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Dendropoma Maximum Presence and Distribution across Three Reef Systems

How environmental factors influence D. Maximum density in Palaun Reefs

Project Brief
Dendropoma maximum, sessile mollusks that feed by extending mucus nets to capture plankton, are common across certain reef systems in Palau. Limited research on the species suggests that these mollusks negatively impact coral health by reducing water flow and competing for resources. Their distribution patterns and biological interactions with the surrounding reef system remain poorly understood. As such, it is not possible to predict how they'll affect reef ecosystems under changing ocean conditions. This study aims to shed more light on how these mollusks interact with their aquatic environment.

In this study, we ask a key question: Which water quality parameters drive the presence or absence of D. maximum on a reef?

Together with a research partner, we designed and carried out an experiment to better understand the environmental factors affecting D. Maximums distribution across different Palaun reefs. We collected environmental and biological data across three reef sites with varying flow regimes. These responsibilities included identifying which environmental parameters to track and study, deploying oceanographic sensors, conducting quadrat surveys of coral-associated D. maximum, measuring individual organisms, and performing  statistical analyses to identify correlations between environmental conditions and D. maximum distribution patterns.

Hypothesis

We hypothesized that reefs with higher flow would support higher densities of D. maximum because of the increased availability of food sources carried by ocean currents. This would ultimately mean that D. maximum would be in competition with the coral reefs as they vie for the same food.


Site 29 [Minimal flow]
Cemetery Reef [Moderate Flow]
Tabkukau [Highest Flow]



Data Collection
In each of the 3 sites, we measured environmental factors using these oceanographic instruments:

  • TCM (Tilt Current Meters): measured current velocity (flow speed)
  • SBE 56 Temperature Logger: captured thermal profiles
  • RBR SOLO pressure sensors: monitored water depth and wave activity
  • MiniDOT sensors: measured dissolved oxygen concentrations
  • iSAMI monitors: recorded pH levels


We conducted systematic quadrat surveys on approximately 20 porites lobata coral colonies. For each quadrat, we:

  • counted D. Maximum individual organisms

  • measured the diameter of up to 10 individuals per quadrat (as a proxy for age/size)

  • documented coral color morph (purple or yellow)

 Environmental Patterns

[processed in Matlab and RStudio]

Figure 1. Speed of flow across sites over time
Figure 2. Dissolved Oxygen Content across sites over time
Figure 3. Tempertaure across sites over time
Figure 4. pH levels across sites over time

D. Maximum Density Patterns 
Figure 5. Cemetery Reef vs Site 29: p = 0.031 (significant), Tabkukau vs Site 29: p = 0.0028 (highly significant), Tabkukau vs Cemetary Reef: p = 0.88 (not significant)
Figure 6. Live coral vs Dead coral: p = 0.046 (significant)

Figure 5 gave us significant information because site 29’s near absence of D. Maximum was statistically distinct from the other two reef sites. However, the similar densities at Tabkukau (Lighthouse) and Cemetery, despite their different flow regimes, suggested that other factors may limit D. Maximum populations beyond a certain flow threshold. Our findings here were significant enough to suggest that D. Maximum prefer to settle on live corals rather than dead ones.
Multivariate Analysis
Figure 7. Non-metric multidimensional scaling (NMDS) on the complete datastet. This ordination positioned each site in multivariate environmental space based on temperature, dissolved oxygen, pH, flow velocity, and flow speed.







This NMDS graph confirmed that Site 29 (low flow) was fundamentally different than the other two sites (Tabkukau and Cemetery) across all measured parameters. This made it difficult to isolate which specific factor drove the absence of D. Maximum.
Conclusions and Limitations

Environmental Drivers:  Temperature and dissolved oxygen were the strongest correlates with D. maximum distribution. While flow clearly differed between sites, the relationship between flow and density wasn't straightforward, likely because flow affects multiple factors simultaneously (food delivery, larval settlement, waste removal, thermal regulation).


Confounding Variables:  With three sites and limited temporal sampling, this study provides valuable exploratory insights but has constraints on causal inference. Site 29 proved to be an outlier across all measured variables, making it difficult to isolate individual environmental drivers. Future work would benefit from additional sites across the flow gradient and longitudinal monitoring to capture seasonal dynamics.

Learnings:  Site selection and adequate replication of independent variable types are of utmost importance to ecological research. The statistical analysis phase of the project further revealed to us the complications of disentangling various correlated environmental variables with limited samples. Working with oceanographic instruments in a remote field setting taught me the value of resilience and adaptation in field research as one has to deal with changing environmental variables, equipment failures, and the whims of mother nature.