation of forest types was found for UTB and SKY, however, the location of forest types along the CDF was different among sites as in the case of the below-water architecture analysis. MANOVA results were significant (Wilk’s l, P Z 0.02, Appendix I) but the misclassification error was 48% (Table 3). The model classified fringing forest (misclassification rate: 36%) better than overwash forest (misclassification rate: 61%).
4. Discussion
Fig. 2. Results of canonical discriminant analyses for each site using habitat type as a category. (A) Above-water architectural only, (B) below-water architectural features only, and (C) all architectural features combined.
was different between sites such that the fringing forest of one site was sometimes found in the same multidimensional space as overwash forest of another (Fig. 2B). Additionally, complete within-site separation of overwash from fringing forest type was only found for UTB and Skyway (SKY) sites (Fig. 2B). MANOVA results
Table 3 Results of the discrimination ability of different discriminant models using the cross-validation method Model
Misclassification Misclassification Overall rate of fringing rate of overwash misclassification forest forest rate
Whole tree 36% architecture Above-water 38% architecture Below-water 32% architecture
61%
48%
91%
65%
44%
38%
Above-water architecture has been consistently used to describe the morphological heterogeneity of Rhizophora mangle trees from different forest types (e.g., Araujo et al., 1997). Yet, in this study, above-water architectural features provided extremely poor discrimination of mangrove forest types. Overall, forest type was classified correctly only in 50% of all cases and above-water architectural features were especially poor at discriminating overwash forest. We suspect that some of the lack of discriminatory power may lie in the quantification of tree morphology. For example, the diameter of most forest tree species consistently decreases when measuring from the ground to the canopy (Brooks pers. obs.) allowing for easy comparison of diameter at breast height. However, it is not uncommon for the bole of R. mangle to inconsistently increase and decrease in size. Similarly, it is questionable if mangrove tree height can be compared directly. While most tree species living within terrestrial forests grow perpendicular to the ground, R. mangle may grow perpendicularly or horizontally with respect to the ground in our site (Brooks pers. obs.). Leaf morphology (i.e., area, length, and width) has been found to differ between dwarf and basin red mangroves (Araujo et al., 1997) and shows promise as an above-water feature that can be directly compared
R.A. Brooks, S.S. Bell / Estuarine, Coastal and Shelf Science 65 (2005) 440e448
among forest types. Results of this study, however, do not indicate any significant difference in the leaf morphology of fringing and overwash forest types using a per tree comparison. Differences in mangrove leaf area can be the result of varying environmental stress levels (e.g., salinity, pollutants, nutrient limitation) among forest types (Araujo et al., 1997). If this is true then the similarities in leaf morphology found in this study suggest that both forest types experience similar stressors in Tampa Bay. Interestingly, classification of habitat based upon root architectural features alone provided the best discrimination of forest type. The fringing forest type exhibited a larger proportion of unattached roots and greater root complexity (i.e., nearest neighbor distance) within the intertidal. On the level of a single prop root, complexity (i.e., maximum root order and the average number of terminal roots arising from a single prop root) was greater for overwash forests. Interestingly, although the complexity of a primary root was higher in the overwash forest type, it did not result in a significant increase in either root density or spatial complexity when viewed over the meter scale. The lack of a difference in root density may be related to the fact that no relationship existed between the number of primary prop roots a tree produced and the actual complexity (i.e., number of branching events) of the root. Alternatively, tree densities may differ such that multiple trees add to the number of roots/m2. Overall, discrimination of forest type even when using below-water features was still relatively poor. Of the 17 architectural characters measured in this study only one parameter, maximum root order, was significantly different between forest types. The biological significance of this is questionable, however, as fringing and overwash forest types differed by only one branching event. Therefore using maximum root order as a single variable to definitively distinguish between forest types does not appear to be practical. Additionally, classification based upon a single measure is not recommended as univariate measures can vary with differences in tree age, disturbance history (Araujo et al., 1997), and measurement technique (Clough et al., 1997). This study is the first to assess the robustness of the original classification scheme proposed by Lugo and Snedaker (1974) using multivariate statistics rather than general descriptors. Based on Rhizophora mangle architecture, the results of this study do not support a sharp distinction between fringing and overwash forest types. Instead, the results support that the two are actually architecturally indistinguishable. Our quantitative findings, based solely upon physical structure, suggest that the more appropriate classification system may be the one employed by Cintron et al. (1985) and Ewel et al. (1998) in which overwash and fringing forests
445
are combined into one category and referred to as ‘‘tide dominated’’ habitat. Architectural similarity of fringing and overwash mangroves may be unique to Tampa Bay as this geographic area represents an ecotone between mangroves at the most northerly portion of their geographic range and freeze tolerant intertidal salt marshes (Dawes, 1998). It is possible that large site-specific differences in tree morphology do not exist in Tampa Bay because of increased physiological costs (e.g., cold temperature stress) or nutrient limitations constraining mangrove phenotypic plasticity (Feller, 1995; Koch and Snedaker, 1997; DeWitt et al., 1998; Chen and Twilley, 1999; Feller and Whigham, 1999; Feller et al., 2002). Twilley (1995) suggests that due to regional scale differences in environmental settings the forest types created by Lugo and Snedaker (1974) are actually ‘‘microtopographic’’ categories and only comparable on a local scale. At the UTB and SKY sites, it was possible to consistently discriminate overwash from fringing forests using architectural features. Therefore, it may be possible to distinguish between forest types within some sites but extrapolating a model across sites even within Tampa Bay appears problematic because of the high degree of structural heterogeneity among mangrove stands (Brooks, 2002). In Florida, the wood boring isopod, Sphaeroma terebrans Bate, exploits the intertidal habitat created by prop roots in intertidal red mangrove habitat (Rehm and Humm, 1973; Estevez, 1978). Sphaeroma terebrans is found almost exclusively within free hanging aerial roots of Rhizophora mangle (Estevez, 1978) and is present only within the intertidal zone. Isopod attack impacts the mangrove tree directly through root architectural changes (Simberloff et al., 1978; Ribi, 1981), reduced root production, increased root atrophy (Perry, 1988; Perry and Brusca, 1989; Ellison and Farnsworth, 1990), and root repair (Brooks and Bell, 2002). Therefore, if S. terebrans utilized the two forest types differently in Tampa Bay it might result in root architectural differences. So we asked the question of whether the patterns of root architecture found in this study might be linked to different levels of isopod attack between forest types. Habitat utilization by S. terebrans at the study sites, however, did not significantly differ between the two habitat types. Within a study site, isopod density and demographic traits were similar and insufficient to account for differences in root order characteristics between fringing and overwash mangroves (Brooks, 2002). The lack of discrimination by S. terebrans between the two habitat types suggests similar food resources and physiological conditions. Categorization of mangrove habitats may benefit from examination over a larger spatial scale or utilization of a landscape perspective (Dunning et al., 1992; Forman, 1995). Specifically, mangrove habitat is
446
R.A. Brooks, S.S. Bell / Estuarine, Coastal and Shelf Science 65 (2005) 440e448
often found in close proximity to a wide variety of other estuarine habitats (e.g., seagrass beds, oyster reefs, salt marsh) and the position of mangrove stands within the seascape affects habitat use by nekton (Rooker and Dennis, 1991; Laegdsgaard and Johnson, 1995; Ley et al., 1999; Thollot et al., 1999; Nagelkerken et al., 2000), fouling organisms (Rodriguez and Stoner, 1990; Bingham, 1992), and juvenile spiny lobsters (Acosta and Butler, 1997). Similarly, the size, shape, connectivity, and orientation to tidal flow of a mangrove stand may also be important if it impacts the encounter rate of nekton (Nagelkerken et al., 2000), or dispersing larvae (Bingham, 1992), the accessibility of predators, and/or the interception of floating propagules. Thus, classification of mangrove habitat for management purposes within coastal systems such as Tampa Bay might be more appropriately viewed using a conservation perspective that assesses habitat at a landscape-scale level rather than at the scale of individual trees. In summary, the purpose of this study was to evaluate if tree architecture could be used to discriminate fringing versus overwash mangrove forest types. The results of this study suggest that there is no sharp distinction between these two categories and they are architecturally indistinguishable within Tampa Bay. Root architectural features appear to be better than classical forestry measurements for distinguishing between the two forest types. However, in most cases forest type was only correctly classified two out of three times. Thus, our results using root structure as well as more traditional tree features support the suggestion by Gilmore and Snedaker (1993) and classification system used by Cintron et al. (1985) and Ewel et al. (1998) in which overwash and fringing forest types are combined into the one category defined as ‘‘tide dominated’’.
