# Mammal responses to global changes in human activity vary by trophic group and landscape

A. Cole Burton, Christopher Beirne, Kaitlyn M. Gaynor, Catherine Sun, Alys Granados, Maximilian L. Allen, Jesse M. Alston, Guilherme C. Alvarenga, Francisco Samuel Álvarez Calderón, Zachary Amir, et al.

► To cite this version:

A. Cole Burton, Christopher Beirne, Kaitlyn M. Gaynor, Catherine Sun, Alys Granados, et al., Mammal responses to global changes in human activity vary by trophic group and landscape. Nature Ecology & Evolution, 2024, 8 (5), pp.924-935. 10.1038/s41559-024-02363-2. hal-04664439

HAL Id: hal-04664439

https://hal.inrae.fr/hal-04664439v1

Submitted on 30 Jul 2024

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
<figure><img src="image_2.png" /></figure>

# Mammal responses to global changes in human activity vary by trophic group and landscape

Received: 7 March 2023

Accepted: 9 February 2024

Published online: 18 March 2024

<figure><img src="image_3.png" /></figure>

Check for updates

A list of authors and their affiliations appears at the end of the paper

Wildlife must adapt to human presence to survive in the Anthropocene, so it is critical to understand species responses to humans in different contexts. We used camera trapping as a lens to view mammal responses to changes in human activity during the COVID-19 pandemic. Across 163 species sampled in 102 projects around the world, changes in the amount and timing of animal activity varied widely. Under higher human activity, mammals were less active in undeveloped areas but unexpectedly more active in developed areas while exhibiting greater nocturnality. Carnivores were most sensitive, showing the strongest decreases in activity and greatest increases in nocturnality. Wildlife managers must consider how habituation and uneven sensitivity across species may cause fundamental differences in human-wildlife interactions along gradients of human influence.

With the global human population size now past 8 billion and the associated human footprint covering much of the Earth's surface¹, survival of wild animals in the Anthropocene requires that they adapt to physical changes to the landscape and to increasing human presence. Animals often perceive humans as threats and subsequently adjust behaviours to avoid people in space or time². Conversely, some animals are attracted to people to obtain resource subsidies or protection from predators³,⁴. These contrasting responses to humans shape the prospects for human-wildlife coexistence, with consequences for the capacity of human-influenced ecosystems to support robust animal populations and communities.

Variation in animal responses to human activity can be driven by intrinsic factors such as species' ecological and life-history traits (Table 1)⁵. For instance, small-bodied generalist species may be more tolerant of human presence, as they can be less conspicuous than larger species and more capable of shifting resource use within their broader niches than are specialists⁶. Wide-ranging, large-bodied carnivores face considerable risk of mortality from humans⁷ and so may exhibit more risk-averse responses to human activity. Animal responses may also be heavily influenced by the type of human activity (for example, hunting versus hiking⁸) and by extrinsic factors such as landscape context. Animals may be warier of people in open or human-modified environments relative to areas with abundant vegetation cover or minimal human landscape modification⁹. Conversely, animals in heavily modified landscapes

could habituate to human presence and thus be less likely to respond to changes in human activity. Our ability to resolve such hypotheses about the interacting influences of species traits and landscape characteristics has been limited by the focus of previous studies on few species and contexts, with indirect measures of human activity and weaker relative inferences. Ultimately, anticipating and managing impacts to wild animals requires stronger inferences from experimental manipulations of human activity and concurrent monitoring of people and animals across a range of species and environmental contexts.

Government policies during the early months of the COVID-19 pandemic (henceforth, pandemic) resulted in widespread changes to human activity that provided a quasi-experimental opportunity to study short-term behavioural responses of wild animals¹⁰. Early observations of animal responses to this 'anthropause'¹¹ relied on qualitative or opportunistic sightings prone to bias (for example, contributed by volunteers¹²), or focused on small spatial scales and few species, reporting a mix of positive and negative responses that make it difficult to reach more general conclusions¹³. Furthermore, measures of human activity have typically been coarse and indirect¹⁴, yet changes to human activity during the pandemic appeared highly variable at the fine scales that affect animal behaviour (Fig. 1). For example, some natural areas experienced increases in human visitation while others were closed to visitors¹⁵ and the strength of government restrictions changed over time¹⁴. It is thus important for studies using

<footnote>e-mail: cole.burton@ubc.ca</footnote>
<table><caption>**Table 1 | Predictor variables hypothesized to explain variation in species responses to higher human activity, with greater reductions in amount of activity or increases in nocturnality predicted for more sensitive species (further details in Supplementary Information)**</caption><thead><tr><th>Class</th><th>Variable</th><th>Prediction</th><th>Range</th></tr></thead><tbody><tr><td>Species trait</td><td>Body mass</td><td>Large-bodied species will be more sensitive</td><td>Small (1–20 kg; n=101); large (20–4,600 kg; n=62)</td></tr><tr><td>Species trait</td><td>Trophic level</td><td>Higher trophic levels will be more sensitive</td><td>Carnivore (n=59), omnivore (n=27), herbivore (n=77)</td></tr><tr><td>Species trait</td><td>Diet breadth</td><td>Specialists with narrower diet will be more sensitive</td><td>1–4 diet categories</td></tr><tr><td>Species trait</td><td>Habitat breadth</td><td>Specialists with narrower habitat preference will be more sensitive</td><td>1–9 habitat categories</td></tr><tr><td>Species trait</td><td>Diel activity</td><td>Diurnal species will be most sensitive, cathemeral species intermediate and nocturnal species least sensitive</td><td>Diurnal (n=13), cathemeral (n=91), nocturnal (n=59)</td></tr><tr><td>Species trait</td><td>Hunting status</td><td>Hunted species (within projects) will be more sensitive to increased human activity than their non-hunted counterparts</td><td>Yes (n=486), no (n=491) (total=977 project-species)</td></tr><tr><td>Species trait</td><td>Relative brain size</td><td>Small-brained species will be more sensitive</td><td>0.006–5.3 kg</td></tr><tr><td>Habitat structure</td><td>Openness</td><td>Animals will be more sensitive in open habitat types relative to closed habitats</td><td>Open (n=31), closed (n=71)</td></tr><tr><td>Land-use disturbance</td><td>Human modification index</td><td>Animals will be more sensitive in landscapes with more human modification</td><td>0.005–0.834</td></tr><tr><td>Magnitude of human change</td><td>Global stringency index</td><td>Animals will show stronger responses where lockdowns were more stringent</td><td>38.9–96.0 stringency units</td></tr><tr><td>Magnitude of human change</td><td>Mean change in human detections (at camera traps)</td><td>Animals will show stronger responses where change in human activity greater</td><td>1–100-fold changes</td></tr></tbody></table>

For continuous variables we show the range (minimum–maximum); for categorical variables we show the sample size for each level, which sum to 163 species for species-level variables or 102 projects for project-level variables (unless otherwise stated). Body mass and trophic level were combined in a new variable ‘trophic group’.

the pandemic as an unplanned experiment to have localized information on human activity that matches their animal data and to tackle context-dependency by using robust, standardized methods across several species and landscapes.

The widespread use of camera traps to survey terrestrial mammals¹⁶ provides a unique opportunity to take advantage of the pandemic experiment and improve our understanding of animal responses to changes in human activity. Thousands of cameras are deployed around the world¹⁷, providing standardized animal sampling while simultaneously quantifying local human activity¹⁵,¹⁸. We harnessed this opportunity to examine relationships between detections of people and mammals across gradients in land use and habitat type–spanning 102 survey sites (projects) in 21 countries (predominantly in Europe and North America) with 5,400 camera-trap locations sampling for 311,208 camera-days before and during the pandemic (Fig. 1; Methods). Some sites experienced a decrease in human activity during the pandemic, consistent with the notion of an anthropause, while there was an increase or no change at others. We focused our analysis on those sites with some change in human activity (either increase or decrease) and standardized our comparisons to be between periods of relatively lower to higher human activity (either across years or within 2020; Fig. 1; Methods) to mimic the general trend of increasing human presence in the Anthropocene. We examined site-level changes in animal detection rates and nocturnality across populations of 163 mammal species (body mass ≥ 1 kg; range 1–65 populations per species; Supplementary Table 1) as measures of the relative amount and timing of animal activity (Methods). We then used meta-analytic mixed-effects models to quantify the extent to which variation in animal responses across sites was explained by species traits, landscape modification and other site characteristics and the magnitude of change in human activity (Table 1; Methods).

## Results and discussion

Our camera-trap measures of human activity varied widely under COVID-19 lockdowns (occurring between March 2020 and January 2021), from 100-fold decreases to 10-fold increases within sites between comparison periods (Fig. 1 and Supplementary Fig. 1). These changes

were not predicted by coarser measures of human activity based on the stringency of lockdowns (Supplementary Fig. 1), highlighting the complementary value of finer-scaled monitoring of human activity.

