Unifying community detection across scales from genomes to landscapes

Hudon, Stephanie F.; Zaiats, Andrii; Röser, Anna; Roopsind, Anand; Barber, Cristina; Robb, Brecken C.; Pendleton, Britt; Camp, Meghan J.; Clark, Patrick E.; Davidson, Merry M.; Frankel-Bricker, Jonas; Fremgen-Tarantino, Marcella R.; Forbey, Jennifer S.; Hayden, Eric J.; Richards, Lora A.; Rodríguez, Olivia K.; Caughlin, T. Trevor

doi:10.1111/oik.08393

Cited by 8 publications

(3 citation statements)

References 73 publications

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“…By reducing species diversity to generalizable classes, PFTs can aid land management (Wainwright et al, 2019) and facilitate dynamic models for the global carbon cycle (Pandit et al, 2018). However, aggregating spectral information into a small number of discrete PFTs risks oversimplifying gradual changes between spatially varying ecological communities (Hudon et al, 2021). In contrast, continuous measurements of fractional photosynthetic cover are well suited to represent gradual change.…”

Section: Discussionmentioning

confidence: 99%

Drone imagery protocols to map vegetation are transferable between dryland sites across an elevational gradient

et al. 2022

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The structure and composition of plant communities in drylands are highly variable across scales, from microsites to landscapes. Fine spatial resolution field surveys of dryland plants are essential to unravel the impact of climate change; however, traditional field data collection is challenging considering sampling efforts and costs. Unoccupied aerial systems (UAS) can alleviate this challenge by providing standardized measurements of plant community attributes with high resolution. However, given widespread heterogeneity in plant communities in drylands, and especially across environmental gradients, the transferability of UAS imagery protocols is unclear. Plant functional types (PFTs) are a classification scheme that aggregates the diversity of plant structure and function. We mapped and modeled PFTs and fractional photosynthetic cover using the same UAS imagery protocol across three dryland communities, differentiated by a landscape-scale gradient of elevation and precipitation. We compared the accuracy of the UAS products between the three dryland sites. PFT classifications and modeled photosynthetic cover had highest accuracies at higher elevations (2241 m) with denser vegetation. The lowest site (1101 m), with more bare ground, had the least agreement with the field data. Notably, shrub cover was well predicted across the gradient of elevation and precipitation ($230-1100 mm/year). UAS surveys captured the heterogeneity of plant cover across sites and presented options to measure leaf-level composition and structure at landscape levels. Our results demonstrate that some PFTs (i.e., shrubs) can readily be detected across sites using the same UAS imagery protocols, while others (i.e., grasses) may require site-specific flight protocols for best accuracy. As UAS are increasingly used to monitor dryland vegetation, developing protocols that maximize information and efficiency is a research and management priority.

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Section: Discussionmentioning

confidence: 99%

Drone imagery protocols to map vegetation are transferable between dryland sites across an elevational gradient

et al. 2022

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“…Although most existing state estimation methods assign a single discrete state to observations or track segments (e.g., Garriga et al 2016; McClintock and Michelot 2018; Patin et al 2020; but see Jonsen et al 2019), animal movement may not be entirely comprised of a single behavior over a given sampling interval (Pohle et al 2017; Patin et al 2020). Latent Dirichlet Allocation (LDA), a mixed-membership clustering method, can be used to classify each track segment as a mixture of multiple states (Valle et al 2014; Hudon et al 2021). For example, a proportion of observations within a given track segment might belong to state 1 while another proportion might belong to state 2 and so on.…”

Section: Methodsmentioning

confidence: 99%

Identifying latent behavioral states in animal movement with M4, a non-parametric Bayesian method

