Predation risk constrains herbivores’ adaptive capacity to warming

Nature Ecology & Evolution, Journal Year: 2020, Volume and Issue: 4(8), P. 1069 - 1074

Published: June 1, 2020

Language: Английский

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Component processes of detection probability in camera-trap studies: understanding the occurrence of false-negatives

Mammal Research, Journal Year: 2020, Volume and Issue: 65(2), P. 167 - 180

Published: Feb. 17, 2020

Abstract Camera-trap studies in the wild record true-positive data, but data loss from false-negatives (i.e. an animal is present not recorded) likely to vary and widely impact quality. Detection probability defined as of recording if study area. We propose a framework sequential processes within detection – pass, trigger, image registration, images being sufficient Using closed-circuit television (CCTV) combined with camera-trap arrays we quantified variation in, drivers of, these for three medium-sized mammal species. also compared trigger success wet dry otter Lutra lutra , example semiaquatic Data failed registration poor capture quality varied between species, model settings, were affected by different environmental variables. Distance had negative effect on positive probability. Faster animals both reduced probabilities. Close passes (1 m) frequently did generate triggers, resulting over 20% all Our results, linked describing processes, can inform design minimize or account during analysis interpretation.

Language: Английский

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Effects of camera‐trap placement and number on detection of members of a mammalian assemblage

Ecosphere, Journal Year: 2021, Volume and Issue: 12(7)

Published: July 1, 2021

Abstract A central goal in camera‐trapping (CT) studies is to maximize detection probability and precision of occupancy estimates while minimizing the number CTs reduce equipment labor costs. Few studies, however, have examined effect CT on probability. Moreover, historically, most focused a specific species design could be tailored toward maximizing this target species. Increasingly, such use data for all captured, non‐target, (by‐catch data) animal community‐level analyses. It remains unclear if, how, targeting one affects non‐target We paired from permanent grid (with 38 CTs) targeted at monitoring Eurasian lynx ( Lynx ) Innlandet County, Norway, with additional randomly placed two spatial scales (38 within same habitat patch 50‐km 2 cell as lynx‐targeted three months. combined multi‐scale models that enable separation large‐scale occupancy, CT‐scale site use, single‐scale models. This allowed us study effects placement (lynx) seven mammal (four carnivores, herbivores, rodent). found species, except moose Alces alces ), had highest CTs. Moose equal probabilities types. Adding extra generally increased probabilities. Consequently, combining or more CTs, accuracy cells compared single estimates. The underestimated grid‐cell known minimum were similar site‐use is, uncertain which extent these refer. therefore recommend multiple (targeted) estimate large interpret an, yet undefined, area surrounding CT.

Language: Английский

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Evaluating species-specific responses to camera-trap survey designs

Wildlife Biology, Journal Year: 2021, Volume and Issue: 2021(1)

Published: Jan. 25, 2021

Camera traps are widely used to collect information on the distribution and abundance of multiple species simultaneously. However, we still lack important guidance for designing camera-trap surveys monitor species, consequences species-specific responses survey design strategies often overlooked. Using data collected ten medium-to-large North-American carnivores in northern Minnesota, USA, between 2016 2018 (23 337 active trap-days), evaluated: 1) two different survey-design frameworks (random- versus road-based), 2) lure types (salmon oil fatty acid scent oil), 3) placement (completely random randomly-selected sites with feature-based placement), 4) timing (spring fall) 5) temporal trends daily encounter probabilities. generalized linear mixed models, found evidence differential all these strategies. For 9 out 10 strong frameworks: red foxes Vulpes vulpes, coyotes Canis latrans, bobcats, Lynx rufus, striped skunks Mephitis mephitis, wolves C. lupus gray Urocyon cinereoargenteus, had estimated frequencies that were 9- 106-fold higher at unlured along secondary roads; black bears Ursus americanus, martens Martes americana fishers Pekania pennanti 15- > 3600-fold lured, randomly selected sites. six salmon provided 2- 4-fold more encounters than oil, but feature-basedplacement only improved detections fishers. Daily probabilities differed spring fall usually decreased slightly within each sampling period Our study confirms even similar-sized or closely-related respond differently choices. To maximize frequencies, recommend multi-species studies use a mix include features during statistical analysis.