Results of a canonical discriminant analysis model using above-water architectural features Class variables
Total canonical structure
Tree height (m) Diameter at breast height (cm) Leaf area (cm2) Leaf length (cm) Leaf width (cm) Number of primary branches Number of secondary branches
ÿ0.11 0.16 ÿ0.05 ÿ0.37 0.16 0.68 0.30
Results of a canonical discriminant analysis model using below-water architectural features Class variables Avg. number of roots originating from a primary root Maximum root branching order Number of primary drop roots Number of primary prop roots Root density (m2) Ratio of unattached to attached roots Root complexity: cell area (c.v.) Root complexity: local aspect ratio (c.v.) Root complexity: nearest neighbor distance (c.v.) Root complexity: total area of cover
A. Ellison, E. Estevez, and two anonymous reviewers made many helpful comments on earlier drafts of the manuscript. This work was supported in part by an Aylesworth Fellowship, Tharpe Fellowship, and Old Salt Fishing Organization Fellowship to R.A. Brooks.
Appendix I MANOVA results examining the separation of overwash from fringing forest types Test
df
Wilk’s l
F
P
Above-water architecture Below-water architecture Whole-tree architecture
7,75 9,68 16,58
0.954 0.731 0.638
0.513 2.779 2.057
0.822 0.008 0.024
0.40 0.78 e 0.20 ÿ0.01 ÿ0.09 0.05 0.16 ÿ0.11 ÿ0.03
Results of a canonical discriminant analysis model combining both above and below-water architectural features Class variables
Acknowledgements
Total canonical structure
(A) Above-water architecture Tree height (m) Diameter at breast height (cm) Leaf area (cm2) Leaf length (cm) Leaf width (cm) Number of primary branches Number of secondary branches (B) Below-water architecture Avg. number of roots originating from a primary root Maximum root branching order Number of primary drop roots Number of primary prop roots Root density (m2) Ratio of unattached to attached roots Root complexity: cell area (c.v.) Root complexity: local aspect ratio (c.v.) Root complexity: nearest neighbor distance (c.v.) Root complexity: total area of cover
Total canonical structure 0.06 0.12 0.09 ÿ0.03 0.11 0.32 0.21 0.36 0.71 e 0.21 0.04 ÿ0.11 0.02 0.14 ÿ0.10 0.05
R.A. Brooks, S.S. Bell / Estuarine, Coastal and Shelf Science 65 (2005) 440e448
References Acosta, C.A., Butler, M.J., 1997. Role of mangrove habitat as a nursery for juvenile spiny lobster, Panulirus argus, in Belize. Marine & Freshwater Research 48, 721e727. Araujo, R.J., Jaramillo, J.C., Snedaker, C., 1997. LAI and leaf size differences in two red mangrove forest types in South Florida. Bulletin of Marine Science 60, 643e647. Bartholomew, A., Diaz, R.J., Cicchetti, G., 2000. New dimensionless indices of structural habitat complexity: predicted and actual effects on a predator’s foraging success. Marine Ecology Progress Series 206, 45e59. Bingham, B.L., 1992. Life histories in an epifaunal community: coupling of adult and larval processes. Ecology 73, 2244e2259. Brooks, G.R., Doyle, L.J., 1998. Recent sedimentary development of Tampa Bay, Florida: a microtidal estuary incised into tertiary platform carbonates. Estuaries 21, 391e406. Brooks, R.A., 2002. Plant-Animal interaction within the red mangroves, Rhizophora mangle L., of Tampa Bay: Mangrove habitat classification and isopod, Sphaeroma terebrans Bate, colonization of a dynamic root substratum. PhD dissertation. University of South Florida, Tampa, Florida, USA. Brooks, R.A., Bell, S.S., 2002. Mangrove response to attack by a root boring isopod: root repair versus architectural modification. Marine Ecology Progress Series 231, 85e91. Byers, J.A., 1992. Dirichlet tessellation of bark beetle spatial attack points. Journal of Animal Ecology 61, 759e768. Carlson, P.R., Yarbro, L.A., Zimmerman, C.F., Montgomery, J.R., 1983. Pore water chemistry of an overwash mangrove island. Florida Scientist 46, 239e249. Chen, R., Twilley, R.R., 1999. Patterns of mangrove forest structure and soil nutrient dynamics along the Shark River Estuary, Florida. Estuaries 22, 955e970. Cintron, G., Lugo, A.E., Martinez, R., 1985. Structural and functional properties of mangrove forests. In: D’Arcy, W.G., Mireya, D.C.A. (Eds.), The Botany and Natural History of Panama. Missouri Botanical Garden, St. Louis, Missouri. Cintron, G.A., Novelli, Y.S., 1984. Methods for studying mangrove structure. In: Snedaker, S.C., Snedaker, J.G. (Eds.), The Mangrove Ecosystem: Research Methods. UNESCO, Paris, pp. 91e113. Clough, B.F., Dixon, P., Dalhaus, O., 1997. Allometric relationships for estimating biomass in multi-stemmed mangrove trees. Australian Journal of Botany 45, 1023e1031. Dawes, C.J., 1998. Marine Botany. John Wiley & Sons Incorporated, New York, USA. DeWitt, T.J., Sih, A., Wilson, D.S., 1998. Costs and limits of phenotypic plasticity. Trends in Ecology & Evolution 13, 77e81. Diaz, M., Carbonell, R., Santos, T., Telleria, J.L., 1998. Breeding bird communities in pine plantations of the Spanish plateaux: biogeography, landscape and vegetation effects. Journal of Applied Ecology 35, 562e574. Dunning, J.B., Danielson, B.J., Pulliam, H.R., 1992. Ecological processes that affect populations in complex landscapes. Oikos 65, 169e175. Ellison, A.M., Farnsworth, E.J., 1990. The ecology of Belizean mangrove-root fouling communities: I. Epibenthic fauna are barriers to isopod attack of red mangrove roots. Journal of Experimental Marine Biology & Ecology 142, 91e104. Erickson, A.A., Bell, S.S., Dawes, C.J., 2004. Does mangrove leaf chemistry help explain crab herbivory patterns? Biotropica 36, 333e343. Estevez, E.D., 1978. Ecology of Sphaeroma terebrans Bate, a wood boring isopod, in A Florida mangrove forest. Dissertation. University of South Florida, Tampa, Florida, USA. Everitt, B.S., Der, G., 1996. A Handbook of Statistical Analyses Using SAS. Chapman & Hall, London, UK.
