10) Correlation - Methods
10) Correlation – Methods
Correlations between structural attributes and arthropod biodiversity were investigated. The results can shed light on the influence of structure and the response of the animals. Using bivariate techniques, the structural descriptors in ten categories were matched against summaries of abundance, richness, diversity, and the abundance of each RTU. NMS vector biplots highlight structural descriptors most strongly correlated with changes in arthropod composition. Mantel tests investigated the strength of the relationship of similarity between trees for structure against the similarity between trees for arthropods. 10.1 Exploring correlations between structure and arthropod biodiversity
The effect of crown structure on the trunk and canopy arthropod biodiversity was explored at a tree level. Only information describing each tree was used. No trap or placement level analyses were conducted. The structural descriptors of the tree were considered independent predictors, and tree level arthropod biodiversity descriptors were considered dependent responses.
Interpretation of the results must accommodate that several structural descriptors were strongly correlated. For example, mean cone volume corresponds with mean cone surface area. Some descriptors were perfectly correlated. For example, the largest branch airspace was always found in a large branch, and therefore the descriptor of maximum branch airspace for all branches will be the same as the maximum branch airspace for dead branches.
Correlation does not equal causation. The correlation of a structural descriptor with any aspect of arthropod biodiversity does necessarily mean the arthropod is responding to that measure. It is possible that the animals are actually responding to cryptic predictors. If these cryptic predictors are themselves correlated with the observed predictor variables, then a potentially misleading predictor-response will be observed.
It is also worth noting that a correlation between measures of structure and arthropods does not indicate which is truly a predictor or a response. If one
10) Correlation - Methods hypothetically accepts that arthropods are responsible for shaping the structure of a tree, then the correlations are also evidence of arthropods predicting structural responses.
The results of this meta-analysis are preliminary and exploratory. With an immense number of explored hypotheses defined robotically, and a very small number of replicates, statistical power is extremely low. Furthermore, many of the predictor responses are correlated to each other. Statistical techniques such as the Bonferroni correction minimize the chance of false positives by increasing the chance of false negatives (Benjamini & Yekutieli, 2001; Garcia, 2004). In the present exploratory research, analysis is performed in a contrasting manner. False positives were considered a lesser evil than false negatives. Positive results will suggest new avenues of investigation, which future investigations can assess. This is in contrast to fields such as medical drug research, where a false positive could expose people to needless harm (Perneger, 1998). Therefore, a decision has been made to prefer false positives to false negatives. 10.2 Bivariate Correlations
Bivariate analyses of correlation were performed using JMP software (SAS Institute, 2003). The structural descriptors were considered predictors, and tree level summaries of arthropod total abundance, richness, and diversity were considered responses. The non-parametric correlation test statistic Spearman’s rho (SAS Institute, 2003) was generated for each combination. This test tests the relationship between two variables by comparing the ranks of trees rather than the actual values. The assumption of normality was considered unstable for the full range of variables. The reported p-value was used to test a null hypothesis of no relationship between the variables.
To remove the confounding factor of age, analysis was done simultaneously and separately on each age class. The predictor variables were the 107 structural descriptors generated to describe each trees crown structure.
Summary variables of arthropod biodiversity were used as response variables. These were the 24 combinations of alltaxa +3 and alltaxa +5 for abundances, RTU richness, and diversity as measured by -ln(Simpsons D). In addition, the abundances per tree for the 128 RTU with greater than five individuals collected were used as
10) Correlation - Methods
response variables. The statistics software automatically removed invalid comparisons. This was usually due to situations in which no specimens of a taxon were caught in an age class.
Interpretation of p-value output: With α = 0.05, one in twenty pairs of variables could be expected to show a significant result by chance alone (Benjamini & Yekutieli, 2001; Garcia, 2004; Perneger, 1998).
Using techniques summarized in Garcia (2004), the following procedures were applied to compensate for the false discovery rate inherent in the high number of comparisons and to filter a manageable number of correlations for presentation.
Standard Bonferroni correction: The standard Bonferroni corrected significance was tested. The Bonferroni correction, or “one-step Bonferroni” (Garcia, 2004), divides the significance level α by the number of tests performed. This test is considered overly conservative and is designed to eliminate the chances of false positives (Perneger, 1998).
Step Up FDR technique: Sharpened “step-up False Discovery Rate” (hereafter “FDR”) significance levels were calculated (Benjamini & Yekutieli, 2001; Benjamini et al., 2002). The FDR technique was designed to control the proportion of false positives to observed positives. FDR is more tolerant of false positives when there are many observed significant results, and less tolerant when there are few (Benjamini & Yekutieli, 2001). The process is reworded by Garcia (2004):
a) p-values are ranked in ascending order, j being the resulting
rank. b) Proceed from jn to j1, until finding a first p-value, ranked k, satisfying
pk≤/zk */ α /n. c) then reject Ho for j ≤ k and accept all the remaining null hypotheses
Observed: Expected ratio for different levels of α: The uncorrected number of expected and observed significant results at both α = 0.1, α = 0.05, α = 0.01, α = 0.005, and α = 0.001 are presented in graphical form and discussed.
P<0.001 for either age class: Pairs of correlations with a p<0.001 for either age class were tabulated. This is one fiftieth of the normal α = 0.05. The p-value of the other age class was also reported.
When combinations were similarly correlated in both age classes, this supports the significant results and is evidence against a false positive. If the combination was
10) Correlation - Methods
highly correlated one age class but not the other, this could be interpreted in two ways. It is either evidence of a false positive, or an indication of different dynamics within the crowns of only one age classes. Similarly, sign mismatches (+/-), can be viewed as evidence of either a false positive or an opposing trend in the age classes.
Combinations were grouped by response type (abundance, richness, diversity, RTU abundance) and by predictor descriptor category (Tree size, crown depth, etc.). Within each chart, predictor categories are divided by lines.
Sorting the combinations by predictor category rather than RTU response was not intended to express an interest in predictive structures over responsive arthropod taxa. This imbalance in presentation was noted and the discussion addresses responding taxa as well as effective predictors.
P<0.05 for both age classes: Bivariate combinations in which both age classes reported a rank correlation probability of p<0.05 were tabulated. The signs of the rank correlation coefficients were compared to test that age classes were not showing opposite relationships. Opposite correlations would suggest a false positive, in which contradictory relationships existed for the different age classes. Combinations were grouped as above. To aid in visually identifying arthropod responses, letters were used to code similar responses. These letters served to link auto-correlated predictors and to serve as identifiers for discussions. 10.3 Multivariate correlations
NMS ordination: Multi-variate techniques were used to compare the effect of the structural descriptors on the faunal composition of each tree. The abundances of the 128 RTU with greater than five five individuals collected were used for NMS ordinations (species matrix), and the 107 structural descriptors were related to the ordination (environmental matrix) (McCune & Grace, 2002).
This was performed for all trees pooled. The r correlation coefficient between each structural descriptors and the axes of Non-metric Multidimensional Scaling (NMS) ordination was output from PC-ORD (McCune, 1999). These offer descriptive comparisons of influence. The unsquared r-value is presented to retain the sign (+/-) of the correlation. P-values were not output from PC-ORD as samples violate the assumption of independence (McCune & Grace, 2002)
10) Correlation - Methods
Higher correlations are evidence of an influence of a descriptor on the composition of the tree fauna. Graphical output was generated overlaying structural variables onto an NMS plot. Numerical output was generated by ranking and tabulating the correlation coefficients.
Mantel test: A Mantel test was performed between the structural descriptors and the arthropod fauna for alltraps +3 using PC-ORD software (McCune, 1999). This was done for both age classes pooled, and then for each age class separately. This test compares the correlation between the (Sorenson/Bray-Curtis) distance matrices relating the trees by structural descriptors and by arthropod collections (McCune & Grace, 2002).
