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(9 Posts)
MaryBWilson
Esri Contributor

Build GIS skills that can make a difference. Join our no-cost MOOC, GIS for Climate Action, opening February 19.

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JoeLemeris
Occasional Contributor

GIS as an Agency-wide Asset at the SC Department of Natural Resources

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ShareUser
Esri Community Manager

Check out the new case study about the Nebraska Game and Parks Commission is using ArcGIS and GPS technology to enhance bighorn sheep safety.

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Amanda__Huber
MVP Regular Contributor

Discover how Three Rivers Park District is revolutionizing wildlife management with cutting-edge GIS solutions to improve efficicency in deer management.

Aerial Deer Survey.png

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MattBullock
Esri Contributor

Howdy! The ArcGIS Solutions team is working on a new solution for Environmental Review. This solution will help organizations streamline their environmental review processes.

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LittleMissEsri
Esri Contributor

Hey Esri Environment Community! 

We recently posted a new tutorial going over very basic home range analysis in ArcGIS Pro. We would LOVE YOUR FEEDBACK! 

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LittleMissEsri
Esri Contributor

Announcing the new Esri Environment Community! 

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LittleMissEsri
Esri Contributor

Extra! Extra! Read all about it! 

Presence-Only Predictive Modeling (aka MaxEnt) is now available in ArcGIS 2.9 and I am SO EXCITED. Why is this a big deal? Well, for one, it's just super fun... 

Ok but really - it's exciting because there are many phenomenon that occur and we know where they've occurred but not necessarily where they HAVEN'T occurred. Like Bigfoot, for example. (Just kidding... or am I?) 

For wildlife biologists (including those that study cryptozoology), botanists or other -ologists looking to understanding where something COULD be... or more specifically, where the conditions might occur to support the presence of said phenomenon... but they only know where it already exists, MaxEnt modeling is extremely helpful. 

How does it work? 

I am no mathematician, but if you want to know the nitty gritty details you can read the documentation. Or you can CC the @GeoSpatialAnalysis team in your response. Ha! 

But here's my version of how it works: 

Let's say I have a rare plant species and I have just a handful of known populations. I've been tasked with strategizing my field work during its flowering season and I want to maximize my time by targeting searches in locations where the species COULD occur. Maybe I know a few things about what it seems to prefer - a specific geology, proximity to a stream, not-too-steep of a slope, and OH! perhaps some habitat characteristics. Great! 

You can pull all those underlying datasets in to ArcGIS Pro: A geology layer, run some Distance Accumulation on a stream layer, derive some slope and pull in some land cover data. On top of that, let's pull in our presence data for our species of interest. 

Now- we're going to run the MaxEnt tool, which is basically us asking the machine to look at where presence occurs, consider the underlying datasets that might EXPLAIN why this species is here and not there, and then score the rest of our study area based on how close we find similar criteria that match. A better score, means the higher the probability is that the right conditions might occur for this species to also be found there. 

What are some considerations?

Again, I totally recommend reading the documentation for the details, but in short, I always tell folks that running this tool does actually require one to know something about the phenomenon you're trying to run it against. 

Why? Because modeling anything - using MaxEnt or otherwise - requires iteration. You must do this a few times. Here's a good example: I once ran MaxEnt on a salamander species and I got a REALLY GREAT result the first time I ran the model. BUT.. when I actually looked at the output and not just the score, I realized that the model had picked up on what's called "sample bias"... I happened to have a lot of records near ROADS, because I would stop the truck, get out and sample, and find a record. So my first run told me ROADS ARE AWESOME FOR SALAMANDERS! 

Oops. I had to remove my sample bias through thinning (a tool available in the Presence-only Prediction Tool in ArcGIS Pro) and run it again, then start to look at my explanatory variables (geology, slope, etc) and ask myself "Is this layer helping my model at all?" and decided whether or not I wanted to keep it. It's pretty easy to do this, because as part of your output, you get little graphs of each of your explanatory variables that tell you which attribute of that variable matters most and how much for influencing the model. 

Pretty awesome, right? 

And you can do this for plants, animals, or other kinds of phenomena! It's SO EASY and super powerful and has tons of potential use cases (wildlife corridors, I'm looking at YOU!) 

For more info check out this blog post

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MichaelBialousz
Esri Contributor

Check out the new Parks Infrastructure Management ArcGIS Solutions which deliver a set of capabilities for Parks, Land Management, and Conservation agencies to inventory assets, maintain asset condition, and communicate asset condition and relative impact on related services. This article gives a nice overview and will get you the resources you need to take advantage of these great Solutions which we were built in partnership with conservation as well as state and local parks agencies. 

https://www.esri.com/arcgis-blog/products/arcgis-solutions/local-government/park-infrastructure-mana...

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