Where and how many (insert species name here) are there? When we need to manage species in the shrinking fragments of a rapidly changing world, the answers to these basic questions are critical. The quantitative tools for turning simple ecological observations into answers, data into knowledge, have expanded rapidly in the past few decades. A cheap laptop and readily available software (i.e., free!) can churn data into knowledge like never before. If—and it's a big if—you know how to use it.
All that powerful software comes with detailed and extensive (often book-length) guides. However, those papers and books usually aim at an audience with a high level of statistical and mathematical competence. If you have a good grounding in calculus and statistics, then no problem. If you don't, well, you are mostly out of luck unless you can find someone to teach you what is going on at the level you can understand. This is the gap that Estimate of Parameters for Animal Populations: A Primer for the Rest of Us fills very nicely.
Larkin Powell and George Gale used these powerful quantitative tools throughout their research careers. More importantly, they also spent decades teaching others to use them, too. Their deep grasp of the material shines through every page. Rather than show the lofty heights they can reach, the scholars shine a bright light down into the valleys and foothills that block the less knowledgeable from reaching those heights. This is a tremendously valuable contribution. And they do it in a way that is easy to read and personable.
Powell and Gale stuff every chapter with examples of the underlying equations that are simple enough to carry out on a calculator. The collection of examples alone would be a great contribution without the accompanying plain English explanations. Students without a firm mathematical grounding struggle to see how even relatively simple equations come together. Working through the toy examples makes the abstract concrete. Many of these examples draw on Powell's work with songbirds and mammals here in the Great Plains.
If I had one wish for the next edition, I would strengthen the linkages to more standard statistical material, particularly in the introductory chapters. For example, the scientists discuss how linear models and link functions come together to calculate probabilities of survival. This has applications far beyond the ecology-specific models. But when they discuss how to code categorical effects like land use or gender, they use the terminology associated with Program MARK, dummy variables, and neglect the connection to how categorical variables are coded generally in statistical software. My students struggle to understand the coding of categorical variables in everyday regression models; making this connection would really expand the applicability of their lucid and concrete explanations.
Reading through this book gave me a lot of ideas that will improve my teaching of these same concepts. It serves as a great textbook to accompany any course focused on ecology-specific models like MARK, DISTANCE, and occupancy models. The introductory chapters would make valuable reading for any statistics course. Now that I think of it, I'm adding this book to the recommended reading list for my statistics course! [End Page 91]