:
11) Correlation - Results
11) Correlation – Results
Several correlations between structural descriptors and arthropod biodiversity were identified. The Bonferroni correction and Step Up FDR techniques yielded no significant results. At several levels of alpha, the old trees showed more significant results than expected with summaries of richness and diversity. Correlations are tabulated when p<0.001 for either age class, and when p<0.05 for both age classes. Mismatched signs (+/-) between the ages classes for a correlation were marked. These indicate either opposing trends in the different age classes, or false results. NMS biplot analysis graphically illustrated and identified structural descriptors that were most correlated with changes arthropod composition. Mantel tests showed no correlation between the structural descriptor distance matrix and arthropod composition distance matrix. 11.1 Bivariate results
11.1.1 Standard Bonferroni correction results
When filtered through the Bonferroni correction, no significant results were detected using Pearson’s rho rank correlation test. For the Bonferroni correction, the required probability for a significant result was approximately 0.00002. Output from JMP statistics software output results to only four decimal places.
11.1.2 Step Up FDR technique results
Similarly, no significant rank correlations were detected using Step Up FDR techniques. Manual implementation of the algorithm using Excel (Microsoft, 2000) and automated implementation using FDRalgo software (Benjamini et al., 2002) vgenerated a required probability of 0.
11.1.3 Observed: Expected ratios for different levels of α
Structural descriptors were correlated to arthropod abundances summaries less often than expected by chance alone (Figure 11.1). 11) Correlation - Results
Bivariate ScatterPlots Rho Correlations Proportion of Expected Significant Results Y at Alpha X 3
Abundance 100yr 2.5 Abundance Old Expected 2 Ratio 1.5 Observed to Expected 1
0.5
0 0.1 0.05 0.01 0.005 0.001 Alpha
Figure 11.1: Ratio of observed to expected significant results at different levels of significance for bivariate rank correlations of 107 structural descriptors to 24 abundance summaries
In the old trees, more rank correlations were found between the structural descriptors and summaries of arthropod richness than expected by chance alone (Figure 11.2). In the 100yr trees, fewer than expected correlations were found.
Bivariate ScatterPlots Rho Correlations Proportion of Expected Significant Results Y at Alpha X
5 4.5 4 3.5 Richness 100yr 3 Ratio Richness Old 2.5 Observed to Expected 2 Expected 1.5 1 0.5 0 0.1 0.05 0.01 0.005 0.001 Alpha
Figure 11.2: Ratio of observed to expected significant results at different levels of significance for bivariate rank correlations of 107 structural descriptors to 24 richness summaries
Similar to the richness results, old trees always had more correlations for diversity than expected by chance (Figure 11.3), and 100yr trees always had less than expected 11) Correlation - Results
Bivariate ScatterPlots Rho Correlations Proportion of Expected Significant Results Y at Alpha X
3 Diversity 100yr 2.5 Diversity Old Expected 2 Ratio 1.5 Observed to Expected 1
0.5
0 0.1 0.05 0.01 0.005 0.001 Alpha
Figure 11.3: Ratio of observed to expected significant results at different levels of significance for bivariate rank correlations of 107 structural descriptors to 24 diversity summaries
For RTU abundances, slightly more correlations than expected by chance alone were detected in the 100yr trees, and slightly fewer in the old trees (Figure 11.4). At all surveyed levels of significance, 100yr trees had a higher ratio of observed: expected significant results than old trees.
Bivariate ScatterPlots Rho Correlations Proportion of Expected Significant Results Y at Alpha X
1.8 1.6 1.4 1.2 1 Ratio Observed to 0.8 Expected RTU 100yr 0.6 RTU Old 0.4 Expected 0.2 0 0.1 0.05 0.01 0.005 0.001 Alpha
Figure 11.4: Ratio of observed to expected significant results at different levels of significance for bivariate rank correlations of 107 structural descriptors to 128 abundances of RTU
11.1.4 P<0.001 for either age class
No combinations of variables were detected as significantly rank correlated at p<0.001 in both age classes. Of 37 bivariate combinations in which one age class showed significant rank correlation at p<0.001, six predicted abundance summaries, seven predicted richness summaries, six predicted diversity summaries, and eighteen 11) Correlation - Results
predicted the abundance of a specific RTU. Combinations with contradictory signs are marked with an asterisk (*). 18 of the 37 had mismatched signs.
Abundance:
Table 11.1. Spearman’ s rho rank correlations between 107 structural descriptors and 24 summaries of abundance in which at least one age class exhibited a p-value<0.001
In the 100yr age class, all significant correlations were found for animals in the CD sticky traps (Table 11.1). Coleoptera abundance was negatively correlated to the ratio of crown airspace to trunk wood volume. The 100yr trees with the most spreading crowns and slender trunks had the fewest beetles. Hymenoptera in CD sticky traps responded positively four auto-correlated descriptors of branch cone size. The 100yr trees with the largest total and average sized cones had more hymenopterans than those with smaller.
In the old trees (Table 11.1), Hemiptera abundance in funnels was negatively rank correlated with the mean vertical arc of live branches. Old trees with live branches with less vertical arc had a greater abundance of bugs.
These results were generally not matched by a significant correspondence in the other age class. The CD sticky hymenopteran abundance showed an contradictory correlation for total branch volume in the two age classes (i.e. signs did not match). It is unclear if this is evidence of an opposing trend or of a spurious result.
Richness:
Table 11.2. Spearman’ s rho rank correlations between 107 structural descriptors and 24 summaries of richness in which at least one age class exhibited a p-value<0.001 11) Correlation - Results
As indicated by Table 11.2, significant correlations with summaries of richness were detected more often in the old trees. Like the correlations of Hemiptera abundance in funnels with a measure of vertical arc, their richness was also negatively correlated with the mean upward arc of branches in old trees. The mean branch upward arc also negatively predicted the total richness collected in the hangtraps.
For old trees, Lepidoptera richness in the hangtraps was positively rank correlated with the mean upwards slope of live branches. Diptera richness in CD sticky traps was negatively predicted by the total live branch airspace. The richness of Hemiptera in hangtraps was positively correlated with the mean surface area of dead branches.
Only one correlation was detected in 100yr trees. The average surface area of dead branch airspace as calculated by polyhedra was positively correlated to the richness of Hymenoptera in the funnel traps.
Diversity:
Table 11.3. Spearman’ s rho rank correlations between 107 structural descriptors and 24 summaries of diversity in which at least one age class exhibited a p-value<0.001
Like the richness summaries, old trees showed more significant correlations of diversity responses at p<0.001 than 100yr trees (Table 11.3).
In the old trees, the total diversity of the arthropod fauna was positively correlated with the portion of the total wood volume in the trunk. Old trees with more wood in the trunk than the branches supported a more diverse fauna. Positive correlations were detected for the total wood volume of live branches with Hymenoptera diversity in the CD sticky traps, and for average branch length with Lepidoptera diversity in the funnel traps. Negative correlations were detected for beetle diversity in CD sticky traps with total wood volume of live branches, and for hangtrapped Lepidoptera diversity with the mean vertical arc of live branches. 11) Correlation - Results
In the 100yr age class, the diversity of Hemiptera was positively correlated with the mean start diameter of the branches. Trees with thicker branch bases had a more diverse bug fauna.
RTU abundance:
Table 11.4. Spearman’ s rho rank correlations between 107 structural descriptors and 128 abundances of RTU in which at least one age class exhibited a p-value<0.001
The abundance of several RTU showed rank correlations with structural descriptors (Table 11.4). Three moths showed multiple correlations. The grouped ants, three flies, two bugs, two wasps, and a beetle each showed single correlations.
An oecophorid moth, Oecophoridae YDB sp. 2, was positively predicted by the total crown depth in 100yr trees and the average surface area of dead branches in old trees. It was negatively predicted by the average live branch height in the 100yr trees. Total crown depth and mean live branch height are auto-correlated structural descriptor. Deeper crowns usually had lower live crowns, and therefore these correlations correspond. A tortricid moth, Torticidae YDB sp. 2, was positively correlated in the 100yr trees with the standard deviation and range of the dead branch scaling ratio, and negatively correlated in the old trees with the average branch starting diameter. A cosmopterygid moth, Cosmopterygidae YDB sp. 1, showed significant rank correlations in the 100yr trees. Its abundance was positively correlated with diameter at breast height, and negatively correlated with lowest branch height.
