Does anyone recommend a good book on data analysis (with R) in the field of "applied" ecology? I am proficient with R and ecological data analysis overall, but I need some references for helping me transition from solely academic/theoretical ecology to conservation and management. Basically, I need to go from theoretical statistical significance to biologically meaningful decision making, predictions, and forecasting. For example, I recently read about loss functions and how they are used to go from statistical inference to decision making. Other examples include (but not limited to): forecasting population sizes as a function of different management scenarios; prioritizing conservation or management importance of different sites; or just going from model coefficients to easy-to-understand values that management organizations can use to make actual decisions.
To be clear, I can figure out most of these things myself based on my current background (and it's fun to!), but: 1) it is a lot of extra time, 2) I don't want to reinvent the wheel, 3) there are probably standardized protocols that are generally "accepted" by applied ecologists, and somewhat conforming to those standards will help with publications.
Note: I've found several books that each focus on particular topics (e.g., fisheries modeling, spatial analysis, survival analysis, forecasting, etc.) but I am looking for something with perhaps less depth and more breadth on applied ecology techniques in general.
EDIT: My interest in this is mostly around plant ecology and conservation, but a resource that includes topics on wildlife management analysis would be helpful too.
Thank you in advance!
This one is on using a Bayesian approach if you would like to consider it:
https://www.amazon.com/Bayesian-Models-Statistical-Primer-Ecologists/dp/0691159289/ref=asc_df_0691159289/?tag=hyprod-20&linkCode=df0&hvadid=312091458201&hvpos=1o1&hvnetw=g&hvrand=8847150229127317848&hvpone=&hvptwo=&hvqmt=&hvdev=c&hvdvcmdl=&hvlocint=&hvlocphy=9007834&hvtargid=pla-514676907837&psc=1&tag=&ref=&adgrpid=63669393113&hvpone=&hvptwo=&hvadid=312091458201&hvpos=1o1&hvnetw=g&hvrand=8847150229127317848&hvqmt=&hvdev=c&hvdvcmdl=&hvlocint=&hvlocphy=9007834&hvtargid=pla-514676907837
It has good step-by-step guidelines with specific examples from different ecological topics (the breadth you mentioned) so you can see something closer to your data and go as deep as you think it is needed for your question.
Hope that helps!
Alina Avanesyan, Thank you! I will look into this. I've used Bayesian inference (with WINBUGS/JAGS in R) and indeed found it very versatile and helpful. My challenge is learning how to go from the posteriors to decision making. In some cases it is straightforward, such as showing that a certain management scenario has a greater probability of increasing population size compared to controls. But in some cases it feels more challenging because I'm not sure what is best to show in more complex (e.g., hierarchical) models or in cases where posteriors need to be back-transformed in order to allow for biological interpretation.
I am probably overthinking this to some extent. :)
Thanks again!
They describe different models in this book, and I don't remember whether JAGS is in the book, but at the workshop I took with Tom Hobbs they used JAGS in R. I wish I would know and understand all the details (I'm still struggling with applying it for my project), but I like that they go progressively from the probabilities to complex models and interpretation. Maybe it will help for your question. Best of luck :-)
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