447
Ewel, K.C., Twilley, R.R., Ong, J.E., 1998. Different kinds of mangrove forests provide different goods and services. Global Ecology & Biogeographical Letters 7, 83e94. Feller, I.C., 1995. Effects of nutrient enrichment on growth and herbivory of dwarf red mangrove (Rhizophora mangle). Ecological Monographs 65, 477e505. Feller, I.C., Mathis, W.N., 1997. Primary herbivory by wood-boring insects along an architectural gradient of Rhizophora mangle. Biotropica 29, 440e451. Feller, I.C., Whigham, D.F., 1999. Effects of nutrient enrichment on within-stand cycling in a mangrove forest. Ecology 80, 2193e2205. Feller, I.C., McKee, K.L., Whigham, D.F., O’Neill, J.P., 2002. Nitrogen vs. phosphorus limitation across an ecotonal gradient in a mangrove forest. Biogeochemistry 62, 145e175. Forman, R.T.T., 1995. Some general principles of landscape and regional ecology. Landscape Ecology 10, 133e142. Gill, A.M., Tomlinson, P.B., 1975. Aerial roots: an array of forms and functions. In: Torrey, J.G., Clarkson, D.T. (Eds.), The Development and Function of Roots. Academic Press, New York, USA, pp. 237e260. Gill, A.M., Tomlinson, P.B., 1977. Studies on the growth of Red Mangrove (Rhizophora mangle L.) 4. The adult root system. Biotropica 9, 145e155. Gilmore Jr., R.G., Snedaker, S.C., 1993. Chapter 5: mangrove forests. In: Martin, W.H., Boyce, S.G., Echternacht, E.C. (Eds.), Biodiversity of the Southeastern United States: Lowland Terrestrial Communities. John Wiley and Sons, Inc. Publishers, New York, NY, p. 502. Hix, D.M., Pearcy, J.N., 1997. Forest ecosystems of the Marietta Unit, Wayne National Forest, southeastern Ohio: multifactor classification and analysis. Canadian Journal of Forestry Research 27, 1117e1131. Koch, M.S., Snedaker, S.C., 1997. Factors influencing Rhizophora mangle L. seedling development in Everglades carbonate soils. Aquatic Botany 59, 87e98. Laegdsgaard, P., Johnson, C.R., 1995. Mangrove habitats as nurseries e unique assemblages of juvenile fish in subtropical mangroves in Eastern Australia. Marine Ecology Progress Series 126, 67e81. Ley, J.A., McIvor, C.C., Montague, C.L., 1999. Fishes in mangrove prop-root habitats of Northeastern Florida Bay: distinct assemblages across an estuarine gradient. Estuarine, Coastal, & Shelf Science 48, 701e723. Lin, G.H., Sternberg, L.D.L., 1992a. Differences in morphology, carbon isotope ratios, and photosynthesis between scrub and fringe mangroves in Florida, USA. Aquatic Botany 42, 303e313. Lin, G.H., Sternberg, L.D.L., 1992b. Comparative study of water uptake and photosynthetic gas exchange between scrub and fringe red mangroves, Rhizophora mangle L. Oecologia (Berlin) 90, 399e 403. Lin, G.H., Sternberg, L.D.L., 1993. Effects of salinity fluctuation on photosynthetic gas exchange and plant growth of the red mangrove (Rhizophora mangle L.). Journal of Experimental Botany 44, 9e16. Lugo, A.E., Snedaker, S.C., 1974. The ecology of mangroves. Annual Review of Ecology & Systematics 5, 39e64. Madley, K.A., 1997. Factors affecting the movement of drifting macroalgae (Gracilaria verrucosa) in seagrass (Halodule wrightii) beds. Masters. University of South Florida, Tampa, Florida, USA. McKee, K.L., Mendelssohn, I.A., Hester, M.H., 1988. Reexamination of pore water sulfide concentrations and redox potentials near the aerial roots of Rhizophora mangle and Avicennia germinans. American Journal of Botany 75, 1352e1359. Momen, B., Zehr, J.P., 1998. Watershed classification by discriminant analyses of lakewater-chemistry and terrestrial characteristics. Ecological Applications 8, 497e507. Nagelkerken, I., Dorenbosch, M., Verberk, W.C.E.P., Cocheret de la Moriniere, E., van der Velde, G., 2000. Importance of shallowwater biotopes of a Caribbean bay for juvenile coral reef
448
R.A. Brooks, S.S. Bell / Estuarine, Coastal and Shelf Science 65 (2005) 440e448
fishes: patterns in biotope association, community structure and spatial distribution. Marine Ecology Progress Series 202, 175e192. Perry, D.M., 1988. Effects of associated fauna on growth and productivity in the Red Mangrove. Ecology 69, 1064e1075. Perry, D.M., Brusca, R.C., 1989. Effects of the root-boring isopod Sphaeroma peruvianum on red mangrove forests. Marine Ecology Progress Series 57, 287e292. Perry, J.N., Liebhold, A.M., Rosenberg, S., Dungan, J., Miriti, M., Jakomulska, A., Citron-Pousty, S., 2002. Illustrations and guidelines for selecting statistical methods for quantifying spatial pattern in ecological data. Ecography 25, 578e600. Pool, D.J., Snedaker, S.C., Lugo, A.E., 1977. Structure of mangrove forests in Florida, Puerto-Rico, Mexico, and Costa-Rica. Biotropica 9, 205e212. Primavera, J.H., 1997. Fish predation on mangrove-associated penaeids the role of structures and substrate. Journal of Experimental Marine Biology & Ecology 215, 205e216. Rehm, A., Humm, J., 1973. Sphaeroma terebrans: a threat to the mangroves of southwestern Florida. Science 182, 173e174. Ribi, G., 1981. Does the wood-boring isopod Sphaeroma terebrans benefit mangroves Rhizophora mangle. Bulletin of Marine Science 31, 295e928. Rodriguez, C., Stoner, A.W., 1990. The epiphyte community of mangrove roots in a tropical estuary: distribution and biomass. Aquatic Botany 36, 117e126.
Ronnback, P., Troell, M., Kautsky, N., Primavera, J.H., 1999. Distribution pattern of shrimps and fish among Avicennia and Rhizophora microhabitats in the Pagbilao mangroves, Philippines. Estuarine, Coastal, & Shelf Science 48, 223e234. Rooker, J.R., Dennis, G.D., 1991. Diel, lunar and seasonal changes in a mangrove fish assemblage off southwestern Puerto Rico. Bulletin of Marine Science 9, 684e698. Simberloff, D., Brown, B.J., Lowrie, S., 1978. Isopod and insect root borers may benefit Florida mangroves. Science 201, 630e632. Taylor, P.R., Littler, M.M., Littler, D.S., 1986. Escapes from herbivory in relation to the structure of mangrove island macroalgal communities. Oecologia 69, 481e490. Thollot, P., Kulbicki, M., Harmelin-Vivien, M., 1999. Trophic analysis and food webs of mangrove fish assemblages from New Caledonia. Comptes Rendus de l’Academie des Sciences-Series III-Science de la Vie 322, 607e619. Thayer, G.W., Colby, D.R., Hettler, W.F., 1987. Utilization of the red mangrove prop root habitat by fishes in south Florida. Marine Ecology Progress Series 35, 25e38. Twilley, R.R., 1995. Properties of mangrove ecosystems related to the energy signature of coastal environments. In: Hall, A.S. (Ed.), Maximum Power The Ideas and Applications of H.T. Odum. University Press of Colorado, Colorado, USA, pp. 43e62.