### Changes in amount of animal activity

Animals did not show consistent, negative responses to greater human activity; instead, responses were highly variable among species and sites (Figs. 2 and 3). Across 1,065 estimated responses (one per species per project, that is, population), changes in animal detection rates (reflecting the intensity of habitat use; Methods) varied from 139-fold increases to 36-fold decreases, with a near-zero mean change overall (–0.04, 95% confidence interval (CI) = –0.11–0.03; Fig. 2b). Trophic group (combining body mass and trophic level) was the strongest predictor of changes in animal activity in response to increasing human use, with large herbivores showing the largest increases in activity and carnivores showing the strongest decreases (Fig. 2c, Supplementary Table 2 and Supplementary Fig. 3). This is consistent with carnivore avoidance of higher mortality risk from encounters with people⁷ and with increased herbivore activity due to either more frequent disturbance by people or attraction to human activity driven by reduced risk of predation (human shield hypothesis³).

Animal activity in more developed areas (that is, higher human modification index (HMI) measured at the site level; Table 1) generally increased (+25%) with higher levels of human activity, while animals in less-developed areas decreased their activity (–6%) when human activity was higher (Fig. 2c; coefficient = 0.077; 95% CI = –0.001–0.156). This contrast highlights an important interaction between human modification of a landscape and human activity therein–between human footprint and footfalls—which we posit could be the result of two factors. First, local extirpations of sensitive species (species ‘filtering’¹⁹) would result in only human-tolerant species persisting in developed areas—for example, sensitive wolverine (Gulo gulo) were absent from sites with intermediate to high human modification. Second, species found across the gradient, such as mule deer (Odocoileus hemionus), could become habituated to benign human presence in more developed landscapes and therefore be less fearful of human activity than their conspecifics in less-developed areas²⁰. Notably, this
<figure><img src="image_4.png" /><figcaption>**Fig. 1 | Camera-trap sampling of contrasts between periods of higher versus lower human activity.** a, Location of camera-trap projects included in the analysis (n = 102). b,c, Examples for two projects: Edmonton, Canada (b) and Danum Valley, Malaysia (c) showing time series of human detections for the two types of comparisons used to assess the effects of higher human activity on</figcaption></figure>

animals. b, A between-year comparison with increased human activity during the COVID-19 pandemic (treatment, red shading) relative to the same time period the year before (control, blue shading). c, A within-year comparison with decreased human activity during the pandemic (control, blue shading) relative to the prepandemic period (treatment, red shading).

relationship with landscape modification varied predictably across trophic groups (Fig. 2d and Supplementary Table 3). Small and large carnivores, small herbivores and small omnivores increased their activity with higher human activity in developed areas (increasing by an average of 54%), while the response was much weaker for large herbivores and in fact opposite for large omnivores, which decreased activity when human activity increased in more modified landscapes (50% decrease; Fig. 2d). This negative response was common across all of the frequently detected large omnivores–wild boar (*Sus scrofa*), American black bear (*Ursus americanus*) and brown bear (*Ursus arctos*)—and could be driven by their attraction to anthropogenic food resources (for example garbage and fruit trees) that may be less risky to access when human activity is reduced²¹.

Animal detections were also more likely to decline with higher human activity in more open habitat types such as grasslands or deserts, relative to closed habitats such as forests (Fig. 2c; coefficient = -0.172; 95% CI = -0.3428 to -0.0018). This is consistent with predictions under the landscape of fear framework that suggest that animal perceptions of risk are influenced by availability of cover²². Contrary to our expectations, we did not find strong evidence that the magnitude of change in human activity (measured by camera traps or the stringency index; Table 1) affected animal responses or that hunted populations changed their amount of activity more than non-hunted ones (Supplementary Tables 2, 4 and 5). We also did not find strong support for the hypothesis that species with relatively larger brains—as an index of behavioural plasticity²³—would show more pronounced responses to changes in human activity (Supplementary Table 5).

## Changes in timing of animal activity

Whether or not animals change their intensity of use of an area, they could shift their timing of activity to minimize overlap with increasing human activity (Fig. 3a)²⁴. We measured changes in animal nocturnality (proportion of night time detections) across 499 populations (Meth-ods) and found considerable variation in animal responses to increasing human activity (though generally less than for amount of activity): from fivefold increases in nocturnality to sixfold decreases (mean change in proportion of nocturnal detections = 0.008; 95% CI = -0.02-0.04; Fig. 3b). The strongest predictor of changes in nocturnality was the degree of landscape modification (HMI): in more developed areas, animals tended to become more nocturnal as human activity increased (19.3% increase in nocturnality; Fig. 3c, coefficient = 0.047; 95% CI = 0.026-0.069; Supplementary Table 6). This is consistent with previous evidence of increasing wildlife nocturnality in the face of grow-ing human impacts²⁴ and highlights the importance of the temporal refuge provided by night time cover for human-wildlife coexistence in increasingly human-dominated environments²⁵.

Paralleling our findings about changes in the amount of animal activity, trophic group was also an important predictor of changes in nocturnality, with large carnivores becoming notably more noc-turnal than other groups (+5.3%; Fig. 3c and Supplementary Table 6). Again, we found support for an interaction between human modi-fication and trophic group: most groups had stronger increases in nocturnality along the disturbance gradient as human activity increased (mean +22.6%), whereas the increases in nocturnality for large carnivores did not vary with land-use disturbance (Fig. 3d and Supplementary Table 7). This finding could reflect greater
<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /></figure>

**Fig. 2 | Changes in the amount of animal activity in response to increasing human activity.** a, Interpretation of effects. b, Estimated effect sizes (black points) and variances (coloured lines) for all populations included in the analysis (n = 1,065 project-species combinations from 102 independent projects; two example species highlighted) with the global mean (and 95% quantiles) plotted in black to the right. c, Estimated model coefficients (points) and 95% CIs (lines; n = 1,065 project-species combinations from 102 independent projects) for additive factors (with complete data; Methods) hypothesized to influence changes in the amount of animal activity when human activity is higher, where: intercept is diurnal, large herbivore in closed habitat type with a seasonal comparison and all other effects are contrasts. d, Model predictions for the interaction between trophic group and HMI.

<figure><img src="image_8.png" /></figure>

sensitivity of large carnivores to the increased risk of conflict associated with more human presence²⁶, such that they shift timing of activity to minimize overlap regardless of landscape context. Other groups increased night time activity only in landscapes with higher risk of human encounters (that is, more modification), which may in turn enable the increases in amount of activity observed for many of these species (Fig. 2d).

Unlike for the amount of activity, changes in the timing of animal activity were mediated by the hunting status of species in an area, whereby hunted animals showed stronger increases in nocturnal behaviour at higher levels of landscape modification (+26.6%) relative to their non-hunted counterparts (+13.5%; Fig. 3e and Supplementary Table 8). We did not find strong evidence that relative brain size was associated with shifts in animal nocturnality, nor that the magnitude of change in the amount of human activity explained variation in animal responses (Fig. 3c and Supplementary Tables 6 and 9). We did find an effect of our comparison type such that, on average, comparisons between years showed larger shifts in nocturnality than within-year comparisons (Fig. 3c and Supplementary Table 6), underscoring the importance of temporal matching to minimize influence of other factors such as seasonal changes in activity patterns.

## Implications for human–wildlife coexistence

Contrary to popular narratives of animals roaming more widely while people sheltered in place during early stages of the COVID-19 pandemic, our results reveal tremendous variation and complexity in animal responses to dynamic changes in human activity. Using a unique synthesis of simultaneous camera-trap sampling of people and hundreds of mammal species around the world, combined with a powerful before–after quasi-experimental design, we quantified how animals change their behaviours under higher levels of human activity across gradients of human footprint. As the human population continues to grow, the persistence of wild animals will depend on their responses to increasing human presence in both highly and moderately modified landscapes. It may thus be encouraging that many animal populations did not show dramatic changes in the amount or timing of their activity under conditions of higher human activity. Indeed, mean changes across all populations assessed were close to zero, suggesting that there was no global systematic shift in animal activity during the pandemic, consistent with other recent observations of highly variable animal responses¹³,²⁷. Nevertheless, we saw stronger responses to human activity for certain species and contexts and these patterns can help us better understand and mitigate negative impacts of people on wildlife communities.
<figure><img src="image_9.png" /><figcaption>**Fig. 3** | *Changes in animal nocturnality in response to increasing human activity.* a, Interpretation of effects. b, Estimated effect sizes (black points) and variances (coloured lines) for all populations included in the analysis (n = 499 project-species combinations from 100 independent projects; two example species highlighted) with the global mean (with 95% quantiles) plotted in black to the right. c, Estimated model coefficients (points) and 95% CIs (lines; n = 499 project-species combinations from 100 independent projects) for additive factors (with complete data; Methods) hypothesized to influence changes in animal nocturnality when human activity is higher, where: intercept is nocturnal, large herbivore in closed habitat type with a seasonal comparison and all other effects are contrasts. d, Model predictions for interaction between trophic group and human modification index. e, Model predictions for interaction between hunting and HMI.</figcaption></figure>

One striking pattern is that animal responses to human activity varied with the degree of human landscape modification. Our results imply that risk tolerance and associated behaviours vary between wildlife in more- versus less-developed contexts. As human activity increased, many species in more modified landscapes surprisingly had higher overall activity, although this activity was more nocturnal, suggesting that animals persisting in these developed environments may be attracted to anthropogenic resource subsidies but still seek ways to minimize encounters with people through partitioning time²⁸. Wildlife managers in such modified environments should anticipate some animal habituation and manage the timing of human activity to protect night time refuges that promote human-wildlife coexistence—particularly for hunted species that showed the strongest shifts toward nocturnality. On the other hand, regulating the amount of human activity may be more important in less-developed landscapes where we detected the greatest declines in animal activity with increasing human activity. Such remote landscapes are often spatial refuges for sensitive species that may be filtered out as human modification increases; yet these areas face increasing demands from popular pursuits, such as outdoor recreation and nature-based tourism¹⁸, and may also be more difficult to protect from illegal hunting, encroachment or resource extraction²⁹.

