TWS 2025

[Christen Fleming]

Dear all,

We will be giving a full-day Introduction to continuous-time movement modeling for animal tracking data workshop at this year's TWS annual conference in Edmonton, Alberta on Sunday, October 05th from 8:00 AM - 5:00 PM. All proceeds will go towards the TWS Spatial Ecology and Telemetry Working Group.

Early bird registration ends June 30th: https://twsconference.org/registration-2025/

Best,

Chris

[Christen Fleming]

Early bird registration has been extended through July 15.

Best,

Chris

[Erika Lin]

Hello everyone!

I am Erika Lin, a Ph.D. student in Chris's Ecoinformatics Lab at UCF. I am excited to be presenting a talk at this year's TWS Annual Conference on new methods in ctmm. "Improving Statistical Methods for Wildlife Corridor Estimation" will take place at 2:30 PM on Tuesday, Oct. 7, in Hall D (Screen 3), as part of the Spatial Ecology and Modeling session.

I look forward to seeing you all there!

Abstract:
Habitat connectivity is essential to conserving biodiversity, by allowing animals to search across landscapes for resources and mates. This is particularly important for migration and dispersal, which can be impeded by human activities that degrade and fragment habitats. To maintain connectivity, it is imperative that we identify wildlife corridors—areas where animals traverse frequently to move between suitable environments. Traditionally, connectivity has been modeled via two main methods: resistance circuit models and Brownian bridges. However, these methods do not target a “well-calibrated” probabilistic corridor distribution—i.e., where 95% of animals pass through the 95% cross-section. We have developed a new statistical method for corridor estimation, using cross-sectional kernel density estimation (KDE) to model corridors as “range distributions” from animal tracking data. We define the corridor by the distribution of repeated passages between two areas in a landscape. To ensure that this method is robust and offers improvements, we conducted a comparative sensitivity analysis across parameters that impact performance, such as sampling frequency and passage count, using GPS-tracked mule deer (Odocoileus hemionus) and jaguars (Panthera onca). We found that cross-sectional KDE is insensitive to the sampling frequency and produces consistent distribution estimates regardless of the location-recording interval, thus demonstrating that our method provides more rigorous estimates of corridor space needed by migrating animals, even with few tracking points available. Our research, implemented in the ctmm R package, contributes a novel statistical tool that ecologists can directly apply to conservation management, through designating new wildlife corridors and evaluating the impact of existing ones.

Best,

Erika Lin

[Nozomu Hirama]

Hi all,

I am Nozomu Hirama, also a PhD student under Chris's Ecoinformatics Lab at UCF. I would like to take the chance to advertise that I will be presenting a talk at this year's TWS Annual Conference as well, about my work on "Methods for Quantifying Nocturnality and Human Impacts Using Animal Tracking Data." My talk will take place at 2:45 - 3:00 PM on Tuesday, Oct 7, in Hall D (Screen 3), right after Erika's talk. Feel free to stop by if you are interested!

Abstract: 

Diel activity patterns often reflect an animal’s adaptations and strategies to optimize their fitness. However, these patterns can be disturbed by human influence, potentially forcing individuals to be active during less optimal times of the day. To better understand such effects, we introduce a new method for estimating nocturnality from tracking data, along with a new metric of the overlap and distance between the home range and Human Footprint Index (HFI). First, we have developed a continuous-time movement model (ctmm) that switches between high and low levels of movement according to solar time. Using this model, we can accurately estimate the proportion of nighttime activity from irregularly sampled tracking data. Second, we have developed a novel approach to quantifying HFI levels, or other index, both within an individual’s home range and in the proximal surrounding areas by using log expectation values. Importantly, this metric increases with increasing disturbance within the home range, with increasing disturbance in the neighborhood of the home range, and with increasing proximity to said disturbances, which makes this a suitable predictor for examining any response to human influence. With our new metrics, we explore the impacts of human disturbance on nocturnality for the common raccoon (Procyon lotor), coyote (Canis latrans), and Temminck’s ground pangolin (Smutsia temminckii). Our work is implemented in the ctmm R package, providing accessible tools to better inform conservation and management in an increasingly anthropized environment.

Best,

Nozomu Hirama

[Sean Doolittle]

Hello everyone, 

I am Sean Doolittle, a master's student in the Ecoinformatics lab at UCF. I will be presenting a poster at this year's TWS on Monday, October 6th, from 5:00 pm – 6:30 pm during the Contributed and Student Research in Progress Poster session on new Visualization tools for animal tracking data (poster #159). I look forward to seeing you all there!   

Abstract: 

We introduce two new software tools for visualizing animal tracking data in a geospatial context. Our first tool, ctmmRayshader, leverages the Rayshader R package (T. Morgan-Wall 2025) to create interactive 3D raised relief maps that represent animal movement data and distributions in a geospatial context. Our second tool, ctmmEarth, leverages Google Earth Pro to create 3D animations via Keyhole Markup Language (KML) files. Both products are designed to work with Movebank-formatted CSV files and “continuous-time movement modeling” (ctmm) telemetry objects.   

Our Rayshader software, ctmmRayshader, enables users to create striking visualizations by realistically simulating sunlight, resulting in detailed shadows and vivid 3D raised relief maps. Output maps are created using data from the NASA digital elevation model (DEM) with Landsat 8 satellite imagery. CtmmRayshader excels at creating high-resolution visualizations of nonmigratory movement data and can be used to depict predicted or simulated paths and home-range estimates from the ctmm R package.    

Our Google Earth software, ctmmEarth, generates a detailed KML file for a user-specified animation of simulated or predicted movement paths (with prediction intervals) from ctmm movement models. Users can generate animations from 3 camera views—POV, manual, and follow. CtmmEarth excels at visualizing migration and dispersal data, as Google Earth Pro is optimized for visualizing large amounts of satellite data, with a camera that can quickly and smoothly pan with the animal movements.   

These products allow researchers and managers to easily generate professional-quality imagery and animations from animal tracking data. Both tools are implemented in the ctmm. 

Best, 

Sean Doolittle