A multivariate study of mangrove morphology (Rhizophora mangle) using both above and below-water plant architecture R. Allen Brooks a,*, Susan S. Bell b a
Florida Integrated Science Center, U.S. Geological Survey, 7920 NW 71st Street, Gainesville, FL 32653, USA Department of Biology, University of South Florida, 4202 E. Fowler Avenue, Tampa, FL 33620-5150, USA
b
Received 23 February 2004; accepted 22 June 2005 Available online 1 September 2005
Abstract A descriptive study of the architecture of the red mangrove, Rhizophora mangle L., habitat of Tampa Bay, FL, was conducted to assess if plant architecture could be used to discriminate overwash from fringing forest type. Seven above-water (e.g., tree height, diameter at breast height, and leaf area) and 10 below-water (e.g., root density, root complexity, and maximum root order) architectural features were measured in eight mangrove stands. A multivariate technique (discriminant analysis) was used to test the ability of different models comprising above-water, below-water, or whole tree architecture to classify forest type. Root architectural features appear to be better than classical forestry measurements at discriminating between fringing and overwash forests but, regardless of the features loaded into the model, misclassification rates were high as forest type was only correctly classified in 66% of the cases. Based upon habitat architecture, the results of this study do not support a sharp distinction between overwash and fringing red mangrove forests in Tampa Bay but rather indicate that the two are architecturally undistinguishable. Therefore, within this northern portion of the geographic range of red mangroves, a more appropriate classification system based upon architecture may be one in which overwash and fringing forest types are combined into a single, ‘‘tide dominated’’ category. Ó 2005 Elsevier Ltd. All rights reserved. Keywords: forest type; fringing; habitat architecture; habitat classification; overwash; prop root; red mangrove; Rhizophora
1. Introduction Many studies addressing habitat classification have been conducted in terrestrial systems using dominant vegetation cover as indicators of habitat type (e.g., Hix and Pearcy, 1997; Diaz et al., 1998). Habitat classification in marine and coastal environments has been wide ranging with attempts to erect categories over orders of magnitude from the meter scale up to tens of kilometers. Lugo and Snedaker (1974) devised the first classification
* Corresponding author. ENSR International, 9700 16th St. North, St. Petersburg, FL 33716, USA. E-mail address: [email protected] (R.A. Brooks). 0272-7714/$ - see front matter Ó 2005 Elsevier Ltd. All rights reserved. doi:10.1016/j.ecss.2005.06.019
of mangrove forests in South Florida based upon criteria similar to that of terrestrial systems. They proposed five major categories based upon the presence of specific taxa and general plant morphology: fringe, riverine, overwash island, basin, and dwarf communities (Table 1). This classification system, originally proposed for a carbonate setting, is widely used but has yet to be rigorously tested statistically. Rhizophora mangle, the red mangrove, is one of the most prominent features along the coastline and islands of South Florida (Gilmore and Snedaker, 1993). Rhizophora mangle has the highest importance value among the different mangrove species in Florida overwash, fringe, and dwarf forests (Pool et al., 1977) and studies attempting to distinguish among forest types therefore
R.A. Brooks, S.S. Bell / Estuarine, Coastal and Shelf Science 65 (2005) 440e448 Table 1 Description of the five forest types described by Lugo and Snedaker (1974) Forest type
Location
Hydrodynamics
Fringing
Daily tidal flushing
Riverine
Sloping shorelines: ecotone between intertidal and terrestrial communities Small Islands (or land extensions) Freshwater Inlets
Basin
Interior
Dwarf
Stunted Trees
Overwash
Completely flooded during most high tides Daily tidal influence and experience seasonal fluctuations in salinity Tidal flooding only during extreme high tides or storm events May experience, high salt stress, water logging, and nutrient limitation
often use them as a descriptor species. Differences in R. mangle between forest types have been evaluated almost exclusively using above-water measures such as tree density, height, biomass (Pool et al., 1977), canopy architecture (e.g., leaf size, branching order: Araujo et al., 1997; Feller and Mathis, 1997), and physiology (e.g., water use efficiency, gas exchange, and nutrient uptake: Lin and Sternberg, 1992a,b; Lin and Sternberg, 1993). However, evaluations based solely upon abovewater characteristics ignore the above ground root system. Rhizophora mangle produces above ground roots (i.e., aerial roots) which have a thick periderm similar to tree branches (Gill and Tomlinson, 1977) and once attached to the substrate produce multiple above ground lateral branches. The above ground root system is not only important for tree structural support, nutrient provision, and gas exchange (e.g., Gill and Tomlinson, 1975; Carlson et al., 1983; McKee et al., 1988), but also plays an integral role in the mangrove ecosystem, by creating habitats for the fauna and flora (e.g., Taylor et al., 1986; Thayer et al., 1987; Bingham, 1992; Acosta and Butler, 1997; Primavera, 1997; Ronnback et al., 1999; Nagelkerken et al., 2000). Therefore, any variation in root architecture which is attributable to forest type may result in accompanying variation in habitat function. Given the importance of mangrove prop roots, it is curious that studies have not focused on below-water characteristics when classifying mangrove habitat. It is quite possible that either below-water characteristics alone or when combined with above-water characteristics would allow for a better discrimination among forest types than that of forest measures alone. The purpose of this study was to use a multivariate approach to compare the above and below-water architecture of Rhizophora mangle between two categories of mangrove forest type. Two questions regarding the architectural plasticity of the red mangrove and
441
habitat classification were addressed: (1) Do the abovewater and/or below-water architecture differ between overwash and fringing forest types? and (2) Does the whole tree architecture (i.e., above and below-water architecture combined) differ between overwash and fringing forest types? 2. Methods 2.1. Study site Eight sites were selected for sampling within Tampa Bay, Florida (27 46#N, 82 37#W; Fig. 1). Tampa Bay has carbonate sediments and experiences mixed-tides (Brooks and Doyle, 1998). Only two, fringing and overwash, of the five forest types described in Lugo and Snedaker (1974) are present within Tampa Bay. Fringe and overwash forests were visually identified within each location except one site, Pinellas Point, where no overwash forest was found. Two overwash island forests and proximal fringing forest were selected within each site that met the criteria outlined by Lugo and Snedaker (1974). Five sampling points were established haphazardly along the seaward edge within each forest type. Each sampling point consisted of a single Rhizophora
Fig. 1. Location of eight study sites around Tampa Bay (UTB Z Upper Tampa Bay, 4th Z Fourth Street, WI Z Weedon Island, CRB Z Cockroach Bay, PP Z Pinellas Point, SKY Z Skyway, FD Z Fort Desoto, and AM Z Anna Maria Island).
