Predetermine what you'll count before you start

When heading out on a tramp into the wild, it’s best practise to declare your intentions before you leave. You can tell a friend, and write your intentions in the trip log book. These days you can also tell an outdoor safety app. Declaring your intentions means that if you don’t come back, people quickly know where to go looking for you. It could save your life.

Counting the wild is no different, and not just for your health and safety, but also for the counting itself. Your counts are vastly more useful if you always declare what you’re about to count before you start. I call these predetermined counts, and for ecological analysis they’re quite different from the casual observations you might make of interesting things as you find them.

It’s only with predetermined counts that people will know where and when you were looking for something but didn’t find it. The easiest way to make these “sought but not found” observations is not to record each time you didn’t find something (like writing “bellb 0”, “bellb 0”, “bellb 0” for bellbirds in each section of your trip). Instead, just record what you decided to look for before you start (“whatsoughtScientificName:Anthornis melanura”).

For larger lists of species, like “All birds”, or “All big mushrooms (>=6 cm across)”, writing down the names of those species list(s) before you start counting is easily the best way to do it. It’s simple and easy and a good habit to get into.

Beware the uneasy middle-ground in here. It’s common sense that you shouldn’t deviate from your planned route after leaving on a big tramp. Likewise, if you find an interesting species, it’s awkward if you then start consistently recording it when you weren’t initially planning to do so. If you do start recording it after you find your first one, it’s essential that all of your subsequent observations of that species remain categorised as casual observations, not predetermined observations.

Why does that matter? If your predetermined data is polluted with counts you made only after first noticing a species, then you’ll be artificially inflating its abundance. That’s because your counts will contain fewer “sought but not found” observations than they should. Researchers would then conclude that your species is more common than it really is. Because of this they might conclude that it isn’t declining when it really has.

To do my predetermined wild counts, I have set myself up species lists. They’re things like “All butterflies”, “All birds”, “All woody weeds”, or “All big mushrooms (>=6 cm across)” (I’ve got many more). Each species list can be selected before I start counting. They can then be used to figure out which species I was looking for but didn’t find on any particular trip.

I initially set up a long list of these species lists in my iPhone to browse through and check off before heading out somewhere. That got pretty tedious as my catalogue of species lists got longer. I therefore switched to selecting groups of lists for common purposes.

For example, there’s my “Usual biking 20111126–”, “Usual exploring away from roads 2015–”, and “Usual passenger in vehicle 20111126–”. Note the dates (here in year-month-day order) that allow me to later update the species lists and rename a group if I need to.

On my iPhone app I only keep my current species list groups in my dropdown list. I’ve been using this method for a couple of years now. It’s quick to use and a big improvement on my old species list picking method.

However you do it, before you head out on a trip to count the wild, take a moment to note down the species you’ll consistently count. That could just be one big obvious species that you’re interested in. For example, if you’re in a New Zealand, it could be our big wood pigeon, the kererū. If you’re super keen, it could be a group of species lists.

With your species list(s) predetermined, turn on your GPS app, mark your start time, and off you go. Consistently mark where and when you see any of your predetermined species. Even if you don’t see any at all, you’ve still collected useful “sought but not found” data.

If you keep doing that over and over, valuable long-term trends will emerge from your observations.