11.1.5 P<0.05 for both age classes
Only half (32 of 61) of the bivariate combinations with rank correlation significances of p<0.05 for both age classes agreed in their signs (+/-). Five 11) Correlation - Results correlations were identified describing abundances, fifteen correlations for each of richness and diversity, and 26 for RTU abundances. Combinations which contradictory signs are marked with an asterisk (*). A letter is placed next to the arthropod response to aid in visual assessment of response trends.
Abundance:
Abundance +/- 100yr Old Predictor Abundance Response mismatch? Rho P-value Rho P-value Sum Cone Volume (m3) all branches Hymenoptera- CDsticky A * 0.955 0.0008 -0.7928 0.0334 Mean Up Arc (deg) all branches Hymenoptera- Funnel B -0.8829 0.0085 -0.7748 0.0408 Mean Vertical Arc (deg) all branches Hymenoptera- Funnel B -0.8829 0.0085 -0.7748 0.0408 Mean Horizontal Arc (deg) all branches Hemiptera- Funnel C * -0.7748 0.0408 0.8214 0.0234 Mean Horizontal Arc (deg) live only Hymenoptera- Funnel B * -0.7568 0.0489 0.8469 0.0162
Table 11.5. Spearman’ s rho rank correlations between 107 structural descriptors and 24 summaries of abundance in which both age classes exhibited a p-value<0.05
Three of five detected correlations with abundance responses had mismatched signs (Table 11.5). Summaries of funnel trap Hymenopteran abundance responded negatively to measures of upwards arc, vertical arc, and live branch horizontal arc.
Richness:
Richness +/- 100yr Old Predictor Richness Response mismatch? Rho P-value Rho P-value Total Crown Depth (m) Diptera- All traps -0.7306 0.0396 -0.7711 0.0251 Mean branch height as % of treetop height All taxa- Hangtrap I -0.8571 0.0065 -0.8333 0.0102 Mean live branch height as % of treetop heighAll taxa- Hangtrap I -0.7857 0.0208 -0.8571 0.0065 Mean Cone Volume (m3) all branches Lepidoptera- Funnel G 0.7783 0.0393 0.8669 0.0115 Max Cone Volume (m3) dead only Lepidoptera- Funnel G * -0.8524 0.0148 0.8078 0.028 Std Dev Cone Volume (m3) dead only Lepidoptera- Funnel G * -0.8524 0.0148 0.9063 0.0049 Mean Cone Surface Area (m2) all branches Lepidoptera- Funnel G 0.7783 0.0393 0.8669 0.0115 Max Polyhedra Volume (m3) all branches All taxa- Hangtrap I * 0.7619 0.028 -0.8095 0.0149 Max Polyhedra Volume (m3) live only All taxa- Hangtrap I * 0.7619 0.028 -0.8095 0.0149 Std Dev Polyhedra Volume (m3) dead only All taxa- Hangtrap I * -0.9048 0.002 0.7143 0.0465 Std Dev Polyhedra Volume (m3) live only Diptera- Hangtrap * 0.7357 0.0375 -0.7186 0.0446 Range Start Diameter (m) live only All taxa- Hangtrap I 0.7857 0.0208 0.8333 0.0102 Mean Distance (m) all branches Lepidoptera- Funnel G 0.8154 0.0254 0.9063 0.0049 Mean Slope (deg) live only Hemiptera- Funnel C * -0.8829 0.0085 0.7857 0.0362 Mean Slope (deg) live only Lepidoptera- All traps * -0.747 0.0332 0.8051 0.0159
Table 11.5. Spearman’ s rho rank correlations between 107 structural descriptors and 24 summaries of richness in which both age classes exhibited a p-value<0.05
Eight of fifteen correlations with richness responses had mismatched signs (Table 11.5). The total richness of the fauna collected in hangtraps was negatively correlated with relative average branch heights, and with the standard deviation of 11) Correlation - Results dead branch airspace volume. The hangtrap richness was positively correlated with the range in start diameters for live branches. Trees with a range of branch girths had the richest hangtrap catch. The richness of flies in all traps pooled was negatively correlated with total crown depth. Their richness in hangtraps was negatively correlated with the standard deviation of live branch airspace volume. Lepidopteran richness in funnels corresponded positively with the branch volume, surface area, and length.
Diversity:
Diversity +/- 100yr Old Predictor Diversity Response mismatch? Rho P-value Rho P-value % of Wood Volume in Trunk Hemiptera- Hangtrap -0.7665 0.0265 -0.7619 0.028 Highest Branch Height (m) all branches Hemiptera- Funnel C -0.8571 0.0137 -0.8571 0.0137 Total Crown Depth (m) All taxa- CD Sticky D * 0.9286 0.0025 -0.9286 0.0025 Mean Branch Height (m) all branches Coleoptera- Funnel E -0.7857 0.0362 -0.8214 0.0234 Mean Branch Height (m) all branches Diptera- Funnel F -0.7857 0.0362 -0.8214 0.0234 Mean Branch Height (m) live Coleoptera- Funnel E -0.8571 0.0137 -0.8214 0.0234 Mean Branch Height (m) live Diptera- Funnel F -0.8571 0.0137 -0.8214 0.0234 Max Cone Volume (m3) live only Lepidoptera- Funnel G 0.9429 0.0048 0.8857 0.0188 Sum Cone Surface Area (m2) all branches Hemiptera- Hangtrap H 0.8383 0.0093 0.7619 0.028 Sum Cone Surface Area (m2) live only Hemiptera- Hangtrap H 0.7186 0.0446 0.7619 0.028 Sum Polyhedra Volume (m3) live only Hemiptera- Hangtrap H 0.8144 0.0138 0.7857 0.0208 TreePolyhedra Surface Area (m2) Hemiptera- Hangtrap H 0.8982 0.0024 0.7143 0.0465 Mean Start Diameter (m) all branches Lepidoptera- Funnel G 0.8857 0.0188 0.8286 0.0416 Range Start Diameter (m) all branches Hemiptera- Funnel C * -0.8214 0.0234 0.9286 0.0025 Range Start Diameter (m) dead only All taxa- CD Sticky D -0.8214 0.0234 -0.8108 0.0269
Table 11.6. Spearman’ s rho rank correlations between 107 structural descriptors and 24 summaries of diversity in which both age classes exhibited a p-value<0.05
For diversity responses, only two of fifteen correlations had mismatched signs (Table 11.6). The diversity of Hemiptera responded to several structural descriptors. Hemipteran funnel diversity was negatively correlated with the height of the highest branch. Their hangtrap diversity was negatively correlated with the portion of wood volume in the trunk, in contrast to the positive correlation with this predictor for diversity of all taxa in old trees (Table 11.3). Hangtrap Hemipteran diversity was further positively correlated with total branch surface area, branch airspace volume, and total crown airspace surface area.
Funnel Lepidoptera responded positively to maximum live branch volume, and to the average branch starting diameter. Both funnel Coleoptera and Diptera were negatively correlated with mean branch height. The total diversity of arthropods 11) Correlation - Results collected in CD sticky traps was correlated negatively with the range of dead branch diameters.