442
R.A. Brooks, S.S. Bell / Estuarine, Coastal and Shelf Science 65 (2005) 440e448
mangle tree that was tagged with forestry tape and all sampling points were no closer than 10 m of each other. It is important to note that only mature trees that appeared to be undisturbed were included in this study. 2.2. Above-water architecture On each selected tree (i.e., sampling point), several standard forestry measurements were made following other mangrove studies which have evaluated abovewater tree architecture (e.g., Pool et al., 1977; Cintron and Novelli, 1984; Araujo et al., 1997; Feller and Mathis, 1997). Diameter at breast height, the number of primary branches arising from the bole, and the number of secondary branches arising from the lowest primary branch were recorded. Additionally, on each tree, 25 leaves were selected from the distal end of the lower primary branches. Leaf length, width, and surface area were then determined for each leaf in the laboratory using Sigma Scan/Image Analysis Version 1.20.09 (Erickson et al., 2004). 2.3. Below-water architecture A 1-m2 collapsible quadrat was used to determine the root density at each sampling point. The front edge of the quadrat was aligned parallel to the forest edge. The edge of the mangrove forest in this study was defined as the furthest extending prop root at the land/water ecotone. Within the quadrat the spatial location (x, y coordinates within the quadrat) and diameter of each root was recorded to obtain density and complexity estimates (see below). Additionally, free hanging aerial roots over the plot versus those attached to the substratum were identified as two different root types. On each tree designated as a sampling point, the number of primary prop (i.e., aerial roots originating from the bole or other roots) and drop (aerial roots originating from tree branches) roots was recorded. Three of these primary prop roots were then selected for further measurements. Roots were selected to cover all sides of the tree such that one was oriented parallel to the forest edge and on the right side of the bole, the second was oriented parallel to the forest edge but on the left side of the bole, and the third was oriented perpendicular to the forest edge originating from the front side of the bole. The number of root branching events, and both absolute number of roots and specific number of each type of root (i.e., attached versus unattached) at the terminal end of the root were recorded from these three specified primary prop roots. 2.4. Statistical analysis on root complexity The complexity of the root system was evaluated at two different scales. First complexity was determined at
the level of an individual prop root using the information taken from the primary roots and converting it into the maximum root order observed and the average number of roots which originated from a single primary root. Next, root complexity was also evaluated at the 1 m2 scale by determining root spatial arrangement (not biased by density), density, and total area of cover. Tessellation (e.g., Byers, 1992) was used to estimate prop root spatial arrangement. Input data for the analysis were the x, y location recorded within each quadrat as the point of insertion for all attached roots and a point representing a straight vertical drop for all free hanging aerial roots. Tessellation was performed using the Tessellation Analysis Program Ver 1.0.0 developed by the Department of Engineering Materials, University of Southampton. Tessellation involved dividing the sample quadrat into various sized cells such that each root (point) was placed within the centroid of a cell. Edge cells were excluded to avoid any bias. Tessellation was chosen as it retains the x, y coordinate structure and allows for analysis of different spatial relationships among cells (Madley, 1997; Perry et al., 2002). The following parameters were recorded for each quadrat: the average cell area, local area fraction, and the mean nearest neighbor distance. Additionally, the coefficient of variation was used in lieu of the mean quadrat value for each parameter as it is less biased by root density (Brooks, 2002). Although root density could be considered a component of complexity, the goal of this analysis was to evaluate the spatial arrangement of roots. Therefore, root density was entered into statistical analyses as a separate variable. Additionally, the total area of root coverage/m2 was calculated. Root diameters were used to estimate root areas within a two-dimensional plane (Bartholomew et al., 2000). The area of all roots within each 1 m2 quadrat was then summed. 2.5. Statistical analysis on mangrove architecture The objective of this study was to determine if fringing and overwash forest types exhibited detectable differences in architecture and, if so, what architectural characteristics provided the best discriminatory ability. Canonical discriminant analysis (CDA), a multivariate procedure, was used to discern any differences between forest types based upon the measured characteristics (i.e., 7 above and 10 below-water features). CDA computes canonical discriminant functions using class variables to explain the total variance of the data set. The CANDIS procedure in the SAS System for Windows (Release 8.01 e Windows Version 4.0.1111) was used. The specific categories that were assigned a priori as required for this analysis were fringe and overwash forest type. Only two categories (overwash versus
443
R.A. Brooks, S.S. Bell / Estuarine, Coastal and Shelf Science 65 (2005) 440e448
fringing) were entered into the analysis so only one canonical discriminant function (CDF) was derived. The class variables used for the analysis are listed in Table 2. Standard statistical transformations (i.e., log 10, square root, arcsine square root) were applied to all class variables which were not normally distributed. The number of primary drop roots was not entered into the analysis as normality could not be met using standard transformations. However, the number of primary drop roots was not significantly different between forest types (P Z 0.796, ManneWhitney Rank Sum Test). Total canonical structure values were used to indicate the correlation coefficient between individual variables and canonical scores (i.e., canonical loadings). This particular structure was selected as it is the least biased by multicolinearity (Momen and Zehr, 1998). Three multivariate analysis models were made: Model (1) above-water architecture (7 class variables), Model (2) below-water architecture (9 class variables e i.e., number of primary drop roots omitted), and Model (3) whole tree architecture (Models 1 and 2 combined) with 16 class variables. MANOVA test results were used to determine significant differences between forest types. The crossvalidation technique (Everitt and Der, 1996) was used to estimate the ability of the derived discriminant function to correctly classify forest type. Cross-validation, a preferred technique, involves deriving the discriminant function by leaving out one sample and then applying this classification rule to the sample that was omitted. Discriminant analysis using the DISCRIM procedure in SAS was used to estimate the discriminatory ability of the different models.
3. Results 3.1. Above-water architecture CDA results, for variables related to above-water architecture, indicated poor separation of overwash from fringing forest except for the Upper Tampa Bay (UTB) site (Fig. 2A). Cross-validation results indicated a 65% error rate in forest type classification (Table 3) and MANOVA results did not indicate a significant difference between forest types (Wilk’s l, P Z 0.82, Appendix I). Classification error was higher for overwash (91%) compared to the fringing forest type (38%). Univariate analysis indicated that none of the above-water architecture variables were significantly different between overwash and fringing forests (Table 2). The canonical loadings for the CDF axis are shown in Appendix I. 3.2. Below-water architecture A plot of the canonical discriminant function (CDF) using 9 below-water architecture variables (i.e., number of primary drop roots omitted) resulted in scores for overwash forest oriented in a positive direction along the axis (Fig. 2B). Maximum root order and the average number of terminal roots arising from a single prop root had the highest positive canonical loadings (Appendix I). Fringing forest was located in the negative direction along the axis with root complexity, based upon variation in nearest neighbor distance, and the ratio of unattached to attached roots having high canonical loadings. Although separation between forest types was found, the location of the forest scores along the axis
Table 2 Results of univariate analysis comparing overwash versus fringing forest for both above and below-water architecture features. c.v. Z Coefficient of variation Class variable (A) Above-water architecture Tree height (m) Diameter at breast height (cm) Leaf area (cm2) Leaf length (cm) Leaf width (cm) Number of primary branches Number of secondary branches (B) Below-water architecture Avg. number of roots originating from a primary root Maximum root branching order Number of primary drop roots Number of primary prop roots Root density (m2) Ratio of unattached to attached roots Root complexity: cell area (c.v.) Root complexity: local aspect ratio (c.v.) Root complexity: nearest neighbor distance (c.v.) Root complexity: total area of cover
Fringing forest mean (Cs.e.)
Overwash forest mean (Cs.e.)
Univariate F statistic F-value (P-value)
5.72 6.27 24.9 8.3 4.3 16.6 22.7
(0.35) (0.39) (0.68) (0.11) (0.44) (0.86) (1.67)
5.5 6.46 24.8 8.2 3.9 18.3 23.8
(0.33) (0.49) (0.79) (0.16) (0.07) (1.05) (1.99)
0.11 0.40 0.20 0.02 0.32 2.81 1.18
(0.74) (0.53) (0.66) (0.87) (0.57) (0.10) (0.28)
8.5 3.6 5.0 12.8 60.1 0.51 0.70 0.64 0.44 167.6
(0.63) (0.15) (1.10) (0.49) (2.78) (0.07) (0.03) (0.02) (0.01) (9.77)
10.9 4.6 4.4 13.6 62.5 0.42 0.71 0.66 0.43 171.5
(1.08) (0.21) (1.03) (0.58) (3.08) (0.07) (0.03) (0.02) (0.01) (9.32)
3.56 16.40 e 1.14 0.04 0.31 0.01 0.53 0.29 0.08
(0.06) (0.001) (0.29) (0.83) (0.58) (0.93) (0.47) (0.59) (0.78)
444
R.A. Brooks, S.S. Bell / Estuarine, Coastal and Shelf Science 65 (2005) 440e448
were significant (Wilk’s l, P Z 0.008, Appendix I) but the misclassification error rate was high at 38% (Table 3). Univariate analysis indicated that only 1 class variable, maximum root order, was significantly different between forest types (F-test, P Z 0.001, Table 2). 3.3. Whole tree architecture All 16 class variables (i.e., number of primary drop roots omitted) were entered into a CDA analysis and the resultant canonical discriminant function (CDF) indicated separation of overwash from fringing forest within each site (Fig. 2C) and overwash scores were oriented in a positive direction along the CDF. Maximum root order had the largest positive canonical loading (Appendix I). Leaf length had the largest negative canonical loading. Complete separation of forest types was found for UTB and SKY, however, the location of forest types along the CDF was different among sites as in the case of the below-water architecture analysis. MANOVA results were significant (Wilk’s l, P Z 0.02, Appendix I) but the misclassification error was 48% (Table 3). The model classified fringing forest (misclassification rate: 36%) better than overwash forest (misclassification rate: 61%).