Language: Английский

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High‐density camera trap grid reveals lack of consistency in detection and capture rates across space and time

Ecosphere, Journal Year: 2021, Volume and Issue: 12(2)

Published: Feb. 1, 2021

Abstract Counts of independent photo events from camera traps are commonly used to make inference about species occupancy, the density unmarked populations, and relative abundance across time space. These applications rest on untested assumption that data collected individual cameras representative landscape location in which they placed, nearby would record similar when any additional micro‐site differences accounted for. We established a high‐density trapping grid (100 × 100 m; 27 cameras) Virginia, USA, explicitly test these assumptions, investigating variation capture rates detection probabilities for range terrestrial mammals during four 2‐month seasonal surveys. Despite controlling numerous habitat placement factors, we documented, all 5 focal species, large ranges coefficients both rate probabilities, were those seen 2 sets forest sampling sites larger, more typical trap design. also documented lack spatial autocorrelation at distance. Measured local covariates relevant viewshed (stem density, height, log presence, effective distance [EDD], total dbh oak trees) rarely explained significant portion observed or grid. The influence EDD, measured here first stations, was inconsistently important varied direction effect depending season. Our study indicates single‐camera stations may fail sample animal presence frequency use robust repeatable way, primarily resulting idiosyncrasies movement unknown characteristics. recommend replication within (e.g., small‐scale shifting multiple stations) should be considered minimize impacts characteristics, some difficult identify.

Language: Английский

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Human impacts on mammals in and around a protected area before, during, and after COVID‐19 lockdowns

Conservation Science and Practice, Journal Year: 2022, Volume and Issue: 4(7)

Published: June 7, 2022

The dual mandate for many protected areas (PAs) to simultaneously promote recreation and conserve biodiversity may be hampered by negative effects of on wildlife. However, reports these are not consistent, presenting a knowledge gap that hinders evidence-based decision-making. We used camera traps monitor human activity terrestrial mammals in Golden Ears Provincial Park the adjacent University British Columbia Malcolm Knapp Research Forest near Vancouver, Canada, with objective discerning relative various forms cougars (

Language: Английский

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Are we telling the same story? Comparing inferences made from camera trap and telemetry data for wildlife monitoring

Ecological Applications, Journal Year: 2022, Volume and Issue: 33(1)

Published: Sept. 15, 2022

Abstract Estimating habitat and spatial associations for wildlife is common across ecological studies it well known that individual traits can drive population dynamics vice versa. Thus, commonly assumed individual‐ population‐level data should represent the same underlying processes, but few have directly compared contemporaneous representing these different perspectives. We evaluated circumstances under which collected from Lagrangian (individual‐level) Eulerian (population‐level) perspectives could yield comparable inference to understand how scalable information population. used Global Positioning System (GPS) collar (Lagrangian) camera trap (Eulerian) seven species simultaneously in eastern Washington (2018–2020) compare inferences made survey fit respective streams resource selection functions (RSFs) occupancy models estimated habitat‐ space‐use patterns each species. Although previous considered whether generated information, ours first make this comparison multiple specifically ask two differed depending on focal found general agreement between predicted distributions most paired analyses, although specific relationships differed. hypothesize discrepancies arose due differences statistical power associated with GPS‐collar sampling, as mismatches data. Our research suggests individual‐based sampling methods capture coarse population‐wide a diversity of species, results differ when interpreting wildlife‐habitat relationships.

Language: Английский

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Population assessment without individual identification using camera-traps: A comparison of four methods

Basic and Applied Ecology, Journal Year: 2022, Volume and Issue: 61, P. 68 - 81

Published: March 7, 2022

The use of camera traps to estimate population size when animals are not individually recognizable is gaining traction in the ecological literature, because its applicability conservation and management. We estimated synthetic with four trap sampling-based statistical models that do rely on individual recognition. Using a realistic model animal movement generate data, we compared random encounter model, staying time association time-to-event-model investigated impact violation assumptions estimates. While under ideal conditions these provide reliable estimates, movements were characterised by differences speed (due diverse behaviours such as locomotion, grazing resting) none provided both unbiased precise density results but tended overestimate size, while was less underestimate size. Lastly, unable results. found each tested very sensitive method used range field-of-view traps. Density estimates from also biases animals' speed. guidelines how get could be useful wildlife managers practitioners.