RTU abundance:
RTU Abundance +/- 100yr Old Predictor RTU Abundance Response mismatch? Rho P-value Rho P-value Ratio Polyhedra m3:Trunk m3 Coleoptera:Salpingidae:Orphanotrophium frigidum J -0.779 0.0227 -0.8607 0.0061 Mean branch height as % of treetop height Coleoptera:Brentidae:Apion tasmanicum J * 0.7407 0.0356 -0.8045 0.016 Mean dead branch height as % of treetop heigDiptera:Psychodidae YDB sp.1 K * -0.7326 0.0387 0.7559 0.03 Range Branch Height (m) dead only Diptera:Muscidae YDB sp.1 K * -0.8556 0.0067 0.7698 0.0255 Range Branch Height (m) dead only Lepidoptera:Oecophoridae YDB sp. 5 L * 0.7326 0.0387 -0.8452 0.0082 Std Dev Branch Height (m) dead only Orthoptera:Gryllacrididae:Kinemania YDB sp. 1 -0.7991 0.0173 -0.7559 0.03 Std Dev Branch Height (m) live only Diptera:Tachinidae YDB sp.1 K * 0.7259 0.0415 -0.7201 0.044 Sum Cone Volume (m3) dead only Lepidoptera:Oecophoridae YDB sp. 2 L.a * -0.8001 0.0171 0.8118 0.0144 Max Cone Volume (m3) dead only Lepidoptera:Oecophoridae YDB sp. 2 L.a * -0.7638 0.0274 0.7991 0.0173 Std Dev Cone Volume (m3) dead only Lepidoptera:Oecophoridae YDB sp. 2 L.a * -0.7274 0.0409 0.8244 0.0118 Mean Polyhedra Volume (m3) live only Diptera:Tipulidae YDB sp. 3 K * 0.7559 0.03 -0.7173 0.0452 Mean Polyhedra Volume (m3) dead only Hemiptera:Fulgoridae YDB sp.3 * -0.8248 0.0117 0.7412 0.0353 Max Polyhedra Volume (m3) all branches Coleoptera:Scirtidae:Pryonocyphon TFIC sp.01 J.b * 0.7638 0.0274 -0.8085 0.0151 Max Polyhedra Volume (m3) all branches Diptera:Phoridae YDB sp.1 K.c 0.8648 0.0056 0.7326 0.0387 Max Polyhedra Volume (m3) live only Coleoptera:Scirtidae:Pryonocyphon TFIC sp.01 J.b * 0.7638 0.0274 -0.8085 0.0151 Max Polyhedra Volume (m3) live only Diptera:Phoridae YDB sp.1 K.c 0.8648 0.0056 0.7326 0.0387 Std Dev Polyhedra Volume (m3) dead only Hymenoptera: Chalcidoid? YDB sp.1 M -0.8783 0.0041 -0.7109 0.0481 Total Foliage Units Coleoptera:Mycteridae:Trichosalpingus TFIC sp.01 J.d -0.7173 0.0452 -0.7325 0.0388 Range Start Diameter (m) all branches Coleoptera:Coccinellidae:Rhyzobius TFIC sp.05 J * 0.7325 0.0388 -0.7325 0.0388 Mean Scaling Ratio all branches Hymenoptera:Formicidae:Hymenoptera YDB sp. 22 M 0.7559 0.03 0.7326 0.0387 Mean Vertical Arc (deg) live only Diptera:Phoridae YDB sp.1 K.c 0.8154 0.0136 0.7326 0.0387 Mean Down Arc (deg) live only Diptera:Phoridae YDB sp.1 K.c 0.803 0.0164 0.7638 0.0274 Mean Horizontal Arc (deg) all branches Coleoptera:Mycteridae:Trichosalpingus TFIC sp.01 J.d * -0.7303 0.0397 0.8452 0.0082 Mean Horizontal Arc (deg) all branches Diptera:Mycetophilidae YDB sp.2 K 0.7326 0.0387 0.7279 0.0406 Mean Horizontal Arc (deg) live only Araneae:Grouped * -0.8193 0.0128 0.8333 0.0102 Mean Horizontal Arc (deg) live only Coleoptera:Mycteridae:Trichosalpingus TFIC sp.01 J.d * -0.7825 0.0217 0.8452 0.0082
Table 11.7. Spearman’ s rho rank correlations between 107 structural descriptors and 128 abundances of RTU in which both age classes exhibited a p-value<0.05
The significant rank correlations detected for abundance of each RTU are tabulated Table 11.7. Sixteen of 26 correlations had mismatched signs.
Coleopteran RTU responded to measures of crown airspace relative to trunk airspace, relative branch height, maximum branch airspace volume, total foliage, and horizontal branch arc. Dipteran RTU responded to relative branch height and ranges of dead branches, the standard deviation of live branch heights, average and maximum branch airspace volume, and vertical and horizontal arcs of live branches. Only a single Hemiptera RTU exhibited significant responses for both age classes. However, the signs of the correlations were mismatched.
Fulgoridae YDB sp. 3 was correlated with the mean airspace volume of dead branches. Hymenoptera responded negatively to the standard deviation of dead branch airspace volume, and positively to the average scaling ratio of all branches. A lepidopteran, Oecophoridae YDB sp. 2, again showed significant but mismatched correlations, this time with measures of dead branch volume. Spiders exhibited 11) Correlation - Results mismatched responses. A gryllacrid cricket, Kinemania YDB sp. 1 responded negatively to the standard deviation of dead branch heights. 11.2 Multivariate Correlation results
11.2.1 NMS ordination results
Correlations a NMS ordination discriminated the strongest structural predictors of changes in the composition of the arthropod community (Figure 11.5). The unsquared correlation coefficient r is tabulated for correlations where r2 > 0.15 in Table 11.8. Structural descriptors with both positive and negative r-values for an axis are influencing the community in opposing fashions (Figure 11.6)
All RTU with >5 fauna and Structural Descriptors B t05 Age 100yr Old t03 C D t12 t04 A t06
t08 t10 t11 H
2 t02 s G t01 t1t059 Axi t14 t13 t16
E
F t07
Axis 1
A, Lowest branch height; B, Mean up arc of all branches; C, Mean vertical arc of live branches; D,Total foliage units; E, Max distance of dead branches; F, % of vector volume in dead branches; G,Mean slope of all branches; H, % total wood volume in trunk r2 cutoff = .250; length of rays is proportional to r2
Figure 11.5: NMS of all RTU with >5 individuals. Joint plot with axis- correlated structural descriptors where r2 >.250. Mean 3-d stress 9.094 11) Correlation - Results
Of the six most strongly correlated descriptors, four were measures of dead branch size and distribution. Other highly correlated descriptors included the average branch slope, the lowest branch height, the number of live branches, and the portion of wood volume in the trunk.
Influence of structural descriptors on taxonomic composition r correlation coefficient with NMS axis: where r2 >0.15; ranked by highest r2 Axis Structural Descriptor 1 2 3 Mean Distance (m) dead only 0.043 0.69 -0.029 Max Distance (m) dead only -0.126 0.673 -0.148 Lowest Branch Height (m) all branches 0.176 -0.559 0.147 Mean Slope (deg) all branches 0.023 0.157 0.545 Range Branch Height (m) dead only 0.061 0.54 0.11 % of Vector Volume in Dead Branches -0.326 0.51 0.176 Mean Slope (deg) live only 0.022 0.199 0.501 # Branches live only 0.471 0.128 -0.054 % of Wood Volume in Trunk -0.084 -0.463 0.384 Highest Branch Height (m) all branches 0.455 -0.142 0.317 Range Branch Height (m) live only 0.45 -0.109 0.002 Max Distance (m) live only 0.443 0.075 -0.055 Sum Polyhedra Volume (m3) all branches 0.439 -0.001 -0.171 Mean Up Arc (deg) live only 0.423 -0.34 0.212 Mean Up Arc (deg) all branches 0.418 -0.255 0.215 Sum Polyhedra Surface Area (m2) live only 0.417 -0.079 -0.076 Std Dev Cone Volume (m3) dead only -0.414 0.055 0.085 Std Dev Branch Height (m) dead only 0.035 0.375 0.412 Mean Polyhedra Surface Area (m2) dead only -0.407 -0.04 -0.323 Total Foliage Units 0.406 -0.204 0.001 Sum Polyhedra Volume (m3) live only 0.397 0.013 -0.009 Std Dev Polyhedra Volume (m3) dead only -0.395 0.131 -0.326 Sum Polyhedra Surface Area (m2) all branches 0.393 -0.081 -0.091 Max Polyhedra Volume (m3) dead only -0.387 0.069 -0.315 Mean Foliage Units live only 0.084 -0.387 0.174
Table 11.8: Structural descriptors correlated with NMS ordination of arthropod community for which r2>0.15; sorted by strength of correlation with any axis
Comparison of descriptors correlated along the same axis of arthropod fauna shows opposition and agreement in structural influence (Table 11.8) Organisms differentiated by axis 1 were influenced in one direction by auto-correlated 11) Correlation - Results descriptors of branch height, airspace size, foliage, vertical arc and live branch numbers, and influenced in the other direction by measures of dead branch airspace and size variation.