The sensitivity of species to human footprint and footfalls varied by trophic group and body size, as did the interplay of space and time in behavioural responses. Both large and small carnivore species were among the more sensitive to changes in human activity, generally reducing their activity levels and exhibiting more nocturnality with higher human activity. This motivates a continued emphasis on carnivore behaviour and management as a key challenge for human-wildlife coexistence, given the threatened status of many carnivores, the risk of negative outcomes of human-carnivore encounters and the ecological importance of carnivores as strongly interacting species⁷,³⁰. Avoidance of people by carnivores could be beneficial if it reduces human-carnivore conflict²⁵,²⁸ but it could also lead to different types of conflict if it results in lower predation rates on herbivores near people, as seen in overbrowsing by habituated deer¹. Indeed, large herbivores showed the strongest increases in activity with higher human activity in our study, consistent with habituation and increased risk of conflict. Large omnivores, such as bear and boar, were unique in both spatially and temporally avoiding higher human activity in more developed environments, underscoring that management efforts to regulate human activity and create spatial or temporal refuges may lead to outcomes that differ by species and setting. Managers must pay particular attention to the
prospect that such differential responses can alter species interactions and cause knock-on effects with broader consequences for ecosystem functions and services³¹,³².

Our study highlights the value of learning from unplanned 'experiments' caused by rapid changes in human activity³³ and other extreme events (for example, ref. 34). These insights are enabled by sampling methods, such as camera trapping, that facilitate standardized, continuous monitoring of diverse animal assemblages and humans across varied landscape contexts. While many studies of the anthropause focused on wildlife observations by volunteers in more accessible urban environments (for example, ref. 35), our results emphasize that animal responses to changes in human activity differ between more- and less-developed landscapes. This context-dependency should be a focus of further research, including expanded assessment of contexts and species under-represented in our sample, such as those in tropical regions subjected to different pressures during the pandemic³⁶. Many geographic and taxonomic gaps in global biodiversity monitoring remain and must be filled by cost-effective networks that gather reliable evidence across several scales; standardized camera-trap programmes and infrastructure are helping to do so³⁷,³⁸. As the cumulative effects of the human enterprise put pressure on ecosystems worldwide³⁹, bending the curve of biodiversity loss will require context-specific knowledge on ecological responses to human actions that can guide locally appropriate and globally effective conservation solutions.

# Methods

## Data collection

We issued a call in September 2020 to camera-trap researchers around the world for contributions of camera-trap data from before and during the onset of the COVID-19 pandemic and associated restrictions on human activity¹⁰,¹¹. This initial call included a social media post (Twitter, now X) and targeted emails to 143 researchers in 37 countries. We requested datasets that adhered to global camera-trap metadata standards (Wildlife Insights³⁸) and received submissions from 146 projects. Submitted data were summarized using a standardized script and evaluated according to the following key criteria: (1) most or all camera-trap stations were deployed in the same area of interest (hereafter site) before and during COVID-19-related restrictions; (2) a minimum of seven unique camera-trap deployment locations (stations) were sampled; (3) a minimum sampling effort of at least 7 days per camera period (see below); and (4) trends in human detections were recorded from camera-trap data (that is, detections of humans) or human activity for a given sampling area was available from other sources (for example, lockdown dates and local knowledge).

We only included detections of wild mammal species ≥1 kg (mean species body mass in kg obtained from ref. 40; we excluded domestic animals, which represented only 6% of overall detections and were associated with humans) and humans (excluding research personnel servicing cameras). Our full dataset for the next step of analysis included 112 projects sampling across 5,653 cameras for 329,535 camera-days (see below for data included in specific models). The mean number of camera locations per project was 42 (range 6–300) and mean camera-days per project was 2,945 (range 348–27,986). Camera locations were considered independent within projects, as no paired cameras were included (see Supplementary Table 10 for more details on camera deployments and spacing).

## Experimental design

For each project, we first reviewed site-level trends in independent detection events of humans (using a standardized 30 min interval: that is, a detection was considered independent if >30 min from previous detection at the same camera station) to identify whether there were changes in human activity associated with COVID-19 restrictions in 2020. We sought to identify two comparable sampling periods that differed in human activity but were otherwise similar (for example,

in camera locations and sampling effort) and thus could be used as a quasi-experimental comparison to assess wildlife responses to the change in human activity. We initially anticipated that human activity would be reduced during COVID-19 lockdowns (that is, the anthropause¹¹) but observed a wide variety of patterns of human detections across datasets, including decreases, increases and no change in human detections between sampling before and during COVID-19 (Supplementary Fig. 1). Since our primary interest was in evaluating wildlife responses to changes in human activity and in general we anticipate increases in human activity during the Anthropocene, we standardized our treatments to represent increases in human activity. In other words, we defined a 'control' period as one with lower human activity and a 'treatment' period as one with higher human activity, regardless of which occurred before or during the COVID-19 pandemic (Fig. 1).

We identified start and end dates for each period on the basis of clear changes in human detections (determined from visual inspection of daily detections; Fig. 1). For some projects, dates corresponded to known dates of local COVID-19 lockdowns or changes in study design (for example, dates of camera placement or removal). We prioritized comparison between years when data were collected in similar periods in years before 2020 (n = 95 projects). If multiyear data were not available, we selected comparison periods before and after the onset of lockdowns around March 2020 (with specific dates chosen according to local lockdown conditions; n = 17). If there were several potential treatment periods, we prioritized periods on the basis of the following ordered criteria: (1) the fewest seasonal or ecological confounds; (2) the most similar study design; (3) the greatest sampling effort; and (4) the most recent time period. Of the 95 projects for which we made comparisons between 2020 and a previous year, we used 2019 for 88 projects, 2018 for 6 and 2017 for 1.

In cases where there was no noticeable difference in human detections between candidate periods, or there were insufficient human detections from camera traps, we used other data or local knowledge of changes in human activity (for example, lockdown dates and visitor use data) from co-authors responsible for the particular project. Of the 112 projects included in our initial analyses, 15 used this expert opinion to determine changes in human activity. After completing our initial categorization of comparison periods, we shared details with all data contributors for review and adjustment, if necessary, based on expert knowledge of a given study area. Contributors were asked whether our delineation of sampling periods as being high versus low in human activity corresponded with their knowledge of the study system. We also asked them to consider whether other sources of environmental variation (for example, fire, drought, seasonal or interannual variation) or sampling design could confound the attribution of changes in wildlife detections to changes in human activity. After this evaluation and review, we retained 102 project datasets that had a detectable change in human activity between a treatment and control period for subsequent statistical modelling. These projects spanned 21 countries, mostly in North America and Europe but with some representation from South America, Africa and Southeast Asia (Fig. 1 and Supplementary Table 10).

Our paired treatment-control design makes several assumptions. For instance, we assumed that either: (1) changes in human activity occurred in the same direction throughout the entire study area within the treatment period; (2) the direction of the average effect was more important than variation in direction across camera sites; (3) variation in human activity within a study area was lower than differences in human activity between the treatment (higher activity) and control (lower activity) periods. By standardizing our treatment to be the period of higher human activity, we also assumed that the temporal direction of change did not affect animal responses.

## Data analysis

We compared two response variables between treatment and control periods to assess wildlife responses to changes in human activity: the
amount of animal activity and the timing of animal activity (described below). We used a two-stage approach in which we first estimated the direction and magnitude of change in these responses between periods for each species and then used a meta-analytical approach to evaluate the degree to which a set of candidate predictor variables explained variation in estimated responses. All data manipulation and analysis were done using R statistical software (v.4.1.3; ref. 41).

