Logging effects on liana diversity and abundance in Central Guyana

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<strong>Logging</strong> intensity and liana diversity 0.65 III I 0 -0.65 0 0.65 Axis 4 II C RIL 4 RIL 8 RIL 16 -0.65 Axis 2 Figure 3.14 Plot onfAxis-2 and Axis-4 scores based on canonical correspondence analysis of logging experiment in Pibiri. Open symbols are plots in post-harvest census, closed symbols in the pre-harvest census. Boxes enclose plots located in the same Block (in Roman numerals), arrows indicate membership of plots that are not enclosed by the boxes. 3.2.9 Impact of logging intensity on species distributions The quite clear separation of plots by census and treatments over Axes 1 and 3 enables identification of the species that are responsible for this partitioning. As Axis 1 clearly separated the heavily logged plots in the post harvest census from the pre-harvest census and lightly harvested plots, species with high positive scores on this axis can be expected to be indicative of logging-related habitats. The quotient of post and pre-harvest abundance (adjusted by 1 to cater for absence of a species in one of the censuses) is strongly exponentially related to the Axis-1 species scores (r=0.85, p
<strong>Logging</strong> <strong>effects</strong> on liana diversity and abundance in Central Guyana Axis 3 scores are also typical for those few species that in spite of a general decrease in abundance showed a “preference” for the heavily logged plots. This pattern is hard to explain unless it survived from pre-harvest distribution patterns. The species showing the highest positive scores on Axes 1 and 3 are listed in Table 3.9. As the correlations of trends in abundance with these axes is not perfect, the interpretations of the axes as given in the table do not always hold in the same measure for all species mentioned. Separation of plots and identification of species based on preference for logging-related habitats (such as gaps or skidtrails) is not possible at the plot level. An analysis at the subplot level is needed for that. Table 3.9 List of species with high absolute scores on axis 1 and 3 of canonical correspondence analysis performed on the logging experiment in Pibiri and tentative interpretation of their response to logging. Species are listed that were present in at least 5 (of 24) plots with an abundance of at least 4 individuals/ha in the pre or post-logging census. Sample A was used. Axis 1 score Axis 3 score Species * > +1.5 < 0 PRIO ASPE, HIRA AFFI, CLYT SCIU, PINZ CORI, PASS GLAN, BAUH GUIA, ANEM OLIG, ANEM PARK, PAUL CAPR > +1.5 > 0 HETE MULT, MALP SP5, STIG SINU, DICH RUGO, PASS KAWE, COCC MARG, MACH QUIN, SMIL SCHO Interpretation Strongly increased, concentration in heavily logged plots (RIL 16) Strongly increased, concentration in moderately and heavily logged plots (RIL 8 and RIL 16) any < -1.5 DIOS DODE, PAUL PACH, CLUS PALM, SECU SPIN No or declining trend in abundance, concentration in heavily logged plots (RIL 16) any >+1.5 MACH MYRI, CYDI AEQU, DOLI BREV, MARI SCAN, MEMO MORI, CLYT BINA, DICH PEDU, FORS SCHO < -1 any TETR VOLU, CLUS GRAN, ARIS DAEM, PLEO ALBI, CAYA OPHT * For acronyms of species, see Appendix B. Somewhat increased, concentration in moderately and heavily logged plots (RIL 8 and RIL 16) Strongly decreased 3.2.10 Habitat <strong>effects</strong> on species distributions The scale that was used in the canonical correspondence analysis of species composition above, the level of plots, was too large to study the relation between habitats or other site factors and the occurrence of species. These habitats vary at a much smaller spatial scale than the plots, causing “dilution” of trends caused by preferences of species for certain habitats or other site factors in plot-level analysis. In order to assess such relationships, the analysis was repeated at the level of subplots. For each subplot it is known whether it is located on a skidtrail or in a gap 6 , and if species respond to these conditions, it is expected that the analysis differentiates plots by species with preference for certain habitats, and species by their distribution over plots with specific habitat characteristics. The analysis is not perfect – in many cases several habitat types cooccur in the same subplot, causing a reduction in the power of this analysis. The disadvantage of using the species composition of small subplots to characterise highly diverse plant communities is that each subplot “samples” just a small proportion of this community (see also section 3.1.2). This introduces a large sampling effect. The probability for subplots equal in site conditions to have the same species growing in them will be low if the number of species is high and the number of individuals per subplot is low. As a result, by 6 Except for Control plots, see methods 42
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Logging effects on liana diversity and abundance in Central Guyana

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