Dead branches were influential on another axis of composition. The length, range and portion of wood volume associated with dead branches influences taxonomic composition in opposition to the portion of wood volume in the trunk, lowest branch height, and average foliage units per branch. The standard deviation of dead branch height and the average branch slope were correlated with arthropod composition but were not opposed by any other measures.
# Branches live only Highest Branch Height (m) all branches Range Branch Height (m) live only Max Distance (m) live only Std Dev Cone Volume (m3) dead only Sum Polyhedra Volume (m3) all branches Mean Polyhedra Surface Area (m2) dead only Mean Up Arc (deg) live only Axis 1 Std Dev Polyhedra Volume (m3) dead only Mean Up Arc (deg) all branches Max Polyhedra Volume (m3) dead only Sum Polyhedra Surface Area (m2) live only Total Foliage Units Sum Polyhedra Volume (m3) live only Sum Polyhedra Surface Area (m2) all branches
Mean Distance (m) dead only Lowest Branch Height (m) all branches Max Distance (m) dead only Axis 2 % of Wood Volume in Trunk Range Branch Height (m) dead only Mean Foliage Units live only % of Vector Volume in Dead Branches
Mean Slope (deg) all branches Mean Slope (deg) live only Axis 3 Std Dev Branch Height (m) dead only
Figure 11.6: Structural descriptors correlated with NMS ordination of arthropod community for which r2>0.15.. arranged by axis and +/- direction. For each direction, structural descriptors are ranked by descending correlation.
11) Correlation - Results
11.2.2 Mantel test results
Table 11.9: Mantel test results for both age classes pooled and separately for each trap type+3
Mantel tests did not show a significant correlation in the structure and arthropod distance matrices for both age classes pooled, or for each age class separately (Table 11.9). The most probable correlation was for all traps in the 100yr trees (p=0.16), but its likelihood of true significance is contradicted by the least probable correlation for its parallel in the old trees or in both age classes pooled.. No pattern is visible when comparing trap types between age classes and pooled ages. For all comparisons between ages or trap types, mismatched signs and highly variable p- values were observed.
12) Correlation - Discussion
12) Correlation – Discussion
Observations from the correlation analysis were subjectively chosen for discussion. Summaries of entire taxa or entire trap collections were discussed. The possibility that structural traits are surrogate measures of other phenomena are is noted, and a possible causal link between the structural descriptors and arthropod response is proposed.
A table with a selection of correlations worthy of future investigation is presented. Amongst many correlations, it was found that trees with a higher portion of their total wood volume in the trunk rather than the branches had a higher diversity of arthropods. Trees with a larger range of dead branch girths had a less diverse arthropods fauna in the CD sticky traps. More species were collected in the hangtraps in trees with a larger range of live branch girths, a greater upwards arc, or a lower proportional average branch height (relative to total tree height). Trees with shallower crowns had richer Dipteran fauna. Arthropod composition was found to be correlated with measures of crown depth, dead branch size, number and foliage mass of living branches, total crown foliage.
12.1 The influence of crown structure on canopy arthropods of E. obliqua
Several correlations between structural descriptors and measures of arthropod biodiversity were identified. Future assessment of their accuracy and causal relationships will require further research. A distinction bias was present in the researcher’s choice of variables for analysis. Another subjective choice was made in determining what levels of significance to report.
With the number of correlations generated, it is impossible to address each one separately. If one measure of a structural category showed a significant result, it is beyond the scope of this analysis to check if related measures corroborate this correlation. It is possible that the decision to present results at p<0.001 or p<0.05 is hiding a strong pattern of correlations
12) Correlation - Discussion
12.2 Bivariate
Rank correlations used for comparing within each age class may have masked differences in measured structural traits. The old trees express greater variation in their observed structure than the 100yr trees. Ranking trees by a structural descriptor may have applied an even spacing between trees for both age classes, when in fact the old trees were spaced farther apart by the unranked measure. This may account for the greater than expected number of significant results in the old trees for summaries of richness and diversity.
It is more likely that arthropods within the different age classes will correspond to structural descriptors than to respond in opposition. When comparing a strong correlation in one age class to a weak correlation in the other, little weight should be given to the weaker correlation. A high p-value suggests a lack of pattern, rather than an opposite trend. Mismatched signs in combinations were p<0.001 in one age class are less important than mismatched signs when p<0.05 for both age classes.
However, more suspicion should be given to mismatched signs (+/-) with strong correlations in both age classes. These were considered to be most likely evidence of false positives, rather than of truly opposing trends.
12.2.1 All taxa, all trap types
Only one structural descriptor was identified as a predictor of a biodiversity for all taxa, in all traps. There is no known evidence of this relationship in the literature. In the old trees, a greater diversity was rank correlated with the portion of the total wood volume in the trunk. No pattern of correlation was observed in the 100yr trees. A possible explanation is that trees with larger trunks and smaller branches would exhibit a more diverse fauna could be the emphasis in trapping on the trunk. Trees with more trunk habitat than branch habitat may have space for animals that would otherwise be pushed out to the branches. They may have a more diverse fauna because there is more space for them to occupy. This would allow more habitats for a richer fauna, and more space for them to coexist without dominating with each other.
12) Correlation - Discussion
12.2.2 All taxa, one trap type
Correlations were identified for summaries of total diversity in the CD sticky traps, and for total richness in the hangtraps. No structural predictors were identified for the total taxa of funnel traps, or for the total abundance of any trap type.
The total diversity in CD sticky traps in both age classes was negatively correlated with the range of dead branch diameters. Significant, but sign mismatched correlations were found for total crown depth. The diameter ranges for dead branches show variation in branch size as well as the, and could also indicate a greater range of time since branch death. This contradicts Irmler et al.’s (1996) findings that age since death of coarse woody debris to influences the insect fauna. The causal relationship between the two variables is unclear.
The total richness in hangtraps was positively correlated with the mean angular arc upwards on branches in old trees, and with the range in live branch start diameter for both age classes. The average upwards arc could be a surrogate for branch elbowing or epicormic activity on the upper surface of the branch. The range in live branch diameters increases with the presence of large, generally rotten older branches and small, healthy epicormics. If these branch types support a distinct fauna, then the total richness in the tree would increase with variability in live branch sizes.
Hangtrap richness was also negatively correlated with the mean proportional branch height. All of the old trees had a lower mean proportional branch height (Figure 5.7). This was linked to an increased abundance of epicormic branches in the lower crown. This trend could be also driven by proximity to the arthropods associated with the middle strata of rainforest trees. Trees with lower proportional branch height would have lower hangtraps. These traps would be closer to nearby non-Eucalyptus rainforest trees, which support a distinct faunal community (McQuillan, 1993).
12.2.3 One order, all trap types
Ordinal level results for all trap types pooled resulted in few correlations. The diversity of Hemiptera rose with the average branch diameter in 100yr trees. The
12) Correlation - Discussion
phytophagous Hemiptera may be more associated with branches than trunks (CSIRO, 1967). Larger branches would allow more habitat space for these animals, and allow less interference between species (Floater, 2001).
Dipteran richness was negatively correlated with crown depth in both age classes. Trees with shallower crowns had richer fly fauna. It is possible that species of flies were more densely distributed in trees with shallower crowns, and that exposed traps were sampling this more crowded fly fauna. However, abundance of Diptera did not correspond with richness. Crown depth may represent a measure of scale which was not accounted for.
Spider abundance was negatively correlated with mean horizontal arc in the 100yr trees but positively correlated in the old trees. Horizontal arc may be a surrogate for complexity of branchlets. Spider abundance has been linked to structural complexity in several situations (Balfour & Rypstra, 1998; McNett & Rypstra, 2000; Rypstra et al., 1999). The cause of this conflicting trend is unclear. It is possible that the compressed values caused by rankings of the 100yr tree descriptor created a misrepresented trend in the 100yr trees.