**Amount of animal activity.** To evaluate changes in the amount of animal activity, we quantified detection rates for each mammal species (and humans) at each camera for the treatment and control periods of each project. Specifically, we calculated the number of independent detections for a given species and camera station using a standardized 30 min interval (that is, detection was considered independent if >30 min from previous detection of the same species at the same camera station), while controlling for variation in sampling effort (log of camera-days included as an offset in models). We assumed that this detection rate (sometimes termed relative abundance index¹⁶) measured the relative intensity of habitat use by a species at a camera station, which reflects both the local abundance of the species (number of individuals in sampled area) and the movement patterns of individuals.

To quantify the magnitude of change in the amount of animal activity, we first ran single-species models to estimate changes in detection rates for species and humans between the comparison periods for each project. The response variable was the count of independent detection events, modelled as negative binomial, with an offset for active camera-days. Treatment was included as a fixed effect and a random intercept was included for camera station where the same camera locations were sampled in both periods (no random effect was included if a project used different camera locations between periods). All models were implemented using the glmmTMB package⁴². These models produced a regression coefficient (effect size) for each project-species population (humans and animals) representing the estimated magnitude of change in the amount of activity between the control period and the treatment period (and its corresponding sampling variance).

**Timing of animal activity.** To assess changes in timing of animal activity, we first classified each independent detection of a given species within a given project as 'day' or 'night'. We used the lutz package to convert all local times to UTC⁴³. We calculated the angle of the sun at the time of the first image in each detection using the sunAngle function in the oce package⁴⁴, based on the UTC time and latitude and longitude of the camera deployment location. Negative sun angles corresponded to 'night' (between sunset and sunrise) and positive sun angles to 'day' (between sunrise and sunset). Following ref. 24, we calculated an index of nocturnality, *N*, as the proportion of independent camera-trap detections that occurred during the night (*N* = detections during night/ (detections during night + detections during day)) for all species which had ten or more detections in both the control and treatment periods. We then calculated the log risk ratio, RR and its corresponding sampling variance (weighted by sample size) between the treatment and control periods, pooled across all camera traps within a given study using the escalc() function within the metafor package⁴⁵. This effect size compared the percentage of animal detections that occurred at night with high human activity (N_h) to night time animal activity under low human activity (N_l), with RR = ln(N_h/N_l). A positive RR indicated a relatively greater degree of nocturnality in response to human activity, while a negative RR indicated reduced nocturnality.

**Hypothesized explanatory variables.** We identified and calculated a set of variables that we hypothesized would affect species responses to changes in human activity. These fell into four general classes: (1) species traits, (2) habitat (that is, vegetation) structure, (3) anthropogenic landscape modification and (4) magnitude of human change

(Table 1). We did not include any covariates reflecting differences in camera-trap sampling protocols between projects, as our estimates of species responses were made within projects (that is, comparing treatment versus control periods) and thus sampling methods were internally consistent within projects (for example, camera placement and settings).

**Species traits.** We hypothesized that species with the following traits would be more sensitive to changes in human activity (that is, more vulnerable or risk averse): larger body mass⁴⁶, higher trophic level⁴⁶, narrower diet and habitat breadth⁴⁷, diurnal activity⁴⁶ and smaller relative brain size⁴⁸. We extracted variables for each species from the COMBINE database⁴⁰, the most comprehensive archive of several mammal traits curated to date (representing 6,234 species). Given that some traits in the database were imputed, we reviewed the designations for plausibility and cross-referenced the traits with other widely used databases—specifically Elton Traits⁴⁹ and PanTHERIA⁵⁰—and made the following corrections to the 'activity cycle' trait (diurnal, nocturnal and cathemeral): diurnal to cathemeral—Mellivora capensis, Neofelis nebulosa, Neofelis diardi; diurnal to nocturnal—Meles meles; nocturnal to diurnal—Phacochoerus africanus; nocturnal to cathemeral—Ursus americanus. To calculate relative brain size we divided log-transformed brain mass by log-transformed body mass (as in ref. 48). We combined body mass and trophic level into a new variable 'trophic group' (consisting of small- or large-bodied categories for each of the three trophic levels, Table 1). Dietary and habitat breadth are described in ref. 40.

We further hypothesized that animals in hunted populations would be more sensitive to changes in human activity. We requested that all data contributors complete a survey indicating whether a given species was hunted within their project survey area, from which we created a binary factor representing hunting status for each population (1 = hunted; 0 = not hunted).

**Habitat structure.** Camera-trap surveys included in our analysis covered an extensive range of biogeographic areas and habitat types. We made the simplifying assumption that species responses to changes in human activity would be most influenced by the degree of openness of habitat (that is, vegetation structure) in a sampling area. More specifically, we hypothesized that areas with more open habitat types would have higher visibility and thus less security cover for animals and thus that animals in these open habitats would be more sensitive to increases in human activity than would animals in more closed habitats with greater security cover³¹. We used the Copernicus Global Land Cover dataset (100 m resolution⁵²) via Google Earth Engine to extract land cover class at each camera station. We then used the percentage canopy cover of the mode class across all cameras in a given project to define if the survey occurred in primarily closed (>70% canopy cover) or open habitat types (0–70% canopy cover).

**Land cover disturbance.** We posited that animal responses to changes in human activity would differ according to the degree of anthropogenic landscape modification (that is, human footprint¹,⁵³). More specifically, we identified two hypotheses that could underlie variation in species responses as a function of land cover disturbance. On the one hand, our 'habituation hypothesis' predicts that animals in more disturbed landscapes may be less sensitive to changes in human activity (relative to animals in undisturbed landscapes) and thus show less of a negative response or even a positive response as they have already behaviourally adapted to tolerate co-occurrence with people²². On the other hand, our 'plasticity hypothesis' predicts that the ability of animals to coexist with people in disturbed landscapes may be dependent on plasticity in animal behaviour²², such that animals in these landscapes may show more pronounced and rapid responses to changes in human activity (for example, avoidance of areas and times with greater chance of encountering people).
We initially characterized landscape disturbance using three vari-
ables accessed via Google Earth Engine: Gridded Population of the
World (1 km resolution⁵⁴), road density (m km⁻², 8 km resolution; Global
Roads Inventory Project⁵⁵) and HMI (for 2016 at 1 km resolution), which
represents a cumulative measure of the proportion of a landscape
modified by 13 anthropogenic stressors⁵³. Point values were extracted
for each camera station in each site, then the project-level medians
were used in analysis. As the median values of these three variables
were highly correlated across projects (Supplementary Fig. 2), we only
used HMI in our subsequent models.

**Magnitude of human change.** We expected that animal responses would be more pronounced in areas that underwent greater changes in human activity and we used two measures to assess the magnitude of those changes. At a coarse scale, we used the COVID-19 stringency index¹⁴, which characterizes the policies restricting human activities within a given geographic region at a daily time scale and has been widely used in studies of COVID-19 on human mobility and the environment (for example, ref. 13). We used the finest-scale regional data available for each project, which was usually at the country level, with the exception of three countries with province- or state-level data (Brazil, Canada and the United States). When projects spanned several countries, provinces or states, we used the stringency index for the region in which most cameras were located. For each region, we calculated the median stringency for the treatment and control sampling periods.

At a finer scale, we used the effect size for the modelled change in
camera-trap detection rates of humans across all cameras in a project
(as described above under 'amount of animal activity'). Models with
this variable excluded 15 projects that either did not detect humans
with camera traps or the number of humans detected on cameras was
not perceived by the data contributor to be an accurate reflection of
change in human use for the sampled area.

**Meta-analysis models.** To understand which factors mediated the effect of increasing human use on animal activity, we ran mixed-effect meta-analytic models using the rma.rmv() function of the metafor package⁴⁵ on the effect sizes and sampling variances of the two response variables described above (amount and timing of animal activity). Our unit of observation for modelling was the estimated response for each project–species combination (that is, each animal population) and we included random intercepts for project and for species nested within family, to account for repeated observations within each of those higher-level groups and for phylogenetic relatedness within families. All continuous predictor variables (Table 1) were standardized to unit variance with a mean of zero using the stdize function in the MuMln package⁵⁶. We tested pairwise correlations among all predictor variables and found that none were highly correlated (that is, all below a threshold of Pearson $|r| < 0.6$; Supplementary Fig. 2) and thus all were retained for modelling.

We performed our analysis in three steps for each of the two wild-
life response variables. First, we fit a global model including all hypoth-
esized predictor variables for which we had complete data (excluding
hunting status, relative brain size and empirical magnitude of human
change, for which we had incomplete data and thus included in analysis
of subsets of data, described below). Second, we used model selection
to test for plausible interactions and nonlinear effects. Third, we used
model selection on subsets of the full data to compare the global and
interactions models with candidate models adding three more predic-
tor variables with incomplete data.

