How can you model ecological trends across entire stream networks? Hear how the FIRE lab have trained to try and find out!

Happy Friday readers! This week’s blog post describes a workshop that I attended recently in Idaho, USA on spatially mapping environmental and ecological responses across entire stream networks.

A key part of our studies at FIRE lab are to examine the connectivity of waterbodies and how river fragmentation influences instream biota. The Spatial-Stream-Network (SSN) Models training workshop afforded a great opportunity to learn more about how to model various river processes and properties across entire networks. Examples of this include different environmental characteristics (e.g. stream temperature) and ecological responses (e.g. trout density).

SSN image

An image circulated by the SSN workshop organisers highlighting the diversity of applications.

To stress the need for SSN Models, I will briefly introduce a statistical feature which gives many researchers a headache (although can be very useful as well) – spatial autocorrelation. Broadly, spatial autocorrelation highlights the role that geographical proximity plays in shaping environmental properties. Its influence has been widely recognised by scientists for decades, such as within Tobler’s first law of geography which states that ‘‘Everything is related to everything else, but near things are more related than distant things.’’ Given that river systems are dendritic and highly connected environments, spatial autocorrelation can have a drastic influence on statistical models testing various environmental and ecological properties. For an alternative and more detailed overview of spatial autocorrelation and how SSN tackles this, see here.

Tobler image

An image highlighting Tobler’s first geography law and the role of spatial autocorrelation (Image Source: South Western GIS Blogspot).

While theoretically considered, statistical models addressing and accounting for the influence of spatial autocorrelation along stream networks has been sparse. In light of this, Dr Erin Peterson (University of Brisbane) and Prof. Jan Var Hoef (Alaska Fisheries Science Cente) began collaborating (alongside others) and designed two computer toolsets: Spatial Tools for the Analysis of River Systems (STARS – in the ArcGIS programme) and the SSN package (in the statistical programme ‘R’). STARS enables users to process and refine river networks in a GIS environment, thus allowing the connectivity and proximity of watercourses to be established. A STARS output can then be used within the SSN package to model the distribution of different environmental or ecological properties in response to different controls, all the while accounting for the connectivity and spatial proximity of watercourses. Here is the website if anyone was interested and wanted to learn more about STARS and SSN.

Daniel Isaak (US Forestry Service) identified the power that STARS and SSN could have for his own research on predicting trout densities (amongst other things) across stream networks. Daniel began collaborating with the toolset creators ~8-years ago. While undertaking their own research, Isaak, Peterson and Van Hoef all recognised the value in training scientists on how to use STARS and SSN, as well as sharing their experiences and knowledge on the subject. Consequently, the organisers have just ran the 8th SSN training workshop in Boise, Idaho. Since the initiative began, it has been attended by people from around the world who research diverse topics about river environments.

The first day of the workshop entailed the organisers presenting the underlying principles and theories behind SSN modelling, alongside some tutorials that the organisers worked through. The second and third day was much more applied and users got to use SSN statistics on their own personal dataset(s). For example, I began to examine how invertebrate communities inhabiting temporary headwater streams (i.e. those drying periodically) vary spatially in accordance with the duration of flowing conditions. More widely within the FIRE lab research framework, an SSN approach promises to be a powerful tool enabling us to characterise the role of instream infrastructure in obstructing the movement of different biota. We look forward to exploring the power this has in allowing us to address our research questions!

I hope that you enjoyed hearing a bit about SSN & STARS! More next week when we might hear from FIRE Lab collaborator Michile Jorissen about his recent attendance at the Global Dam Watch workshop in Zeist, Netherlands! 

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