12.3 Multivariate compositional predictors
The number, size, height, and total foliage of living branches was identified as factors in determining arthropod composition in opposition to measures of dead branch airspace and size. This could reflects changes in herbivore and saproxylic communities. Martikainen et al. (2000), Grove et al. (2002), and Schiegg (2001) have identified the presence of dead wood as a factor contributing to beetle species richness. These structural descriptors may also be surrogate for overall tree vigour.
The length and crown depth of dead branches, and the proportion of branch volume in dead branches, was also determinant factors in arthropod composition. This composition trend was opposed by the average amount of foliage on each live branch, the lowest branch height, and the portion of wood volume in the trunk. In the bivariate correlations, the portion of wood volume in the trunk was also linked to a higher diversity of arthropods. None of these factors were significantly different between age classes, and therefore these measures are unlikely to be a surrogate for age classes.
12) Correlation - Discussion
Like axis 1, the average foliar units per branch could be a measure of vigour in opposition to the amount of dead branches. In E. obliqua forests on the mainland, Oliver et al. (2000) identified the number of stems, altitude, and litter depth as major determinants of ant and beetle composition. It is possible that the number of stems in these forest is analogous to the number of branchlets, or foliar units, in E. obliqua crowns.
12.4 Surrogate measures
In the context of the current study, it is impossible to fully test the veracity of correlations and to determine the causal mechanisms behind them. It is possible that the compositional changes are actually being driven by cryptic factors that are correlated to the structural descriptors. For example, measures of foliage abundance could be a surrogate for vigour or soil nutrient quality. Wormington et al. (2002) found the height of the tallest trees to positively influence the richness and abundance of marsupials in Queensland eucalypt forests, but comment that these may be reflecting a greater site productivity. Similarly, the correlation with the NMS ordination may actually be surrogates for the age class distinctions.
12.5 Applicability
It must be emphasized that these results are exploratory only. Although the structural predictors and arthropod responses were as comprehensive as possible, replication may have been inadequate. In data mining situations where many variables are tested with few replicates, doubt will always remain. The combination of structural descriptors with arthropod responses was the natural outcome of the crown structure and canopy arthropod measurements. Future analysis of the results at a placement or trap-level may offer more insights into the relationships between the two data sets.
It is clear that some arthropods respond to some structural descriptors. A selection of bivariate correlation is given in Table 12.1. Summaries of arthropod biodiversity responds to structural descriptors such as the portion of wood volume in the trunk, crown depth, start diameters, and upwards arc, but the causal link is
12) Correlation - Discussion unknown. The composition of fauna responded to several structural descriptors, and the direction and axis that they lined up on offers insight into their relationships. Results from the Mantel tests show that the suite of structural descriptors used in analysis, do not determine en masse the composition of arthropods. It is hoped that future researchers will be able to use these as suggestions for more detailed measurements of structure and more targeted taxa of interest.
Group (+/-) Structural Predictor Summary Arthropod response Old trees with a higher % of total wood volume in the trunk had a more diverse total arthropod fauna Trees with less crown depth had a richer Dipteran fauna Trees with a narrower range of dead branch starting diameters had a more diverse sticky trap catch Trees with a wider range of live branch starting diameters had a richer hangtrap catch Old trees with greater mean upwards arc had a richer hangtrap catch Trees with a lower mean branch height as % of total height had a richer hangtrap catch
Table 12.1: A selection of statements regarding some of the observed correlations between structural predictors and arthropod biodiversity in the sixteen studied E. obliqua.
13) Conclusions
13) Conclusions In this section, the study aims are listed and an assessment is made of their fulfilment. Suggested avenues for future research are identified. The conclusions and implications follow and close this document. 13.1 Mapping Aim 1: Quantitatively assess the differences in crown structure and size between 100yr and old E. obliqua The collected data is the most comprehensive data set currently known comparing the crown structure of mature and old-growth Eucalyptus, and the most comprehensive analysis known of tree level structural descriptors for any species.
The structural descriptor variables illuminate several differences in crown structure between these age classes. Tree level analysis is sufficient to distinguish them and offers a framework for understanding the branch-level development in crown structure of E. obliqua.
Branch mapping allows future researchers a structure to plan studies within the same trees. Just as a map of a city allows a researcher to study its development, or plan their field trip, so can a map of a tree.
Structural descriptor variables are only approximations of the detail within the collected data. Continued analysis at a tree level can continue to compare ratios of descriptors between ages. For example, Jacobs (1955) comments that old crowns have approximately twice the foliage of a mature crown. A similar ratio was observed in the total number of foliar units within the study trees. But how does that doubling compare to other measures of tree size and vigour? The crown airspace of old trees was also doubled between age classes, but the total wood volume increased seven times between ages. This “uncoupling” of growth rates sheds light on resource allocation and growth dynamics for E. obliqua (Lusk et al., 2003).
Future analysis at a branch level could further illuminate differences in crown dynamics, and provide quantitative material to model the competition of branches within the crown, after Ishii &McDowell (2001). Curtin (1970) and Attiwill (1962) formulated several allometric formulae for young E. obliqua, but they did so at a time when old trees were considered “useless veterans” (Jacobs, 1955), and little or no distinction was made between natural and silvicultural regeneration. They did not
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address old-growth trees in their studies. The next step for future research on Eucalyptus is to study more than just two age classes, after Van Pelt &Nadkarni (2004a) and Ishii & McDowell (2001).
Future comparisons to the collected crown structure information can be performed in the same trees at the LTER. It is likely that the study trees will outlive both the author and the readers of this document. At a later date, observations of the change in branch structure can assist in creating models of branch occlusion, crown senescence, secondary crown development, and other processes of growth and decline in E. obliqua. Furthermore, the structural data offers a ready source of comparisons for other data sets quantifying the architecture of Eucalyptus (Curtin, 1970; Kelly et al., 2004; Van Pelt et al., 2004) or other forest trees (Clement et al., 2001; Clement & Shaw, 1999; Ishii & Wilson, 2001; Sillett, 1999). 13.2 Mapping Aim 2: Investigate the presence of structural features in the crowns of 100yr and old E. obliqua The descriptions of structural features in the present study can only offer the most basic hints to the occurrence and formation of these features. The current project was hindered by the selection, access, and distinction biases. It was impossible to photograph or record every feature which attracted the attention of the field workers, and difficult to fit them into simple categories. Each phenomenon is worth investigating on its own. Tree hollows, in particular, have been studied in great detail. Gibbons & Lindenmayer (2002) have written a book reviewing their value for wildlife conservation. Researchers in Tasmanian E. obliqua at Warra LTER have focused on vertebrate and invertebrate use of hollows (Harrison, 2004; Koch, 2004).
However, few, if any, projects have investigated the development and faunal associations in E. obliqua of features such as dead tops, snapped main stems, burls, bark strip flakes, or flaky armpit bark. It is possible that these features support a distinct animal, epiphyte, fungal, or micro- organism community. By photographing and recording the presence of these features, future researchers can both consider them in their studies, and know which trees they can be found in. A future research project to assess the animal communities associated with these features can use the information in this document to mitigate difficulties in selection and distinction.
13) Conclusions
13.3 Mapping Aim 3: Modify the conifer mapping technique of Van Pelt et al. (2004b) for Eucalyptus trees A crown- mapping technique developed for use in the coniferous forest of the Western United States is published by Van Pelt et al. (2004b). This protocol was trialled in Eucalyptus forests in Victoria. In addition to the crown-mapping methods, a technique for quantifying forest stand structure and foliage distribution is described. Their crown mapping methods, as described, were modified for mapping the crowns of E. obliqua in the present research. Pocometric principles (Takenaka et al., 1998) were used for 3-dimensional surveying of vectors. The techniques developed were directly descended and inspired from the conifer-mapping methods but were changed in several ways:
a) Using spherical vectors with relative anchor points eliminates the dependence on a fixed vertical axis from the origin for cylindrical coordinates. Vectors branches are measured in pocometric space from their starting point, independently from any other points. Cylindrical coordinate systems do not work for all trees. Trees with a strongly leaning trunk do not have a central axis. In trees with contending branches, trunk snaps, or suppressed leaders, the central trunk axis may not be present above a certain point. It is unclear in their methods if azimuths and distances are always fixed to the central axis, or if they are recalculated relative to their parent vector.
b) The adoption of a pocometric spherical coordinate system instead of a horizontal extent allows for a more intuitive measurement of a branch than a cylindrical coordinate system, and easier virtualization using CAD software. The length of a branch can be interpreted immediately, whereas the horizontal extent of a branch must be trigonometrically combined with slope to interpret the length of a branch.
c) Asymmetrical foliage distributions around the measured branch are addressed by separate angular measurements of arc for down, up, left and right, rather than by a symmetrical depth and spread.