**Global model.** As all of our predictor variables were independent, we used a global model approach that included additive fixed effects for all predictor variables (Table 1). We interpreted the *P* value of each effect contrast to indicate statistically significant support (at *P* < 0.05 or marginal support at *P* < 0.10) for a consistent effect direction of a given predictor and we used the estimated effect size as a measure of effect

magnitude. We calculated the pseudo-$R^2$ to estimate the total variation
explained by our global models. We also calculated the $\hat{L}^2$ (ref. 57) of
each global model to determine the amount of heterogeneity observed
between the random effect levels; consistent variation in the response
terms between projects, families and species would result in higher
$\hat{L}^2$ values compared to the null model with no fixed effects. To aid inter-
pretation, we present effect sizes in terms of the proportional change
(%) in model-predicted responses across lowest-to-highest values for
continuous predictors (for example, HMI) or between two categories
of interest (for example, trophic groups).

**Model selection of plausible interactions and nonlinear terms.**
To explore the possibility of context-specific effects of the predic-
tors of wildlife responses to changes in human activity, we assessed a
suite of ecologically plausible interaction and nonlinear (quadratic)
terms through adding them in turn to the global model and using
Akaike's Information Criterion (corrected for small sample size, AICc)
to find the most parsimonious model. We assessed the following terms:
(1) 'HMI * habitat_closure', to evaluate the potential for habitat structure
to mediate responses to human landscape modification;
(2) 'trophic_group * HMI', to evaluate the potential for different trophic groups
to respond to human modification in different ways;
(3) 'trophic_group * habitat_closure', to evaluate the potential for different trophic groups
to respond to habitat structure in different ways; and
(4) $HMI^2$, to assess nonlinear effects of wildlife responses to human modification. Models
including the candidate interaction or nonlinear terms were compared
to the global model without interaction terms using AICc (in the MuMln
package<sup>56</sup>) and were discussed above if they were within 2 AICc of the
best-supported model and there was no simpler, nested model with
more support.

**Model selection on subsets of data.** We had a small amount of missing information in the data available for assessing the effects of population hunting status, species relative brain size and empirical (that is, camera-trap-based) magnitude of change in human activity (91.7%, 98.8% and 86.5% of project-species had data for these variables, respectively). Therefore, we ran the same global model used for the full dataset on the subsetted data along with candidate models including each of these predictor variables and all plausible interactions of interest (as above). These additional candidate models were compared to the global model (run on the same partial dataset) using AICc and were discussed in the results if they resulted in a lower AICc value (that is, had more support than the global model, which was a simpler nested model).

**Reporting summary**

Further information on research design is available in the Nature
Portfolio Reporting Summary linked to this article.

**Data availability**

The data used in this paper are available in Figshare, with the identifier:
https://doi.org/10.6084/m9 figshare.23506536.

**Code availability**

The code used to analyse the data and create the figures in this paper
are available in Figshare, with the identifier: https://figshare.com/
articles/software/Analysis_R Code/23506512.

**References**

1. Venter, O. et al. Sixteen years of change in the global terrestrial human footprint and implications for biodiversity conservation. *Nat. Commun.* **7**, 12558 (2016).

2. Suraci, J. P., Clinchy, M., Zanette, L. Y. & Wilmers, C. C. Fear of humans as apex predators has landscape-scale impacts from mountain lions to mice. *Ecol. Lett.* **22**, 1578–1586 (2019).
3. Berger, J. Fear, human shields and the redistribution of prey and predators in protected areas. *Biol. Lett.* **3**, 620–623 (2007).

4. McShea, W. J. Ecology and management of white-tailed deer in a changing world. *Ann. NY Acad. Sci.* **1249**, 45–56 (2012).

5. Suraci, J. P. et al. Disturbance type and species life history predict mammal responses to humans. *Glob. Change Biol.* **27**, 3718–3731 (2021).

6. Pacifici, M. et al. Global correlates of range contractions and expansions in terrestrial mammals. *Nat. Commun.* **11**, 2840 (2020).

7. Ripple, W. J. et al. Status and ecological effects of the world's largest carnivores. *Science* **343**, 1241484 (2014).

8. Kays, R. et al. Does hunting or hiking affect wildlife communities in protected areas? *J. Appl. Ecol.* **54**, 242–252 (2017).

9. Reilly, C. M., Suraci, J. P., Smith, J. A., Wang, Y. & Wilmers, C. C. Mesopredators retain their fear of humans across a development gradient. *Behav. Ecol.* **33**, 428–435 (2022).

10. Bates, A. E., Primack, R. B., Moraga, P. & Duarte, C. M. COVID-19 pandemic and associated lockdown as a 'Global Human Confinement Experiment' to investigate biodiversity conservation. *Biol. Conserv.* **248**, 108665 (2020).

11. Rutz, C. et al. COVID-19 lockdown allows researchers to quantify the effects of human activity on wildlife. *Nat. Ecol. Evol.* **4**, 1156–1159 (2020).

12. Basile, M., Russo, L. F., Russo, V. G., Senese, A. & Bernardo, N. Birds seen and not seen during the COVID-19 pandemic: the impact of lockdown measures on citizen science bird observations. *Biol. Conserv.* **256**, 109079 (2021).

13. Bates, A. E. et al. Global COVID-19 lockdown highlights humans as both threats and custodians of the environment. *Biol. Conserv.* **263**, 109175 (2021).

14. Hale, T. et al. A global panel database of pandemic policies (Oxford COVID-19 Government Response Tracker). *Nat. Hum. Behav.* **5**, 529–538 (2021).

15. Procko, M., Naidoo, R., LeMay, V. & Burton, A. C. Human impacts on mammals in and around a protected area before, during and after COVID-19 lockdowns. *Conserv. Sci. Pract.* **4**, e12743 (2022).

16. Burton, A. C. et al. Wildlife camera trapping: a review and recommendations for linking surveys to ecological processes. *J. Appl. Ecol.* **52**, 675–685 (2015).

17. Chen, C. et al. Global camera trap synthesis highlights the importance of protected areas in maintaining mammal diversity. *Conserv. Lett.* **15**, e12865 (2022).

18. Naidoo, R. & Burton, A. C. Relative effects of recreational activities on a temperate terrestrial wildlife assemblage. *Conserv. Sci. Pract.* **2**, e271 (2020).

19. Betts, M. G. et al. Extinction filters mediate the global effects of habitat fragmentation on animals. *Science* **366**, 1236–1239 (2019).

20. Lowry, H., Lill, A. & Wong, B. B. M. Behavioural responses of wildlife to urban environments. *Biol. Rev.* **88**, 537–549 (2013).

21. Klees van Bommel, J., Badry, M., Ford, A. T., Golumbia, T. & Burton, A. C. Predicting human-carnivore conflict at the urban-wildland interface. *Glob. Ecol. Conserv.* **24**, e01322 (2020).

22. Gaynor, K. M., Brown, J. S., Middleton, A. D., Power, M. E. & Brashares, J. S. Landscapes of fear: spatial patterns of risk perception and response. *Trends Ecol. Evol.* **34**, 355–368 (2019).

23. González-Lagos, C., Sol, D. & Reader, S. M. Large-brained mammals live longer. *J. Evol. Biol.* **23**, 1064–1074 (2010).

24. Gaynor, K. M., Hojnowski, C. E., Carter, N. H. & Brashares, J. S. The influence of human disturbance on wildlife nocturnality. *Science* **360**, 1232 (2018).

25. Carter, N. H., Shrestha, B. K., Karki, J. B., Pradhan, N. M. B. & Liu, J. Coexistence between wildlife and humans at fine spatial scales. *Proc. Natl Acad. Sci. USA* **109**, 15360–15365 (2012).

26. Packer, C. et al. Conserving large carnivores: dollars and fence. *Ecol. Lett.* **16**, 635–641 (2013).

27. Tucker, M. A. et al. Behavioral responses of terrestrial mammals to COVID-19 lockdowns. *Science* **380**, 1059–1064 (2023).

28. Lamb, C. T. et al. The ecology of human-carnivore coexistence. *Proc. Natl Acad. Sci. USA* **117**, 17876 (2020).

29. Ripple, W. J. et al., Bushmeat hunting and extinction risk to the world's mammals. *R. Soc. Open Sci.* **3**, 160498 (2016).

30. Soulé, M. E., Estes, J. A., Berger, J. & Del Rio, C. M. Ecological effectiveness: conservation goals for interactive species. *Conserv. Biol.* **17**, 1238–1250 (2003).

31. Estes, J. A. et al. Trophic downgrading of planet earth. *Science* **333**, 301–306 (2011).

32. Raynor, J. L., Grainger, C. A. & Parker, D. P. Wolves make roadways safer, generating large economic returns to predator conservation. *Proc. Natl Acad. Sci. USA* **118**, e2023251118 (2021).

