Informing wetland management with waterfowl movement and sanctuary use responses to human-induced disturbance

McDuie, Fiona; Lorenz, Austen A.; Klinger, R; Overton, Cory T.; Feldheim, Cliff L.; Ackerman, Joshua T.; Casazza, Michael L.

doi:10.1016/j.jenvman.2021.113170

Cited by 22 publications

(23 citation statements)

References 42 publications

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“…This design allowed us to show that road noise could be an important aspect of disturbance, and that it might not necessarily be just the visual stimulus of vehicle traffic that could prompt birds to move away from the road. This finding does not diminish the role that visual cues play in disturbance for even humans on foot influence waterbird movement (Pease et al 2005, McDuie et al 2021), but it does suggest that road noise could be a sufficient stimulus for causing disturbance. This information could be important to take into account when planning for mitigation of noise disturbance, especially if considering vegetation or noise walls, which block visual stimuli but allow sound to persist.…”

Section: Discussionmentioning

confidence: 90%

“…These data have indicated various responses of waterbirds to traffic noise, including a reduction in density for some species near road noise (e.g., northern shoveler [ Anas clypeata ], Eurasian coot [ Fulica atra ]; Reijnen et al 1996) and no response from others (e.g., tufted duck [ Aythya fuligula ], mute swan [ Cygnus olor ], northern shoveler; Reijnen et al 1996, black swan [ Cygnus atratus ]; Payne et al 2012). In these studies, the auditory stimulus of road traffic noise was not tested independently of the visual stimulus of the vehicle, and there is a tendency in the literature to infer the effects of noise on animals without explicitly testing noise levels (Pease et al 2005, Loesch et al 2021, McDuie et al 2021).…”

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confidence: 99%

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Disturbance of wintering waterbirds by simulated road traffic noise in Arkansas wetlands

Veon

McClung

2023

J Wildl Manag

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As humans intensify their activity on landscapes, it is important to consider anthropogenic noise when managing habitat for wildlife. Wetlands along rural to urban gradients are subject to road noise pollution, and the waterbirds that live there could be at risk for behavioral disturbance. We tested the positional response of wintering waterbirds to road traffic noise (i.e., sound pressure level) in a playback study in wetlands of Arkansas, USA, from January through March 2018. Each trial consisted of 3, 20-minute phases of road noise playback designated as pre, during, and post. We repeated instantaneous scan sampling every minute during each phase to collect the number, species, and distance of each bird relative to the playback speaker. Distance bins were designated as 0-25, 26-50, 51-75, and 76-100 m from the speaker. Birds approached the speaker at closer distances during the pre phase compared to during and post phases. There was an increase in the probability of a bird occurring in the distance bin farthest from the speaker over the course of the trial. Our results suggest waterbirds might avoid noisier areas within a habitat; however, more study is needed to assess speciesspecific responses, determine thresholds for disturbance, and examine downstream effects of habitat avoidance. If these results persist at larger scales, the soundscape of a wetland could be an important consideration in conservation planning.

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Section: Discussionmentioning

confidence: 90%

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confidence: 99%

Disturbance of wintering waterbirds by simulated road traffic noise in Arkansas wetlands

Veon

McClung

2023

J Wildl Manag

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“…Furthermore, waterfowl appear to adjust to hunting disturbance quickly, with many individuals using sanctuaries diurnally (Shirkey et al 2020, McDuie et al 2021) or reducing movement during legal shooting hours (McDuie et al 2021). McDuie et al (2021) documented diurnal use of sanctuary tripled during opening weekend and flights during legal shooting hours nearly ceased within 2 weeks following the opening day of the hunting season. Despite an initial spike in shotgun volleys, harvest data indicate waterfowl harvest is typically distributed throughout the hunting season in most areas (Vrtiska 2016, USFWS 2022), which may suggest harvest efficacy increases as hunters become more accurate shooters as the season progresses (Ellis et al 2022).…”

Section: Discussionmentioning

confidence: 99%

Influence of sanctuary disturbance, weather, and landscape characteristics on waterfowl harvest opportunity in western Tennessee