4. Discussion
Fig. 2. Results of canonical discriminant analyses for each site using habitat type as a category. (A) Above-water architectural only, (B) below-water architectural features only, and (C) all architectural features combined.
was different between sites such that the fringing forest of one site was sometimes found in the same multidimensional space as overwash forest of another (Fig. 2B). Additionally, complete within-site separation of overwash from fringing forest type was only found for UTB and Skyway (SKY) sites (Fig. 2B). MANOVA results
Table 3 Results of the discrimination ability of different discriminant models using the cross-validation method Model
Misclassification Misclassification Overall rate of fringing rate of overwash misclassification forest forest rate
Whole tree 36% architecture Above-water 38% architecture Below-water 32% architecture
61%
48%
91%
65%
44%
38%
Above-water architecture has been consistently used to describe the morphological heterogeneity of Rhizophora mangle trees from different forest types (e.g., Araujo et al., 1997). Yet, in this study, above-water architectural features provided extremely poor discrimination of mangrove forest types. Overall, forest type was classified correctly only in 50% of all cases and above-water architectural features were especially poor at discriminating overwash forest. We suspect that some of the lack of discriminatory power may lie in the quantification of tree morphology. For example, the diameter of most forest tree species consistently decreases when measuring from the ground to the canopy (Brooks pers. obs.) allowing for easy comparison of diameter at breast height. However, it is not uncommon for the bole of R. mangle to inconsistently increase and decrease in size. Similarly, it is questionable if mangrove tree height can be compared directly. While most tree species living within terrestrial forests grow perpendicular to the ground, R. mangle may grow perpendicularly or horizontally with respect to the ground in our site (Brooks pers. obs.). Leaf morphology (i.e., area, length, and width) has been found to differ between dwarf and basin red mangroves (Araujo et al., 1997) and shows promise as an above-water feature that can be directly compared
R.A. Brooks, S.S. Bell / Estuarine, Coastal and Shelf Science 65 (2005) 440e448
among forest types. Results of this study, however, do not indicate any significant difference in the leaf morphology of fringing and overwash forest types using a per tree comparison. Differences in mangrove leaf area can be the result of varying environmental stress levels (e.g., salinity, pollutants, nutrient limitation) among forest types (Araujo et al., 1997). If this is true then the similarities in leaf morphology found in this study suggest that both forest types experience similar stressors in Tampa Bay. Interestingly, classification of habitat based upon root architectural features alone provided the best discrimination of forest type. The fringing forest type exhibited a larger proportion of unattached roots and greater root complexity (i.e., nearest neighbor distance) within the intertidal. On the level of a single prop root, complexity (i.e., maximum root order and the average number of terminal roots arising from a single prop root) was greater for overwash forests. Interestingly, although the complexity of a primary root was higher in the overwash forest type, it did not result in a significant increase in either root density or spatial complexity when viewed over the meter scale. The lack of a difference in root density may be related to the fact that no relationship existed between the number of primary prop roots a tree produced and the actual complexity (i.e., number of branching events) of the root. Alternatively, tree densities may differ such that multiple trees add to the number of roots/m2. Overall, discrimination of forest type even when using below-water features was still relatively poor. Of the 17 architectural characters measured in this study only one parameter, maximum root order, was significantly different between forest types. The biological significance of this is questionable, however, as fringing and overwash forest types differed by only one branching event. Therefore using maximum root order as a single variable to definitively distinguish between forest types does not appear to be practical. Additionally, classification based upon a single measure is not recommended as univariate measures can vary with differences in tree age, disturbance history (Araujo et al., 1997), and measurement technique (Clough et al., 1997). This study is the first to assess the robustness of the original classification scheme proposed by Lugo and Snedaker (1974) using multivariate statistics rather than general descriptors. Based on Rhizophora mangle architecture, the results of this study do not support a sharp distinction between fringing and overwash forest types. Instead, the results support that the two are actually architecturally indistinguishable. Our quantitative findings, based solely upon physical structure, suggest that the more appropriate classification system may be the one employed by Cintron et al. (1985) and Ewel et al. (1998) in which overwash and fringing forests
445
are combined into one category and referred to as ‘‘tide dominated’’ habitat. Architectural similarity of fringing and overwash mangroves may be unique to Tampa Bay as this geographic area represents an ecotone between mangroves at the most northerly portion of their geographic range and freeze tolerant intertidal salt marshes (Dawes, 1998). It is possible that large site-specific differences in tree morphology do not exist in Tampa Bay because of increased physiological costs (e.g., cold temperature stress) or nutrient limitations constraining mangrove phenotypic plasticity (Feller, 1995; Koch and Snedaker, 1997; DeWitt et al., 1998; Chen and Twilley, 1999; Feller and Whigham, 1999; Feller et al., 2002). Twilley (1995) suggests that due to regional scale differences in environmental settings the forest types created by Lugo and Snedaker (1974) are actually ‘‘microtopographic’’ categories and only comparable on a local scale. At the UTB and SKY sites, it was possible to consistently discriminate overwash from fringing forests using architectural features. Therefore, it may be possible to distinguish between forest types within some sites but extrapolating a model across sites even within Tampa Bay appears problematic because of the high degree of structural heterogeneity among mangrove stands (Brooks, 2002). In Florida, the wood boring isopod, Sphaeroma terebrans Bate, exploits the intertidal habitat created by prop roots in intertidal red mangrove habitat (Rehm and Humm, 1973; Estevez, 1978). Sphaeroma terebrans is found almost exclusively within free hanging aerial roots of Rhizophora mangle (Estevez, 1978) and is present only within the intertidal zone. Isopod attack impacts the mangrove tree directly through root architectural changes (Simberloff et al., 1978; Ribi, 1981), reduced root production, increased root atrophy (Perry, 1988; Perry and Brusca, 1989; Ellison and Farnsworth, 1990), and root repair (Brooks and Bell, 2002). Therefore, if S. terebrans utilized the two forest types differently in Tampa Bay it might result in root architectural differences. So we asked the question of whether the patterns of root architecture found in this study might be linked to different levels of isopod attack between forest types. Habitat utilization by S. terebrans at the study sites, however, did not significantly differ between the two habitat types. Within a study site, isopod density and demographic traits were similar and insufficient to account for differences in root order characteristics between fringing and overwash mangroves (Brooks, 2002). The lack of discrimination by S. terebrans between the two habitat types suggests similar food resources and physiological conditions. Categorization of mangrove habitats may benefit from examination over a larger spatial scale or utilization of a landscape perspective (Dunning et al., 1992; Forman, 1995). Specifically, mangrove habitat is
446
R.A. Brooks, S.S. Bell / Estuarine, Coastal and Shelf Science 65 (2005) 440e448
often found in close proximity to a wide variety of other estuarine habitats (e.g., seagrass beds, oyster reefs, salt marsh) and the position of mangrove stands within the seascape affects habitat use by nekton (Rooker and Dennis, 1991; Laegdsgaard and Johnson, 1995; Ley et al., 1999; Thollot et al., 1999; Nagelkerken et al., 2000), fouling organisms (Rodriguez and Stoner, 1990; Bingham, 1992), and juvenile spiny lobsters (Acosta and Butler, 1997). Similarly, the size, shape, connectivity, and orientation to tidal flow of a mangrove stand may also be important if it impacts the encounter rate of nekton (Nagelkerken et al., 2000), or dispersing larvae (Bingham, 1992), the accessibility of predators, and/or the interception of floating propagules. Thus, classification of mangrove habitat for management purposes within coastal systems such as Tampa Bay might be more appropriately viewed using a conservation perspective that assesses habitat at a landscape-scale level rather than at the scale of individual trees. In summary, the purpose of this study was to evaluate if tree architecture could be used to discriminate fringing versus overwash mangrove forest types. The results of this study suggest that there is no sharp distinction between these two categories and they are architecturally indistinguishable within Tampa Bay. Root architectural features appear to be better than classical forestry measurements for distinguishing between the two forest types. However, in most cases forest type was only correctly classified two out of three times. Thus, our results using root structure as well as more traditional tree features support the suggestion by Gilmore and Snedaker (1993) and classification system used by Cintron et al. (1985) and Ewel et al. (1998) in which overwash and fringing forest types are combined into the one category defined as ‘‘tide dominated’’.