33. Rutz, C. Studying pauses and pulses in human mobility and their environmental impacts. *Nat. Rev. Earth Environ.* **3**, 157–159 (2022).

34. Ward, M. et al. Impact of 2019–2020 mega-fires on Australian fauna habitat. *Nat. Ecol. Evol.* **4**, 1321–1326 (2020).

35. Schrimpf, M. B. et al. Reduced human activity during COVID-19 alters avian land use across North America. *Sci. Adv.* **7**, eabf5073 (2021).

36. Lindsey, P. et al. Conserving Africa's wildlife and wildlands through the COVID-19 crisis and beyond. *Nat. Ecol. Evol.* **4**, 1300–1310 (2020).

37. Kays, R. et al. SNAPSHOT USA 2020: a second coordinated national camera trap survey of the United States during the COVID-19 pandemic. *Ecology* **103**, e3775 (2022).

38. Ahumada, J. A. et al. Wildlife Insights: a platform to maximize the potential of camera trap and other passive sensor wildlife data for the planet. *Environ. Conserv.* **47**, 1–6 (2019).

39. Díaz, S. et al. Pervasive human-driven decline of life on Earth points to the need for transformative change. *Science* **366**, eaax3100 (2019).

40. Soria, C. D., Pacifici, M., Di Marco, M., Stephen, S. M. & Rondinini, C. COMBINE: a coalesced mammal database of intrinsic and extrinsic traits. *Ecology* **102**, e03344 (2021).

41. R Core Team. *R: A Language and Environment for Statistical Computing* (R Foundation for Statistical Computing, 2022).

42. Brooks, M. E. et al. glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. *R J.* **9**, 378–400 (2017).

43. Teucher A., lutz: Look up time zones of point coordinates. R package version 0.3.1 (2019).

44. Kelley D., Richards C., oce: Analysis of oceanographic data. R package version 1.7-10 (2022).

45. Viechtbauer, W. Conducting meta-analyses in R with the metafor package. *J. Stat. Softw.* **36**, 1–48 (2010).

46. Purvis, A., Gittleman, J. L., Cowlishaw, G. & Mace, G. M. Predicting extinction risk in declining species. *Proc. R. Soc. Lond. B* **267**, 1947–1952 (2000).

47. Chichorro, F., Juslén, A. & Cardoso, P. A review of the relation between species traits and extinction risk. *Biol. Conserv.* **237**, 220–229 (2019).

48. Benson-Amram, S., Dantzer, B., Stricker, G., Swanson, E. M. & Holekamp, K. E. Brain size predicts problem-solving ability in mammalian carnivores. *Proc. Natl Acad. Sci. USA* **113**, 2532–2537 (2016).

49. Wilman, H. et al. EltonTraits 1.0: species-level foraging attributes of the world's birds and mammals. *Ecology* **95**, 2027 (2014).

50. Jones, K. E. et al. PanTHERIA: a species-level database of life history, ecology and geography of extant and recently extinct mammals. *Ecology* **90**, 2648 (2009).
51. Stankowich, T. Ungulate flight responses to human disturbance: a review and meta-analysis. *Biol. Conserv.* **141**, 2159–2173 (2008).

52. Buchhorn, M. et al. Copernicus global land cover layers— Collection 2. *Remote Sens.* **12**, 1044 (2020).

53. Kennedy, C. M., Oakleaf, J. R., Theobald, D. M., Baruch-Mordo, S. & Kiesecker, J. Managing the middle: a shift in conservation priorities based on the global human modification gradient. *Glob. Change Biol.* **25**, 811–826 (2019).

54. Gridded Population of the World, Version 4 (GPWv4): Population Density (CIESIN, 2016).

55. Meijer, J. R., Huijbregts, M. A., Schotten, K. C. & Schipper, A. M. Global patterns of current and future road infrastructure. *Environ. Res. Lett.* **13**, 064006 (2018).

56. Bartoń, K. MUMIn: Multi-model inference. R package version 1.471 (2022).

57. Higgins, J. P. & Thompson, S. G. Quantifying heterogeneity in a meta-analysis. *Stat. Med.* **21**, 1539–1558 (2002).

## Acknowledgements

We recognize the tragic consequences of the COVID-19 pandemic and would like to acknowledge all people impacted. Full acknowledgements are provided in the Supplementary Information. This synthesis project was funded by the Natural Sciences and Engineering Research Council of Canada (Canada Research Chair 950-231654 and Discovery Grant RGPIN-2018-03958 to A.C.B. and RGPIN-2022-03096 to K.M.G.) and the National Center for Ecological Analysis and Synthesis (Director's Postdoc Fellowship to K.M.G.). Additional funding sources for component subprojects are listed in the Supplementary Information.

## Author contributions

A.C.B., C. Beirne, R.K., K.M.G., C. Sun and A. Granados conceived this work. A.C.B., C. Beirne, R.K., K.M.G., A. Granados, C. Sun and F.C. were responsible for data curation. C. Beirne and K.M.G. conducted the formal analysis. A.C.B., R.K. and K.M.G. acquired funding. A.C.B., C. Beirne, K.M.G., C. Sun, A. Granados, M.L.A., J.M.A., G.C.A., F.S.Á.C., Z.A., C.A.-D., C.A., S.A.-A., G.B., A.B.-M., D.B., E.B., E.L.B., C. Baruzzi, S.M.B., N. Beenaerts, J. Belmaker, O.B., B.B., T.B., D.A.B., N. Bogdanović, A.B., M.B., L.B., J.F.B., J. Brooke, J.W.B., F.C., B.S.C., J. Carvalho, J. Casaer, R. Černe, R. Chen, E.C., M.C., C. Cincotta, D.Č., T.D.C., J. Compton, C. Coon, M.V.C., A.P.C., S.D.F., A.K.D., M. Davis, K.D., V.D.W., E.D., T.A.D., J.D., M. Duła, S.E.-F., C.E., A.E., J.F.-L., J. Favreau, M.F., P.F., F.F., C.F., L.F., J.T.F., M.C.F.-R., E.A.F., U.F., J.F.I., J.M.F., A.F., B. Franzetti, S. Frey, S. Fritts, Š. Frýbová, B. Furnas, B.G., H.M.G., D.G.G., A.J.G., T.G., M.E.G., D.M.G., M.G., A. Green, R.H., R.(B.)H., S. Hammerich, C. Hanekom, C. Hansen, S. Hasstedt, M. Hebblewhite, M. Heurich, T.R.H., T.H., D.J., P.A.J., K.J.J., A.J., M.J., M.C.K., M.J.K., M.T.K., S.K.-S., M. Krofel, A.K., K.M.K., D.P.J.K., E.K.K., J.K., M. Kutal, D.J.R.L., S.L., M. Lashley, R. Lathrop, T.E.L.J., C.L., D.B.L., A.L., M. Linnell, J. Loch, R. Long, R.C.L., J. Louvrier, M.S.L., P.M., S.M., B.M., G.K.H.M., A.J.M., D.M., Z.M., T.M., W.J.M., M.M., C.M., J.J.M., C.M.M.-M., D.M.-A., K.M., C. Nagy, R.N., I.N., C. Nelson, B.O., M.T.O., V.O., C.O., F.O., P.P., K.P., L.P., C.E.P., M. Pendergast, F.F.P., R.P., X.P.-O., M. Price, M. Procko, M.D.P., E.E.R., N.R., S.R., K.R., M.R., R.R.-H., D.R., E.G.R., A.R., C. Rota, F.R., H.R., C. Rutz, M. Salvatori, D.S., C.M.S., J. Scherger, J. Schipper, D.G.S., Ç.H.Ş., P.S., J. Sevin, H.S., C. Shier, E.A.S.-R., M. Sindicic, L.K.S., A.S., T.S., C.C.S.C., J. Stenglein, P.A.S., K.M.S., M. Stevens, C. Stevenson, B.T., I.T., R.T.T., J.T., T.U., J.-P.V., D.V., S.L.W., J. Weber, K.C.B.W., L.S.W., C.A.W., J. Whittington, I.W., M.W., J. Williamson, C.C.W., T.W., H.U.W., Y.Z., A.Z. and R.K. were involved in reviewing and editing the final manuscript.