et al. 2023

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The North American Waterfowl Management Plan (NAWMP) recognizes waterfowl hunters and non‐consumptive users as important stakeholders to sustain waterfowl populations through economic and political support for habitat conservation initiatives. Opportunities to shoot at and harvest ducks are key determinants of achievement‐oriented hunt satisfaction and quality. Therefore, identifying factors influencing harvest opportunity would help hunters and wildlife managers identify optimal days afield and promote hunter recruitment, retention, and reactivation (R3) efforts in support of NAWMP. Waterfowl extensively use spatial sanctuaries during hunting seasons, especially diurnally, which could limit their perceived availability to hunters. Thus, a possible management action to support R3 is allowing limited public access on sanctuaries, which could offer diverse recreational opportunities for non‐consumptive users and potentially increase harvest opportunities for hunters in traditional hunting areas if the limited disturbance caused waterfowl to leave sanctuaries. Therefore, we evaluated to what extent experimental sanctuary disturbance, landscape characteristics, and weather influenced local harvest opportunities. We conducted disturbance treatments including travel by a covered truck (i.e., truck with enclosed cab), a pedestrian on foot, and an uncovered vehicle (e.g., motor boat or all‐terrain vehicle [ATV]). To measure harvest opportunity, we used autonomous recording units (ARU) to enumerate daily shotgun volleys at a landscape‐scale across western Tennessee, USA, during 2019–2021 waterfowl hunting seasons. We identified 339,391 distinct shotgun volleys ( = 73 shotgun volleys/day/ARU). Shotgun volleys decreased 50% within 2 weeks of opening day and did not notably increase for the remainder of the hunting season. Contrary to our predictions, sanctuary disturbance decreased harvest opportunity. Specifically, the pedestrian disturbance and covered vehicle disturbance decreased daily shotgun volleys by 32% and 31%, respectively. Additionally, harvest opportunity decreased 20% with every 5‐km increase in distance from a sanctuary. Harvest opportunity increased 2% for every 1°C decrease from mean low temperatures and 9% with every 10‐hPa increase in barometric pressure from the previous day. Conversely, harvest opportunity was unaffected by changes in cloud cover, daily precipitation, waterfowl abundance, or surface water inundation. Our results suggest disturbing sanctuaries decreases harvest opportunity and, in turn, may reduce hunt quality and satisfaction. If increasing harvest opportunity is a primary management objective, we recommend limiting sanctuary disturbance to maximize local harvest opportunities. Furthermore, greater harvest opportunity nearer sanctuaries indicates that additional disturbance‐free areas may increase local harvest opportunities and hunter satisfaction.

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“…Therefore, our final set of data included 9334 bird-days of hourly GPS locations from which to develop features for model training, validation, and testing (Table 2 ; see Overton et al [ 33 ] for data availability). Details of field procedures, marking, and data processing are provided in McDuie et al [ 12 , 14 ] and the Additional file 3 .…”

Section: Methodsmentioning

confidence: 99%

“…Furthermore, near real-time classification of animal life history states from marked individuals would be advantageous for crucial management endeavors such as abatement programs designed to minimize conflicts with migrating animals [ 9 ] or disease surveillance efforts [ 10 , 11 ]. Since animal behavior often differs according to individual life history state needs, and behavior is expressed through patterns of movement, we can use movement to classify divergent behavior [ 12 – 14 ] and differentiate among activities related to specific life histories. Such movement information is obtainable by electronically tracking organisms with Global Positioning Systems (GPS).…”

Section: Introductionmentioning

confidence: 99%

Machine learned daily life history classification using low frequency tracking data and automated modelling pipelines: application to North American waterfowl

et al. 2022

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Background Identifying animal behaviors, life history states, and movement patterns is a prerequisite for many animal behavior analyses and effective management of wildlife and habitats. Most approaches classify short-term movement patterns with high frequency location or accelerometry data. However, patterns reflecting life history across longer time scales can have greater relevance to species biology or management needs, especially when available in near real-time. Given limitations in collecting and using such data to accurately classify complex behaviors in the long-term, we used hourly GPS data from 5 waterfowl species to produce daily activity classifications with machine-learned models using “automated modelling pipelines”. Methods Automated pipelines are computer-generated code that complete many tasks including feature engineering, multi-framework model development, training, validation, and hyperparameter tuning to produce daily classifications from eight activity patterns reflecting waterfowl life history or movement states. We developed several input features for modeling grouped into three broad categories, hereafter “feature sets”: GPS locations, habitat information, and movement history. Each feature set used different data sources or data collected across different time intervals to develop the “features” (independent variables) used in models. Results Automated modelling pipelines rapidly developed easily reproducible data preprocessing and analysis steps, identification and optimization of the best performing model and provided outputs for interpreting feature importance. Unequal expression of life history states caused unbalanced classes, so we evaluated feature set importance using a weighted F1-score to balance model recall and precision among individual classes. Although the best model using the least restrictive feature set (only 24 hourly relocations in a day) produced effective classifications (weighted F1 = 0.887), models using all feature sets performed substantially better (weighted F1 = 0.95), particularly for rarer but demographically more impactful life history states (i.e., nesting). Conclusions Automated pipelines generated models producing highly accurate classifications of complex daily activity patterns using relatively low frequency GPS and incorporating more classes than previous GPS studies. Near real-time classification is possible which is ideal for time-sensitive needs such as identifying reproduction. Including habitat and longer sequences of spatial information produced more accurate classifications but incurred slight delays in processing.

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Informing wetland management with waterfowl movement and sanctuary use responses to human-induced disturbance

Cited by 22 publications

References 42 publications

Disturbance of wintering waterbirds by simulated road traffic noise in Arkansas wetlands

Disturbance of wintering waterbirds by simulated road traffic noise in Arkansas wetlands

Influence of sanctuary disturbance, weather, and landscape characteristics on waterfowl harvest opportunity in western Tennessee

Machine learned daily life history classification using low frequency tracking data and automated modelling pipelines: application to North American waterfowl

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