13) Conclusions
d) Using spherical instead of cylindrical coordinates eliminates the need for an expensive clinometer and laser rangefinder package with internal trigonometric functions.
e) Trunk anchor azimuths are recorded to represent non-radial branches.
f) Branches are defined with reference to resolution levels, following the advice of Moffett (2000). The distinctions between limb segments, branch segments, and branches are removed, as truly reiterated secondary trunks do not exist in Eucalyptus. In these trees, the architecturally reiterated sub-unit of Halle (1995) is the foliar unit, which topologically is below the resolution level of both Van Pelt et al. (2004b) and the present research.
g) A system using two levels of resolution allows statistically valid comparison between branches- the definition of the trunk allows for a standard definition of a branch. In Van Pelt et al. (2004b)’s context of studying foliage and wood volume, counting parent and children branches as distinct units may be justified, but misrepresents the actual topology and confounds branching generations.
h) The limb segment-branch topology naming system is abandoned and replaced with a system based solely on observed branching generation and not by the characteristic of reiterated secondary trunks.
Researchers wishing to map angiospermous trees may benefit from these modifications. However the mapping technique used in the present project was not as effective as other researchers’ in some aspects.
i) The biomass, leaf area, and leaf count of foliar units was not calibrated to each tree.
j) The tools used for measuring slope and distance (clinometer, Leica Disto laser and tape measure) were less precise than the
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combination clinometer/laser (Impulse) used by Van Pelt et al. (2004b).
k) In the present study, a separate data field was not utilised to record whether a branch was original or epicormic. 13.4 Mapping Aim 4: Develop a technique for displaying crown structure of forest trees using computer models based on 3-dimensional spherical coordinates The study trees are individual organisms that have survived a century or more in a far corner of the world. In mapping, photographing, and discussing these trees, the unique shape of each tree is recorded. The old trees are a record of half a millennium of growth, and a picture of their form offers a link to the past. The arborograph models are presented in an Appendix 1. In addition, throughout this document photographs, focused arborographics, and text descriptions are provided to assist in perception of the variability in branching architecture and crown dynamics. The use of a computerized measurement of visual information as a surrogate of tree complexity is unknown in ecological literature
The virtualization of plant structure is a vibrant field. Several strands of thought are being reconciled: mapping with modelling, plant physiologist with computer scientist, and ecologist with agriculturalist (Godin, 2000; Godin et al., 2004; Hanan & Room, 1997). The present work contributes to the field by refining existing techniques of crown mapping (Van Pelt et al., 2004) and offering detailed instructions for virtualization in a computer aided design environment. This method was applied in trees of exceptional size and the arborographs presented are currently the most advanced known virtual representations of trees of their size.
The 3-d computer arborographs represent a parallel technique to the established methods of photographs and hand-illustration (Pakenham, 1996; Van Pelt, 2002). All three techniques show a tree in a different, complementary, way. Drawing trees is an ancient practice, and computer graphics offer a modern approach to precisely displaying an actual tree. Future uses and improvements of the data set and virtualization could include:
a) the generation of a software tool to eliminate the need for manual data conversion
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b) an improved depiction of Eucalyptus foliage units
c) graphic animations of the arborographs, such as spinning around the trunk
d) a satisfactory solution to the problem of depicting little traps in large trees
e) posting of the arborographs on the internet with 3-d view control, photographs, structural data, and arthropod trap collection data. Available at http://www.geog.utas.edu.au/yoav
f) submission of the structural data set to the International Canopy Networks Big Canopy database at http://canopy.evergreen.edu/bcd/
The data archive can be viewed at http://scidb.evergreen.edu/databank/studycenter/ydbtasmania
Work with the International Canopy Network in Olympia continues to further develop 3-d visualisation tools. 13.5 Mapping Aim 5: Expand Jacobs’ (1955) theory of intra- branch competition in Eucalyptus saplings to mature and old- growth E. obliqua Integrating Halle’s (1995) concept of competing branches with Ishii & McDowell’s (2001) analogy of branch age to tree age, Jacobs’ (1955) illustration of sapling branch dynamics was extended to mature and old-growth Eucalyptus. At 100 years of age, crown structure is formed by similar dynamics to a sapling less than ten years old. At some stage between 100 and 500 years of age, E. obliqua no longer follows a regular pattern of upper original branch growth and lower crown mortality . Branch mortality from catastrophic crown fires or gradual branch senescence is balanced by the release of epicormic buds within the crown.
The concepts outlined in the model of crown structural development in old trees will be recognized by those familiar with Eucalyptus trees. However, no previous study is known that has addressed these dynamics for this genus in writing since Jacobs (1955). In addition, the computer 3-d arborographs offer empirical, visual evidence of Jacobs’ (1955) theoretical line drawings. This knowledge could be useful to arborists seeking to preserve individual Eucalyptus tree. Also, foresters
13) Conclusions seeking to conserve biodiversity by managing for old-growth crown structures can gain insights from the analogy of branches to competing trees of different ages. 13.6 Mapping Aim 6: Generate a predictor data set to explore the influence of crown structure on canopy arthropods In tandem with Trapping Aim 4, the descriptor variables used for analysis of crown structural development can be used as a predictor data set to explore the influence of crown structure on canopy arthropod biodiversity. Mopper et al. (1991) describe how herbivory can influence the structure of plants. While outside the scope of this study, the crown structural descriptors are also response variables to an unmeasured set of environmental and experiential predictors. 13.7 Trapping Aim 1: Determine what differences exist in arthropod biodiversity between 100 year old and old growth Eucalyptus obliqua The results of the present study are the first known comparisons of Eucalyptus canopy arthropod biodiversity associated with different aged trees. Several differences in abundances, richness, diversity, community structure, and composition have been described.
After extensive literature search, only a single publication was found addressing the arthropod community of native Eucalyptus forests, or trees of different ages. Abbott et al. (1992) looked at foliage arthropods of sapling and resprout E. marginata, but did not address results at a tree level. No value of foliage mass per tree was given to allow scaling up. In their paper, this study is one of four concurrently reported projects. In an article spanning eleven pages, it receives exactly two paragraphs and one table. No other research project has addressed age-related changed in Australian Eucalyptus.
Old-growth forests are being replaced at an accelerating rate with young eucalypt plantations. Studies addressing the changes in biodiversity associated with this practice are urgently required to assess and mitigate its impact.
Concurrently, at Warra LTER, other research projects have looked at E. obliqua of similar age classes to those of the present study. Harrison (2004) has investigated the interior habitats of live Warra E. obliqua immediately after cutting
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them down, and Yee et al. (2001) has investigated rotting logs of different ages and sizes (Bashford et al., 2001). 13.8 Trapping Aim 2:Contribute to the knowledge of Eucalyptus canopy arthropods Very little is known about the canopy arthropods of Eucalyptus (Majer et al., 1997), despite it being one of the most prominent tree genera in the world. Ecologically, economically, socially, and spiritually, Eucalyptus typifies the Australian continent. Despite this, surprisingly few researchers have studied the arthropods in their crowns.
Furthermore, little work has been done on the arthropod communities of very old Eucalyptus, living or dead (Grove et al., 2002; Grove & Bashford, 2003; Majer et al., 1997; Yee et al., 2001). In the politically and economically prominent wet sclerophyllous Eucalyptus forests of Tasmania, only Grove et al. (2002) has sampled canopy arthropods from old-growth Eucalyptus.