A. Granados, M. Lashley, J.M.A., G.C.A., F.S.Á.C., Z.A., C.A.-D., C.A., S.A.-A., G.B., A.B.-M., D.B., E.B., E.L.B., C. Baruzzi, S.M.B., N. Beenaerts, J. Belmaker, O.B., B.B., T.B., D.A.B., N. Bogdanović, A.B., M.B., L.B., J.F.B., J. Brooke, J.W.B., F.C., B.S.C., J. Carvalho, J. Casaer, R. Černe, R. Chen, E.C., M.C., C. Cincotta, D.Č., T.D.C., J. Compton, C. Coon, M.V.C., A.P.C., S.D.F., A.K.D., M. Davis, K.D., V.D.W., E.D, T.A.D., J.D., M. Duła, S.E.-F., C.E., A.E., J.F.-L., J. Favreau, M.F., P.F., F.F., C.F., L.F., J.T.F., M.C.F.-R., E.A.F., U.F., J.F.I., J.M.F., A.F., B. Franzetti, S. Frey, S. Fritts, Š. Frýbová, B. Furnas, B.G., H.M.G., D.G.G., A.J.G., T.G., M.E.G., D.M.G., M.G., A. Green, R.H., R.(B.)H., S. Hammerich, C. Hanekom, C. Hansen, S. Hasstedt, M. Hebblewhite, M. Heurich, T.R.H., T.H., D.J., P.A.J., K.J.J., A.J., M.J., M.C.K., M.J.K., M.T.K., S.K.-S., M. Krofel, A.K., K.M.K., D.P.J.K., E.K.K., J.K., M. Kutal, D.J.R.L., S.L., M. Lashley, R. Lathrop, T.E.L.J., C.L., D.B.L., A.L., M. Linnell, J. Loch, R. Long, R.C.L., J. Louvrier, M.S.L., P.M., S.M., B.M., G.K.H.M., A.J.M., D.M., Z.M., T.M., W.J.M., M.M., C.M., J.J.M., C.M.M.-M., D.M.-A., K.M., C. Nagy, R.N., I.N., C. Nelson, B.O., M.T.O., V.O., C.O., F.O., P.P., K.P., L.P., C.E.P., M. Pendergast, F.F.P., R.P., X.P.-O., M. Price, M. Procko, M.D.P., E.E.R., N.R., S.R., K.R., M.R., R.R.-H., D.R., E.G.R., A.R., C. Rota, F.R., H.R., C. Rutz, M. Salvatori, D.S., C.M.S., J. Scherger, J. Schipper, D.G.S., Ç.H.Ş., P.S., J. Sevin, H.S., C. Shier, E.A.S.-R., M. Sindicic, L.K.S., A.S., T.S., C.C.S.C., J. Stenglein, P.A.S., K.M.S., M. Stevens, C. Stevenson, B.T., I.T., R.T.T., J.T., T.U., J.-P.V., D.V., S.L.W., J. Weber, K.C.B.W., L.S.W., C.A.W., J. Whittington, I.W., M.W., J. Williamson, C.C.W., T.W., H.U.W., Y.Z., A.Z. and R.K. were involved in reviewing and editing the final manuscript.

## Competing interests

The authors declare no competing interests.

## Additional information

**Supplementary information** The online version contains supplementary material available at https://doi.org/10.1038/s41559-024-02363-2.

**Correspondence and requests for materials** should be addressed to A. Cole Burton.

**Peer review information** *Nature Ecology & Evolution* thanks Mason Fidino, Mahdieh Tourani and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

**Reprints and permissions information** is available at www.nature.com/reprints.

**Publisher's note** Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Open Access** This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

© The Author(s) 2024
A. Cole Burton 1,2,161 , Christopher Beirne 1,161 , Kaitlyn M. Gaynor 2,3,4 , Catherine Sun 1 , Alys Granados 1 , Maximilian L. Allen 5 , Jesse M. Alston 6 , Guilherme C. Alvarenga 7 , Francisco Samuel Álvarez Calderón 8 , Zachary Amir 9 , Christine Anhalt-Depies 10 , Cara Appel 11 , Stephanny Arroyo-Arce 12 , Guy Balme 13 , Avi Bar-Massada 14 , Daniele Barcelos 7 , Evan Barr 15 , Erika L. Barthelmess 16 , Carolina Baruzzi 17 , Sayantani M. Basak 18 , Natalie Beenaerts 19 , Jonathan Belmaker 20 , Olgirda Belova 21 , Branko Bezarević 22 , Tori Bird 23 , Daniel A. Bogan 24 , Neda Bogdanović 25 , Andy Boyce 26 , Mark Boyce 27 , LaRoy Brandt 28 , Jedediah F. Brodie 29,30 , Jarred Brooke 31 , Jakub W. Bubnicki 32 , Francesca Cagnacci 33,34 , Benjamin Scott Carr 35 , João Carvalho 36 , Jim Casaer 37 , Rok Černe 38 , Ron Chen 39 , Emily Chow 40 , Marcin Churski 32 , Connor Cincotta 41 , Duško Ćirović 25 , T. D. Coates 42 , Justin Compton 43 , Courtney Coon 44 , Michael V. Cove 45 , Anthony P. Crupi 46 , Simone Dal Farra 33 , Andrea K. Darracq 15 , Miranda Davis 47 , Kimberly Dawe 48 , Valerie De Waele 49 , Esther Descalzo 50 , Tom A. Diserens 32,51 , Jakub Drimaj 52 , Martin Duła 52,53 , Susan Ellis-Felege 54 , Caroline Ellison 55 , Alper Ertürk 56 , Jean Fantle-Lepczyk 57 , Jorie Favreau 41 , Mitch Fennell 1 , Pablo Ferreras 50 , Francesco Ferretti 34,58 , Christian Fiderer 59,60 , Laura Finnegan 61 , Jason T. Fisher 62 , M. Caitlin Fisher-Reid 63 , Elizabeth A. Flaherty 31 , Urša Fležar 38,64 , Jiří Flousek 65 , Jennifer M. Foca 27 , Adam Ford 66 , Barbara Franzetti 67 , Sandra Frey 62 , Sarah Fritts 68 , Šárka Frýbová 69 , Brett Furnas 70 , Brian Gerber 71 , Hayley M. Geyle 72 , Diego G. Giménez 73 , Anthony J. Giordano 73 , Tomislav Gomercic 74 , Matthew E. Gompper 75 , Diogo Maia Grábin 7 , Morgan Gray 76 , Austin Green 77 , Robert Hagen 78,79 , Robert (Bob) Hagen 80 , Steven Hammerich 76 , Catharine Hanekom 81 , Christopher Hansen 82 , Steven Hasstedt 83 , Mark Hebblewhite 29 , Marco Heurich 59,60,84 , Tim R. Hofmeester 85 , Tru Hubbard 86 , David Jachowski 87 , Patrick A. Jansen 88,89 , Kodi Jo Jaspers 90 , Alex Jensen 87 , Mark Jordan 91 , Mariane C. Kaizer 92 , Marcella J. Kelly 93 , Michel T. Kohl 35 , Stephanie Kramer-Schadt 79,94 , Miha Krofel 64 , Andrea Krug 95 , Kellie M. Kuhn 83 , Dries P. J. Kuijper 32 , Erin K. Kuprewicz 47 , Josip Kusak 74 , Miroslav Kutal 52,53 , Diana J. R. Lafferty 86 , Summer LaRose 96 , Marcus Lashley 97 , Richard Lathrop 98 , Thomas E. Lee Jr 99 , Christopher Lepczyk 57 , Damon B. Lesmeister 100 , Alain Licoppe 49 , Marco Linnell 100 , Jan Loch 101 , Robert Long 90 , Robert C. Lonsinger 102 , Julie Louvrier 79 , Matthew Scott Luskin 9 , Paula MacKay 90 , Sean Maher 103 , Benoît Manet 49 , Gareth K. H. Mann 13 , Andrew J. Marshall 104 , David Mason 97 , Zara McDonald 44 , Tracy McKay 61 , William J. McShea 26 , Matt Mechler 105 , Claude Miaud 106 , Joshua J. Millspaugh 82 , Claudio M. Monteza-Moreno 107 , Dario Moreira-Arce 108 , Kayleigh Mullen 23 , Christopher Nagy 109 , Robin Naidoo 110 , Itai Namir 20 , Carrie Nelson 111 , Brian O'Neill 112 , M. Teague O'Mara 113 , Valentina Oberosler 114 , Christian Osorio 115 , Federico Ossi 33,34 , Pablo Palencia 116,117 , Kimberly Pearson 118 , Luca Pedrotti 119 , Charles E. Pekins 120 , Mary Pendergast 121 , Fernando F. Pinho 7 , Radim Plhal 52 , Xochilt Pocasangre-Orellana 8 , Melissa Price 122 , Michael Procko 1 , Mike D. Proctor 123 , Emiliano Esterci Ramalho 7 , Nathan Ranc 33,124 , Slaven Reljic 74 , Katie Remine 90 , Michael Rentz 125 , Ronald Revord 96 , Rafael Reyna-Hurtado 126 , Derek Risch 122 , Euan G. Ritchie 127 , Andrea Romero 112 , Christopher Rota 128 , Francesco Rovero 114,129 , Helen Rowe 130,131 , Christian Rutz 132 , Marco Salvatori 114,129 , Derek Sandow 133 , Christopher M. Schalk 134 , Jenna Scherger 66 , Jan Schipper 135 , Daniel G. Scognamillo 136 , Çağan H. Şekercioğlu 77,137 , Paola Semenzato 138 , Jennifer Sevin 139 , Hila Shamon 26 , Catherine Shier 140 , Eduardo A. Silva-Rodríguez 141 , Magda Sindicic 74 , Lucy K. Smyth 13,142 , Anil Soyumert 56 , Tiffany Sprague 130 , Colleen Cassady St. Clair 27 , Jennifer Stenglein 10 , Philip A. Stephens 143 , Kinga Magdalena Stepniak 144 , Michael Stevens 145 , Cassondra Stevenson 27 , Bálint Ternyik 143,146 , Ian Thomson 12 , Rita T. Torres 36 , Joan Tremblay 47 , Tomas Urrutia 115 , Jean-Pierre Vacher 106 , Darcy Visscher 147 , Stephen L. Webb 148 , Julian Weber 149 , Katherine C. B. Weiss 135 , Laura S. Whipple 150 , Christopher A. Whittier 151 , Jesse Whittington 152 , Izabela Wierzbowska 18 , Martin Wikelski 107,153 , Jacque Williamson 154 , Christopher C. Wilmers 155 , Todd Windle 156 , Heiko U. Wittmer 157 , Yuri Zharikov 158 , Adam Zorn 159 & Roland Kays 45,160

