Using dynamic Brownian Bridge Movement Models to identify home range size and movement patterns in king cobras

Silva, Inês; Crane, Matthew; Suwanwaree, Pongthep; Strine, Colin Thomas; Goode, Matt

doi:10.1371/journal.pone.0203449

Cited by 41 publications

(39 citation statements)

References 59 publications

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“…We tracked individuals four times a day, with approximately 78 four hours between tracks from 2014-03-22 to 2018-07-28 (06:30, 11:00, 16:00, 20:00; the distribution 79 of time lags between tracking is available in Supplementary Figure 1). Full details of the tracking 80 protocols can be found in Silva et al (2018). We named every individual according to their age class, 81 sex and capture number (e.g.…”

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confidence: 99%

No room to roam: King Cobras reduce movement in agriculture

Marshall

Crane

Silva

et al. 2020

Preprint

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13Studying animal movement provides insights into how animals react to land-use changes, specifically 14 how animals can change their behaviour in agricultural areas. Recent reviews show a tendency for 15 species to reduce movements in response to increased human landscape modification, but the study of 16 movement has not been extensively explored in reptiles. We examined movements of a large reptilian 17 predator, the King Cobra (Ophiophagus hannah), in Northeast Thailand. We used a consistent regime 18 of radio-telemetry tracking to document movements across protected forest and adjacent agricultural 19 areas. We then adapted GPS-targeting analytic methods to examine the movement using metrics of site 20 reuse and dynamic Brownian Bridge Movement Model derived motion variance. Examination of 21 motion variance demonstrated that King Cobra movements increased in forested areas and tended to 22 decrease in agricultural areas. Our Integrated Step-Selection Functions indicated that when moving in 23 agricultural areas King Cobras restricted their movements, thereby remaining within vegetated semi-24 natural areas, often located along the banks of irrigation canals. Site reuse metrics of residency time and 25 number of revisits remained unaffected by distance to landscape features. Neither motion variance nor 26 reuse metrics were consistently affected by the presence of threatening landscape features (e.g. roads, 27 human settlements); suggesting that King Cobras will remain in close proximity to threats, provided 28 habitat patches are available. Although King Cobras displayed heterogeneity in their response to 29 agricultural landscapes, the overall trend suggested a reduction in movements when faced with 30 fragmented habitat patches embedded in an otherwise inhospitable land-use matrix. Reductions in 31 movements are consistent with findings for mammals and forest specialist species. 32

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No room to roam: King Cobras reduce movement in agriculture

Marshall

Crane

Silva

et al. 2020

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“…Previous studies on Burmese pythons (both in their native range and invasive range) have used more "traditional" home range estimators, such as MCPs and KDEs, which are limited in their ability to be compared across studies (Silva et al, 2018;Silva et al, 2020). Rather, we measured the occurrence distributions of our tracked snakes during our tracking period.…”

Section: Discussionmentioning

confidence: 99%

“…We quantified space use or occurrence distributions using dynamic Brownian Bridge Movement Models (dBBMMs). We elected to use dBBMMs to quantify space use over more traditional methods such as minimum convex polygons (MCPs) and kernel density estimates (KDEs) due to the fact that these traditional methods tend to greatly overestimate or underestimate space use during the study period (Silva et al, 2018. In contrast, dBBMMs create a more refined visualization of animal movement by taking into account the order of animal relocations and the duration of time spent at each location (Kranstauber et al, 2012).…”

Section: Space Use/occurrence Distributions Analysismentioning

confidence: 99%

Restricted constrictors: Space use and habitat selection of native Burmese pythons in Northeast Thailand

Smith

Jones

Marshall

et al. 2020

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Animal movement and resource use are tightly linked. Investigating these links to understand how animals utilize space and select habitats is especially relevant in areas that have been affected by habitat fragmentation and agricultural conversion. We set out to explore the space use and habitat selection of Burmese pythons (Python bivittatus) in a patchy land use matrix dominated by agricultural crops and human settlements. We used radio telemetry to record daily locations of seven Burmese pythons over the course of our study period of approximately 22 months. We created dynamic Brownian Bridge Movement Models (dBBMMs) for all individuals, using occurrence distributions to estimate extent of movements and motion variance to reveal temporal patterns. Then we used integrated step selection functions to determine whether individual movements were associated with particular landscape features (aquatic agriculture, forest, roads, settlements, terrestrial agriculture, water), and whether there were consistent associations at the population level. Our dBBMM estimates suggested that Burmese pythons made use of small areas (98.97 ± 35.42 ha), with low mean individual motion variance characterized by infrequent moves and long periods at a single location. At both the individual and population level, Burmese pythons in the agricultural matrix were associated with aquatic environments. Only one individual showed a strong avoidance for human settlements which is troublesome from a human-wildlife conflict angle, especially as Burmese pythons have been observed entering human settlements and consuming livestock in our study site. Our study is one of the first to contribute to the knowledge of Burmese python ecology in their native range as the majority of studies have focused on invasive populations.

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“…Home range estimation and other spatial ecology analytical methods are continually improving through technological and modeling advances (e.g., Fieberg and Börger, 2012;Horne et al, 2007;Laver and Kelly, 2008;Signer and Balkenhol, 2015). Nevertheless, detection effects persist for methods limited by observer time and expertise, like radio telemetry and color band relocation (e.g., Silva et al, 2018), for cryptic species like many herpetofauna (Ćorović et al, 2018), and for GPS studies in which bias co-varies with animal habitat preferences (Ironside et al, 2017). The pervasive yet unpredictable effects of variation in detection probability necessitate tools for planning field protocols and choosing modeling methods to fit a study's detection and methodological conditions.…”

Section: Discussionmentioning

confidence: 99%

Simulating detection-censored movement records for home range analysis planning

Wszola

Simonsen

Corral

et al. 2019

Ecological Modelling

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Home range estimation is an important analytical method in applied spatial ecology, yet best practices for addressing the effects of spatial variation in detection probability on home range estimates remain elusive. We introduce the R package "DiagnoseHR," simulation tools for assessing how variation in detection probability arising from landscape, animal behavior, and methodological processes affects home range inference. We demonstrate the utility of simulation methods for home range analysis planning by comparing bias arising from three home range estimation methods under multiple detection scenarios. We simulated correlated random walks in three landscapes that varied in detection probability and constructed home ranges from locations filtered through a range of sampling protocols. Home range estimates were less biased by reduced detection probability when sampling effort was increased, but the effects of sampling day distribution were minimal. Like others, we found that kernel density estimates were the least affected by variation in detection probability, while minimum convex polygons were most affected. Our results illustrate the value of quantifying uncertainty in home range estimates and suggest that field biologists working in environments with low detection may wish to weight sample-size greater than concerns about temporal autocorrelation when designing sampling protocols.

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Using dynamic Brownian Bridge Movement Models to identify home range size and movement patterns in king cobras

Cited by 41 publications

References 59 publications

No room to roam: King Cobras reduce movement in agriculture

No room to roam: King Cobras reduce movement in agriculture

Restricted constrictors: Space use and habitat selection of native Burmese pythons in Northeast Thailand

Simulating detection-censored movement records for home range analysis planning

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