Incorporating statistical analysis in Marine Biology dissertation research

[HaroldJimen]

In the vast expanse of the deep sea, amidst the intricate dance of marine life, I found myself facing an unexpected revelation. While conducting research on the biodiversity of deep-sea ecosystems, my focus shifted towards the incorporation of statistical analysis in my dissertation.

I have always valued observation and endurance in my work. However, navigating the complexities of statistical methods in the realm of Marine Biology has proven to be a challenging yet necessary endeavor. During my field expeditions and data collection at sea, I have encountered significant environmental variability that begs for a deeper understanding through quantitative analysis. As I delve into oceanographic methods and marine ecology,

I realize the crucial role that research design plays in unraveling the mysteries of these ecosystems. How can I effectively integrate statistical analyses to enhance the robustness of my findings without losing sight of the qualitative essence of my research? As I embark on this academic journey, I seek guidance on striking a balance between scientific rigor and the exploratory nature of my work.

 

[FraankChaver]

Harold, your post captures exactly what every deep-sea researcher grapples with. The abyss doesn't give up its secrets easily, and the variability you're seeing IS the story – you just need the right tools to tell it.

Practical recommendations from recent methodological advances:

For spatio-temporal environmental variability: Look into models that incorporate both space and time as correlated processes. A recent UW dissertation on walleye pollock developed spatio-temporal models linking fish demographics to environmental conditions – exactly what you're describing .

For non-linear relationships: Marine systems rarely behave linearly. Generalized Additive Mixed Models (GAMMs) are becoming standard because they let the data reveal complex patterns without forcing preconceived shapes . The otolith chemistry literature has excellent examples of this.

For integrating different data types: If you're combining traditional survey data with opportunistic observations, recent work on dolphin distribution modeling shows how to account for different detection probabilities across data sources .

For categorical questions: Sometimes discretizing continuous variables (high/low, present/absent) helps communicate findings to managers – there's AIC-based frameworks for finding optimal thresholds .