Language: Английский

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Deer Behavior Affects Density Estimates With Camera Traps, but Is Outweighed by Spatial Variability

Frontiers in Ecology and Evolution, Journal Year: 2022, Volume and Issue: 10

Published: May 18, 2022

Density is a key trait of populations and an essential parameter in ecological research, wildlife conservation management. Several models have been developed to estimate population density based on camera trapping data, including the random encounter model (REM) trap distance sampling (CTDS). Both need account for variation animal behavior that depends, example, species sex animals along with temporally varying environmental factors. We examined whether estimates REM CTDS can be improved Europe’s most numerous deer species, by adjusting behavior-related parameters per accounting differences movement speeds between sexes, seasons, years. Our results showed bias through inadequate consideration was exceeded uncertainty estimates, which mainly influenced number independent observations locations. The neglection seasonal annual speed overestimated densities red autumn spring ca. 14%. This GPS telemetry-derived found problematic roe females summer when characterized small-scale displacements relative intervals fixes. In CTDS, foremost behavioral reactions traps (avoiding max. 19%), while species-specific delays photos had larger effect deer. general, applicability both would profit profoundly from improvements their precision reduction achieved exploiting available information data.

Language: Английский

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Camera trapping in ecology: A new section for wildlife research

Ecology and Evolution, Journal Year: 2023, Volume and Issue: 13(3)

Published: March 1, 2023

Ecological research is undergoing a substantial transformation. Camera trapping—"capturing" photograph remotely, allowing observation of wildlife separately from the observer—has been around for over century. However, it emerged as substantive mode sampling occurrence only about three decades ago (Kucera & Barrett, 2011; O'Connell et al., 2011) and now rapidly improving innovating, changing face ecology (Burton 2015). With repeated made possible across space time, limited by logistics resources, observations can be gathered analyzed at unprecedented spatial temporal scales. engineering relatively inexpensive camera models that do not require costly support systems (such those needed satellite telemetry), traps also serve to democratize research. trapping has consequently spread global south developing countries (Agha 2018; Cremonesi 2021; Galindo-Aguilar 2022). Many private citizens run their own traps; networking these citizen scientists have yielded great insights will continue so (McShea 2016). are being employed Indigenous peoples ask questions on traditional territories (Artelle Fisher 2021), an important step towards meeting principles United Nations Declaration Rights Peoples (Gilbert, 2007). Camera-trap spans ecological hierarchy, with applications animal behavior (Caravaggi 2017, 2020) such diel activity (Frey 2017; Rowcliffe 2014), populations (Bischof 2020; Gardner 2010), species' distributions (Rich Tobler 2015), communities (Ahumada Wittische 2021). adequate inferential logic analysis, more complex processes species interactions discerned (Beirne Clare 2016; Niedballa 2019). The field rich planting seeds new ideas. In fact, though largely used mammals, expanding taxonomically include vegetation (Seyednasrollah 2019; Sun herptiles (Moore Welbourne 2020), avifauna (Jachowski 2015; Murphy 2018). Software advanced in-step hardware. Converting images numerical data easier custom software, much open-source (Greenberg Young Processes automatic identification developed greatly speed up image classification process "big data" (Duggan Shepley Conceptual advances, frameworks understanding how detections sample underlying processes, paving way sophisticated (Glover-Kapfer Hofmeester Tremendous discoveries lay in future. Networking arrays different landscapes—even globally, similar weather networks (Steenweg 2017)—will allow macroecological scale never before (Chen 2022; Magle Rich 2017). Notwithstanding, await small focal studies too—these foundations inference. We endeavors Ecology Evolution's section Trapping Ecology. journal's mandate author-friendly, without gatekeeping assessments importance barrier, makes us place welcomes both small-scale autecological large-scale syntheses. This philosophy help authors work read scientific community—we believe this Section goal. first volume featured its camera-trapping study (Fisher 2011), paper desk-rejected several other journals "interesting but improbable" among fare. Editorial team gave chance, 100 citations later, continues stimulate debate (Stuber Fontaine, Since then, we published s camera-trap studies. eagerly anticipating many papers dedicated Section, Evolution plans forefront proliferation research, platform thought debate. Jason Thomas Fisher: Conceptualization (equal); writing – original draft (equal). None. No available.

Language: Английский

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