Cullen

Poli

Fletcher

et al. 2020

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1. Understanding animal movement often relies upon telemetry and biologging devices. These data are frequently used to estimate latent behavioral states to help understand why animals move across the landscape (or seascape). While there are a variety of methods that make behavioral inference from biotelemetry data, some features of these methods (e.g., analysis of a single data stream, use of parametric distributions) may result in the misclassification of behavioral states. 2. We address some of the limitations of segmentation and state-space models (SSMs) in our non-parametric Bayesian framework, available within the open-source R package bayesmove. This framework can analyze multiple data streams, which may capture complex behaviors more successfully than a single data stream. Additionally, parametric distributions are not used in our framework since they may poorly characterize the underlying true distributions. We tested our Bayesian framework using simulated trajectories and compared model performance against a representative segmentation method (behavioral change point analysis; BCPA) and one type of SSM, a hidden Markov model (HMM). We also illustrated this Bayesian framework using movements of juvenile snail kites (Rostrhamus sociabilis) in Florida, USA. 3. The Bayesian framework estimated breakpoints more accurately than BCPA for tracks of different lengths, albeit at a slower computational speed. Likewise, the Bayesian framework provided more accurate estimates of behavior than the HMM when simulations were generated from atypical distributions. This framework also performed up to three times faster than the HMM when run in a similar fashion. Three behavioral states were estimated from snail kite movements, which were labeled as encamped, area-restricted search, and transit. Changes in these behaviors over time were associated with known dispersal events from the nest site, as well as movements to and from possible breeding locations. 4. Our framework estimated behavioral states with comparable or superior accuracy compared to BCPA and HMM when step lengths and turning angles of simulations were generated from atypical distributions. Since empirical data can be complex and do not necessarily conform to parametric distributions, methods (such as our Bayesian framework) that can flexibly classify latent behavioral states will become increasingly important to address questions in movement ecology.

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“…Garriga et al, 2016;McClintock & Michelot, 2018;Patin et al, 2020;but see Jonsen et al, 2019), animal movement may not be entirely comprised of a single behaviour over a given sampling interval (Patin et al, 2020;Pohle et al, 2017). Latent Dirichlet allocation (LDA), a mixed-membership clustering method, can be used to classify each track segment as a mixture of multiple states (Hudon et al, 2021;Valle et al, 2014). For example, a proportion of observations within a given track segment might belong to state 1 while another proportion might belong to state 2 and so on.…”

Section: Mixed-membership Clusteringmentioning

confidence: 99%

Identifying latent behavioural states in animal movement with M4, a nonparametric Bayesian method

et al. 2021

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1. Understanding animal movement often relies upon telemetry and biologging devices. These data are frequently used to estimate latent behavioural states to help understand why animals move across the landscape. While there are a variety of methods that make behavioural inferences from biotelemetry data, some features of these methods (e.g. analysis of a single data stream, use of parametric distributions) may limit their generality to reliably discriminate among behavioural states. 2. To address some of the limitations of existing behavioural state estimation models, we introduce a nonparametric Bayesian framework called the mixed-membership method for movement (M4), which is available within the open-source bayesmove R package. This framework can analyse multiple data streams (e.g. step length, turning angle, acceleration) without relying on parametric distributions, which may capture complex behaviours more successfully than current methods.We tested our Bayesian framework using simulated trajectories and compared model performance against two segmentation methods (behavioural change point analysis (BCPA) and segclust2d), one machine learning method [expectationmaximization binary clustering (EMbC)] and one type of state-space model [hidden Markov model (HMM)]. We also illustrated this Bayesian framework using movements of juvenile snail kites Rostrhamus sociabilis in Florida, USA.3. The Bayesian framework estimated breakpoints more accurately than the other segmentation methods for tracks of different lengths. Likewise, the Bayesian framework provided more accurate estimates of behaviour than the other state estimation methods when simulations were generated from less frequently considered distributions (e.g. truncated normal, beta, uniform). Three behavioural states were estimated from snail kite movements, which were labelled as 'encamped', 'area-restricted search' and 'transit'. Changes in these behaviours over time were associated with known dispersal events from the nest site, as well as movements to and from possible breeding locations. 4. Our nonparametric Bayesian framework estimated behavioural states with comparable or superior accuracy compared to the other methods when step lengths and turning angles of simulations were generated from less frequently considered distributions. Since the most appropriate parametric distributions may not be obvious a priori, methods (such as M4) that are agnostic to the underlying | 433Methods in Ecology and Evoluঞon CULLEN Et aL.

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Unifying community detection across scales from genomes to landscapes

Cited by 8 publications

References 73 publications

Drone imagery protocols to map vegetation are transferable between dryland sites across an elevational gradient

Drone imagery protocols to map vegetation are transferable between dryland sites across an elevational gradient

Identifying latent behavioral states in animal movement with M4, a non-parametric Bayesian method

Identifying latent behavioural states in animal movement with M4, a nonparametric Bayesian method

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