Results of a canonical discriminant analysis model using above-water architectural features Class variables
Total canonical structure
Tree height (m) Diameter at breast height (cm) Leaf area (cm2) Leaf length (cm) Leaf width (cm) Number of primary branches Number of secondary branches
ÿ0.11 0.16 ÿ0.05 ÿ0.37 0.16 0.68 0.30
Results of a canonical discriminant analysis model using below-water architectural features Class variables Avg. number of roots originating from a primary root Maximum root branching order Number of primary drop roots Number of primary prop roots Root density (m2) Ratio of unattached to attached roots Root complexity: cell area (c.v.) Root complexity: local aspect ratio (c.v.) Root complexity: nearest neighbor distance (c.v.) Root complexity: total area of cover
A. Ellison, E. Estevez, and two anonymous reviewers made many helpful comments on earlier drafts of the manuscript. This work was supported in part by an Aylesworth Fellowship, Tharpe Fellowship, and Old Salt Fishing Organization Fellowship to R.A. Brooks.
Appendix I MANOVA results examining the separation of overwash from fringing forest types Test
df
Wilk’s l
F
P
Above-water architecture Below-water architecture Whole-tree architecture
7,75 9,68 16,58
0.954 0.731 0.638
0.513 2.779 2.057
0.822 0.008 0.024
0.40 0.78 e 0.20 ÿ0.01 ÿ0.09 0.05 0.16 ÿ0.11 ÿ0.03
Results of a canonical discriminant analysis model combining both above and below-water architectural features Class variables
Acknowledgements
Total canonical structure
(A) Above-water architecture Tree height (m) Diameter at breast height (cm) Leaf area (cm2) Leaf length (cm) Leaf width (cm) Number of primary branches Number of secondary branches (B) Below-water architecture Avg. number of roots originating from a primary root Maximum root branching order Number of primary drop roots Number of primary prop roots Root density (m2) Ratio of unattached to attached roots Root complexity: cell area (c.v.) Root complexity: local aspect ratio (c.v.) Root complexity: nearest neighbor distance (c.v.) Root complexity: total area of cover
Total canonical structure 0.06 0.12 0.09 ÿ0.03 0.11 0.32 0.21 0.36 0.71 e 0.21 0.04 ÿ0.11 0.02 0.14 ÿ0.10 0.05
R.A. Brooks, S.S. Bell / Estuarine, Coastal and Shelf Science 65 (2005) 440e448
References Acosta, C.A., Butler, M.J., 1997. Role of mangrove habitat as a nursery for juvenile spiny lobster, Panulirus argus, in Belize. Marine & Freshwater Research 48, 721e727. Araujo, R.J., Jaramillo, J.C., Snedaker, C., 1997. LAI and leaf size differences in two red mangrove forest types in South Florida. Bulletin of Marine Science 60, 643e647. Bartholomew, A., Diaz, R.J., Cicchetti, G., 2000. New dimensionless indices of structural habitat complexity: predicted and actual effects on a predator’s foraging success. Marine Ecology Progress Series 206, 45e59. Bingham, B.L., 1992. Life histories in an epifaunal community: coupling of adult and larval processes. Ecology 73, 2244e2259. Brooks, G.R., Doyle, L.J., 1998. Recent sedimentary development of Tampa Bay, Florida: a microtidal estuary incised into tertiary platform carbonates. Estuaries 21, 391e406. Brooks, R.A., 2002. Plant-Animal interaction within the red mangroves, Rhizophora mangle L., of Tampa Bay: Mangrove habitat classification and isopod, Sphaeroma terebrans Bate, colonization of a dynamic root substratum. PhD dissertation. University of South Florida, Tampa, Florida, USA. Brooks, R.A., Bell, S.S., 2002. Mangrove response to attack by a root boring isopod: root repair versus architectural modification. Marine Ecology Progress Series 231, 85e91. Byers, J.A., 1992. Dirichlet tessellation of bark beetle spatial attack points. Journal of Animal Ecology 61, 759e768. Carlson, P.R., Yarbro, L.A., Zimmerman, C.F., Montgomery, J.R., 1983. Pore water chemistry of an overwash mangrove island. Florida Scientist 46, 239e249. Chen, R., Twilley, R.R., 1999. Patterns of mangrove forest structure and soil nutrient dynamics along the Shark River Estuary, Florida. Estuaries 22, 955e970. Cintron, G., Lugo, A.E., Martinez, R., 1985. Structural and functional properties of mangrove forests. In: D’Arcy, W.G., Mireya, D.C.A. (Eds.), The Botany and Natural History of Panama. Missouri Botanical Garden, St. Louis, Missouri. Cintron, G.A., Novelli, Y.S., 1984. Methods for studying mangrove structure. In: Snedaker, S.C., Snedaker, J.G. (Eds.), The Mangrove Ecosystem: Research Methods. UNESCO, Paris, pp. 91e113. Clough, B.F., Dixon, P., Dalhaus, O., 1997. Allometric relationships for estimating biomass in multi-stemmed mangrove trees. Australian Journal of Botany 45, 1023e1031. Dawes, C.J., 1998. Marine Botany. John Wiley & Sons Incorporated, New York, USA. DeWitt, T.J., Sih, A., Wilson, D.S., 1998. Costs and limits of phenotypic plasticity. Trends in Ecology & Evolution 13, 77e81. Diaz, M., Carbonell, R., Santos, T., Telleria, J.L., 1998. Breeding bird communities in pine plantations of the Spanish plateaux: biogeography, landscape and vegetation effects. Journal of Applied Ecology 35, 562e574. Dunning, J.B., Danielson, B.J., Pulliam, H.R., 1992. Ecological processes that affect populations in complex landscapes. Oikos 65, 169e175. Ellison, A.M., Farnsworth, E.J., 1990. The ecology of Belizean mangrove-root fouling communities: I. Epibenthic fauna are barriers to isopod attack of red mangrove roots. Journal of Experimental Marine Biology & Ecology 142, 91e104. Erickson, A.A., Bell, S.S., Dawes, C.J., 2004. Does mangrove leaf chemistry help explain crab herbivory patterns? Biotropica 36, 333e343. Estevez, E.D., 1978. Ecology of Sphaeroma terebrans Bate, a wood boring isopod, in A Florida mangrove forest. Dissertation. University of South Florida, Tampa, Florida, USA. Everitt, B.S., Der, G., 1996. A Handbook of Statistical Analyses Using SAS. Chapman & Hall, London, UK.