Beyond the comparison between tree ages analysed in this document, the data collected can be used to address other questions about Eucalyptus arthropods, beyond the scope of the current thesis document. What differences exist between upper and lower crowns? Does the trap collection overlap with the canopy fogging at Warra (Bashford et al., 2001)? How much overlap is there with the beetle collections from felled trees (Harrison, 2004)? Do dead branches have a distinct fauna from live branches?
Voucher specimens of all animals will be archived in the Tasmanian Forest Insect Collection at Forestry Tasmania in Hobart, donated to the Australian National Insect Collection in Canberra, or sent to interested taxonomists. Voucher photographs will be packaged with raw and processed data and made available electronically. 13.9 Trapping Aim 3: Develop robust, inexpensive trap designs suitable for transport to and use in E. obliqua The present research is the first available data from passive trapping in the upper crown of Eucalyptus trees. Two other projects have used passive traps in eucalypt crowns, but unfortunately no results are available at the present time. Larson (2004) placed sticky and flight-intercept traps in the crown of E. grandis in
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subtropical rainforest, and the author of this document placed sticky traps in the crowns of E. regnans in Wallaby Creek, Victoria.
The traps designs presented here are inexpensive, robust, and simple to make. The concepts behind the designs have been tried and tested in Eucalyptus trees and on ropes in the canopy of other forest trees (Basset, 1991; Basset et al., 1997; Bickel & Tasker, 2004; Majer et al., 2003; Wilkening et al., 1981). By presenting details of construction and placement, and by tabulation of the time, materials, and monetary effort involved, it is hoped that other researchers will find them useful and appropriate for their studies. In particular, the use of CD cases for sticky panels offers an easy way to transport these notoriously messy traps.
A word of caution to any research intending to place traps while climbing on ropes in trees: great care must be given to the sequence of events and the risk of dropping items. The placement and timing of labels, hammers, nails, tape, bottles, and any other elements must be planned meticulously. Lanyards need to be attached to any critical tools that must not be dropped. Important items, like carefully printed trap labels, are worse than useless when they are drifting through the canopy in the wind. Ink labels may dissolve in alcoholic trapping fluids. Small items such as bottlecaps and pens can make climbing into the tree useless. For example, in the pilot study, a staple gun was used to anchor cardboard sticky traps to the stringy bark. Reloading the gun required releasing a spring load staple cartridge. If this was not done carefully, it would have be very easy to launch the staple cartridge right out of the tree crown! 13.10 Trapping Aim 4: Generate a response data set to explore the influence of crown structure on canopy arthropods A response data set at a tree level was generated using three sets of summaries and the abundances of each RTU. For each of the biodiversity aspect summaries of abundance, richness, and diversity, the 24 values for alltraps +3 and alltaxa +5 were tabulated. The abundances of the 118 RTU with abundances greater than five were tabulated. These 190 variables were the response variables to the 107 descriptors of crown structure.
Carey (1996) points out that any attribute of the arboreal ecosystem could be considered of import. The sum total interactions involved in the Eucalyptus treetop
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ecosystems are vastly more complicated, stochastic, and variable than we can imagine. Exploratory research will never provide satisfactory and clear answers as to what is going on up there, but can only offer a few tantalizing clues.
The results of the exploration can guide future researchers in planning studies targeting a narrower range of structures or taxa. Despite the limitations inherent in the automated comparison of the data sets, it offers useful insights into the influence of crown structure on the canopy arthropods in E. obliqua. No comparable work is known for arboreal invertebrates. 13.11 Conclusions: Answering the Research Questions
1) What are the structural differences between 100 year old and old- growth E. obliqua, and how can they be measured and displayed?
Quantifiable structural differences exist between the 100 year old and old- growth E. obliqua at Warra LTER. The old trees have more variable crowns and a greater abundance of recognized structural features. Different dynamics of branch competition determine their crown structure. Intra-crown branch competition in old- growth E. obliqua trees is distinct from the mature 100 year old trees. Old trees have either senescent primary crowns or resprouted epicormic crowns, whereas 100 year old trees only have mature young crowns.
2) How is the canopy arthropod biodiversity different in 100 year old and old-growth E. obliqua?
High levels of arthropod biodiversity were found in the crowns of both 100yr and old growth trees. More than 300 recognizable taxonomic units were collected in only 3 months in only sixteen trees. Because spider, mites, and ants were unsorted, this value is certainly an underestimate. Furthermore, the traps types undoubtedly failed to collect animals that are sessile, only in the outer crown, or living inside the tree.
Evidence was collected that suggest higher levels of arthropod biodiversity exist in the crowns of the old-growth trees. The composition of arthropods collected in the crowns was different between the age classes. Changing the demographics of
13) Conclusions
the forest by replacing multi-aged stands with even-aged ones will impact the arthropods associated with both of these age classes. While only sampling two points in the lifespan of E. obliqua, the results of this study indicate the old growth trees support a greater and different arthropod biodiversity than the 100 year old trees. This suggests, in agreement with the literature for vertebrates, that older Eucalyptus trees sustain a greater and different arthropod biodiversity than younger trees.
3) In what ways does crown structure influence arthropod biodiversity in E. obliqua?
The arthropod biodiversity responses to tree structural descriptors was explored. Several correlations between structural predictors and arthropod responses of composition, abundance, richness, and diversity are identified and explored. These are presented as promising avenues of future investigation.
The structural results show that trees one century in age have yet to develop characteristically old-growth crowns, and by extension are probably not supporting a characteristically old-growth fauna. It is important not to forget that the old-growth trees potentially represent a larger range of ages than the 100yr trees. It is possible that the crown fire event that differentiated the old-growth crowns also impacted the arthropod fauna. Future analysis of the data will test the hypothesis that senescent primary crowns and resprouted epicormic old-growth crowns have different arthropod biodiversity. Future research linking the presence of structural features to arthropod biodiversity will be invaluable in assessing the impact of losing these potential keystone structures. An important direction for future research in the canopy of Eucalyptus forests is to look at even younger trees (i.e. within the planned silvicultural rotation of 80-100 years) in comparison with these two age classes.
Rank correlations between crown structure and arthropod biodiversity identified potential structural predictors of arthropod responses. Trees with a higher portion of their total wood volume in the trunk rather than the branches had a higher diversity of arthropods. Trees with a larger range of dead branch girths had a less diverse arthropods fauna in the CD sticky traps. More species were collected in the hangtraps in trees with a larger range of live branch girths, a greater upwards arc, or a lower proportional average branch height (relative to total tree height). Trees with
13) Conclusions shallower crowns had richer Dipteran fauna. Arthropod composition was found to be correlated with measures of crown depth, dead branch size, number and foliage mass of living branches, total crown foliage. The causal relationships behind these correlations remains to be tested by future investigation.
13.12 Future directions Very little research has been performed in situ in the Eucalyptus forest canopy. Very few projects have mapped tree crowns in detail. Very little is known of the biodiversity living in the Eucalyptus canopy. No other research is known for any plant species linking detailed crown structural mapping with a survey of canopy arthropod biodiversity.
Promising avenues for scientific research include investigations of the detected correlations between crown structure and arthropod biodiversity, the linking of structural features to animal communities, the mapping of crown structures in forest trees, and the study of canopy invertebrates. Eucalyptus forests are of world significance and all people would benefit by learning more about these magnificent trees. By providing branch structural data, information about structural features, 3- dimensional tree maps, arthropod voucher collections, suggestions on trap designs and placement, and access cords in place for the study trees at Warra LTER, it is hoped that other researchers will climb into these magnificent trees and benefit from the results of this exploration.
13) Conclusions
Please visit the WWW for more resources, information, and access to raw datasheets:
Project web page:
http://www.geog.utas.edu.au/yoav
Data Archive at the International Canopy Network:
http://scidb.evergreen.edu/databank/studycenter/ydbtasmania
Future correspondence:
14) References
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