447
Ewel, K.C., Twilley, R.R., Ong, J.E., 1998. Different kinds of mangrove forests provide different goods and services. Global Ecology & Biogeographical Letters 7, 83e94. Feller, I.C., 1995. Effects of nutrient enrichment on growth and herbivory of dwarf red mangrove (Rhizophora mangle). Ecological Monographs 65, 477e505. Feller, I.C., Mathis, W.N., 1997. Primary herbivory by wood-boring insects along an architectural gradient of Rhizophora mangle. Biotropica 29, 440e451. Feller, I.C., Whigham, D.F., 1999. Effects of nutrient enrichment on within-stand cycling in a mangrove forest. Ecology 80, 2193e2205. Feller, I.C., McKee, K.L., Whigham, D.F., O’Neill, J.P., 2002. Nitrogen vs. phosphorus limitation across an ecotonal gradient in a mangrove forest. Biogeochemistry 62, 145e175. Forman, R.T.T., 1995. Some general principles of landscape and regional ecology. Landscape Ecology 10, 133e142. Gill, A.M., Tomlinson, P.B., 1975. Aerial roots: an array of forms and functions. In: Torrey, J.G., Clarkson, D.T. (Eds.), The Development and Function of Roots. Academic Press, New York, USA, pp. 237e260. Gill, A.M., Tomlinson, P.B., 1977. Studies on the growth of Red Mangrove (Rhizophora mangle L.) 4. The adult root system. Biotropica 9, 145e155. Gilmore Jr., R.G., Snedaker, S.C., 1993. Chapter 5: mangrove forests. In: Martin, W.H., Boyce, S.G., Echternacht, E.C. (Eds.), Biodiversity of the Southeastern United States: Lowland Terrestrial Communities. John Wiley and Sons, Inc. Publishers, New York, NY, p. 502. Hix, D.M., Pearcy, J.N., 1997. Forest ecosystems of the Marietta Unit, Wayne National Forest, southeastern Ohio: multifactor classification and analysis. Canadian Journal of Forestry Research 27, 1117e1131. Koch, M.S., Snedaker, S.C., 1997. Factors influencing Rhizophora mangle L. seedling development in Everglades carbonate soils. Aquatic Botany 59, 87e98. Laegdsgaard, P., Johnson, C.R., 1995. Mangrove habitats as nurseries e unique assemblages of juvenile fish in subtropical mangroves in Eastern Australia. Marine Ecology Progress Series 126, 67e81. Ley, J.A., McIvor, C.C., Montague, C.L., 1999. Fishes in mangrove prop-root habitats of Northeastern Florida Bay: distinct assemblages across an estuarine gradient. Estuarine, Coastal, & Shelf Science 48, 701e723. Lin, G.H., Sternberg, L.D.L., 1992a. Differences in morphology, carbon isotope ratios, and photosynthesis between scrub and fringe mangroves in Florida, USA. Aquatic Botany 42, 303e313. Lin, G.H., Sternberg, L.D.L., 1992b. Comparative study of water uptake and photosynthetic gas exchange between scrub and fringe red mangroves, Rhizophora mangle L. Oecologia (Berlin) 90, 399e 403. Lin, G.H., Sternberg, L.D.L., 1993. Effects of salinity fluctuation on photosynthetic gas exchange and plant growth of the red mangrove (Rhizophora mangle L.). Journal of Experimental Botany 44, 9e16. Lugo, A.E., Snedaker, S.C., 1974. The ecology of mangroves. Annual Review of Ecology & Systematics 5, 39e64. Madley, K.A., 1997. Factors affecting the movement of drifting macroalgae (Gracilaria verrucosa) in seagrass (Halodule wrightii) beds. Masters. University of South Florida, Tampa, Florida, USA. McKee, K.L., Mendelssohn, I.A., Hester, M.H., 1988. Reexamination of pore water sulfide concentrations and redox potentials near the aerial roots of Rhizophora mangle and Avicennia germinans. American Journal of Botany 75, 1352e1359. Momen, B., Zehr, J.P., 1998. Watershed classification by discriminant analyses of lakewater-chemistry and terrestrial characteristics. Ecological Applications 8, 497e507. Nagelkerken, I., Dorenbosch, M., Verberk, W.C.E.P., Cocheret de la Moriniere, E., van der Velde, G., 2000. Importance of shallowwater biotopes of a Caribbean bay for juvenile coral reef
448
R.A. Brooks, S.S. Bell / Estuarine, Coastal and Shelf Science 65 (2005) 440e448
fishes: patterns in biotope association, community structure and spatial distribution. Marine Ecology Progress Series 202, 175e192. Perry, D.M., 1988. Effects of associated fauna on growth and productivity in the Red Mangrove. Ecology 69, 1064e1075. Perry, D.M., Brusca, R.C., 1989. Effects of the root-boring isopod Sphaeroma peruvianum on red mangrove forests. Marine Ecology Progress Series 57, 287e292. Perry, J.N., Liebhold, A.M., Rosenberg, S., Dungan, J., Miriti, M., Jakomulska, A., Citron-Pousty, S., 2002. Illustrations and guidelines for selecting statistical methods for quantifying spatial pattern in ecological data. Ecography 25, 578e600. Pool, D.J., Snedaker, S.C., Lugo, A.E., 1977. Structure of mangrove forests in Florida, Puerto-Rico, Mexico, and Costa-Rica. Biotropica 9, 205e212. Primavera, J.H., 1997. Fish predation on mangrove-associated penaeids the role of structures and substrate. Journal of Experimental Marine Biology & Ecology 215, 205e216. Rehm, A., Humm, J., 1973. Sphaeroma terebrans: a threat to the mangroves of southwestern Florida. Science 182, 173e174. Ribi, G., 1981. Does the wood-boring isopod Sphaeroma terebrans benefit mangroves Rhizophora mangle. Bulletin of Marine Science 31, 295e928. Rodriguez, C., Stoner, A.W., 1990. The epiphyte community of mangrove roots in a tropical estuary: distribution and biomass. Aquatic Botany 36, 117e126.
Ronnback, P., Troell, M., Kautsky, N., Primavera, J.H., 1999. Distribution pattern of shrimps and fish among Avicennia and Rhizophora microhabitats in the Pagbilao mangroves, Philippines. Estuarine, Coastal, & Shelf Science 48, 223e234. Rooker, J.R., Dennis, G.D., 1991. Diel, lunar and seasonal changes in a mangrove fish assemblage off southwestern Puerto Rico. Bulletin of Marine Science 9, 684e698. Simberloff, D., Brown, B.J., Lowrie, S., 1978. Isopod and insect root borers may benefit Florida mangroves. Science 201, 630e632. Taylor, P.R., Littler, M.M., Littler, D.S., 1986. Escapes from herbivory in relation to the structure of mangrove island macroalgal communities. Oecologia 69, 481e490. Thollot, P., Kulbicki, M., Harmelin-Vivien, M., 1999. Trophic analysis and food webs of mangrove fish assemblages from New Caledonia. Comptes Rendus de l’Academie des Sciences-Series III-Science de la Vie 322, 607e619. Thayer, G.W., Colby, D.R., Hettler, W.F., 1987. Utilization of the red mangrove prop root habitat by fishes in south Florida. Marine Ecology Progress Series 35, 25e38. Twilley, R.R., 1995. Properties of mangrove ecosystems related to the energy signature of coastal environments. In: Hall, A.S. (Ed.), Maximum Power The Ideas and Applications of H.T. Odum. University Press of Colorado, Colorado, USA, pp. 43e62.