<footnote n="1">¹Department of Forest Resources Management, University of British Columbia, Vancouver, British Columbia, Canada. ²Biodiversity Research Centre, University of British Columbia, Vancouver, British Columbia, Canada. ³Departments of Zoology and Botany, University of British Columbia, Vancouver, British Columbia, Canada. ⁴National Center for Ecological Analysis and Synthesis, Santa Barbara, CA, USA. ⁵Illinois Natural History Survey, Prairie Research Institute, University of Illinois, Champaign, IL, USA. ⁶School of Natural Resources and the Environment, University of Arizona, Tucson, AZ, USA.</footnote>

<footnote n="7">⁷Instituto de Desenvolvimento Sustentável Mamirauá, Tefé, Brazil. ⁸Fundación Naturaleza El Salvador, San Salvador, El Salvador. ⁹School of Biological Sciences, University of Queensland, Brisbane, Queensland, Australia. ¹⁰Wisconsin Department of Natural Resources, Madison, WI, USA. ¹¹College of Agricultural Sciences, Oregon State University, Corvallis, OR, USA. ¹²Coastal Jaguar Conservation, Heredia, Costa Rica. ¹³Panthera, New York, NY, USA.</footnote>

<footnote n="14">¹⁴Department of Biology and Environment, University of Haifa at Oranim, Kiryat Tivon, Israel. ¹⁵Watershed Studies Institute, Murray State University, Murray, KY, USA. ¹⁶St. Lawrence University, Canton, NY, USA. ¹⁷School of Forest, Fisheries and Geomatics Sciences, University of Florida, Gainesville, FL, USA.</footnote>

<footnote n="18">¹⁸Institute of Environmental Sciences, Faculty of Biology, Jagiellonian University, Kraków, Poland. ¹⁹Centre for Environmental Sciences, Hasselt University, Hasselt, Belgium. ²⁰School of Zoology, Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel. ²¹Institute of Forestry, Lithuanian Research Centre for Agriculture and Forestry, Kėdainių, Lithuania. ²²National Park Tara, Mokra Gora, Serbia. ²³Hogle Zoo, Salt Lake City, UT, USA. ²⁴Siena College, Loudonville, NY, USA. ²⁵Faculty of Biology, University of Belgrade, Belgrade, Serbia. ²⁶Smithsonian's National Zoo and Conservation Biology Institute, Washington, DC, USA. ²⁷Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada. ²⁸Lincoln Memorial University, Harrogate, TN, USA. ²⁹Division of Biological Sciences & Wildlife Biology Program, University of Montana, Missoula, MT, USA. ³⁰Institute of Biodiversity and Environmental Conservation,</footnote>
Universiti Malaysia Sarawak, Kota Samarahan, Malaysia. ³¹Purdue University, West Lafayette, IN, USA. ³²Mammal Research Institute, Polish Academy of Sciences, Biatowieża, Poland. ³³Animal Ecology Unit, Research and Innovation Centre, Fondazione Edmund Mach, Trento, Italy. ³⁴National Biodiversity Future Center (NBFC), Palermo, Italy. ³⁵Warnell School of Forestry and Natural Resources, University of Georgia, Athens, GA, USA. ³⁶Department of Biology and Centre for Environmental and Marine Studies, University of Aveiro, Aveiro, Portugal. ³⁷Research Institute for Nature and Forest, Brussels, Belgium. ³⁸Slovenia Forest Service, Ljubljana, Slovenia. ³⁹Hamaarag, Steinhardt Museum of Natural History, Tel Aviv University, Tel Aviv, Israel. ⁴⁰British Columbia Ministry of Forests, Cranbrook, British Columbia, Canada. ⁴¹Paul Smith's College, Paul Smiths, NY, USA. ⁴²Royal Botanic Gardens Victoria, Melbourne, Victoria, Australia. ⁴³Springfield College, Springfield, MA, USA. ⁴⁴Felidae Conservation Fund, Mill Valley, CA, USA. ⁴⁵North Carolina Museum of Natural Sciences, Raleigh, NC, USA. ⁴⁶Alaska Department of Fish and Game, Juneau, AK, USA. ⁴⁷University of Connecticut, Storrs, CT, USA. ⁴⁸Quest University Canada, Squamish, British Columbia, Canada. ⁴⁹Service Public of Wallonia, Gembloux, Belgium. ⁵⁰Instituto de Investigación en Recursos Cinegéticos, Ciudad Real, Spain. ⁵¹Faculty of Biology, University of Warsaw, Warsaw, Poland. ⁵²Faculty of Forestry and Wood Technology, Mendel University in Brno, Brno, Czech Republic. ⁵³Friends of the Earth Czech Republic, Carnivore Conservation Programme, Olomouc, Czech Republic.

⁵⁴University of North Dakota, Grand Forks, ND, USA. ⁵⁵Texas Parks and Wildlife Department, Austin, TX, USA. ⁵⁶Hunting and Wildlife Program, Kastamonu University, Kastamonu, Turkey. ⁵⁷College of Forestry, Wildlife and Environment, Auburn University, Auburn, AL, USA. ⁵⁸Department of Life Sciences, University of Siena, Siena, Italy. ⁵⁹Bavarian Forest National Park, Grafenau, Germany. ⁶⁰University of Freiburg, Breisgau, Germany. ⁶¹fRI Research, Hinton, Alberta, Canada. ⁶²University of Victoria, Victoria, British Columbia, Canada. ⁶³Bridgewater State University, Bridgewater, MA, USA. ⁶⁴Biotechnical Faculty, University of Ljubljana, Ljubljana, Slovenia. ⁶⁵Krkonoše Mountains National Park, Vrchlabí, Czech Republic. ⁶⁶Department of Biology, University of British Columbia, Kelowna, British Columbia, Canada. ⁶⁷Italian Institute for Environmental Protection and Research, Rome, Italy. ⁶⁸Texas State University, San Marcos, TX, USA. ⁶⁹Department of Botany and Zoology, Faculty of Science, Masaryk University, Brno, Czech Republic. ⁷⁰California Department of Fish and Wildlife, Sacramento, CA, USA. ⁷¹University of Rhode Island, Kingstown, RI, USA. ⁷²Research Institute for the Environment and Livelihoods, Charles Darwin University, Darwin, Northern Territory, Australia. ⁷³Society for the Preservation of Endangered Carnivores and their International Ecological Study (S.P.E.C.I.E.S.), Ventura, CA, USA. ⁷⁴Faculty of Veterinary Medicine, University of Zagreb, Zagreb, Croatia. ⁷⁵New Mexico State University, Las Cruces, NM, USA. ⁷⁶Pepperwood, Santa Rosa, CA, USA. ⁷⁷University of Utah, Salt Lake City, UT, USA. ⁷⁸Agricultural Center for Cattle, Grassland, Dairy, Game and Fisheries of Baden-Württemberg, Aulendorf, Germany. ⁷⁹Leibniz Institute for Zoo and Wildlife Research, Berlin, Germany. ⁸⁰University of Kansas, Lawrence, KS, USA. ⁸¹Ezemvelo KZN Wildlife, Pietermartizburg, South Africa. ⁸²University of Montana, Missoula, MT, USA. ⁸³US Air Force Academy, Colorado Springs, CO, USA. ⁸⁴Inland Norway University, Hamar, Norway. ⁸⁵Department of Wildlife, Fish and Environmental Studies, Swedish University of Agricultural Sciences, Umeå, Sweden. ⁸⁶Northern Michigan University, Marquette, MI, USA. ⁸⁷Clemson University, Clemson, SC, USA. ⁸⁸Smithsonian Tropical Research Institute, Balboa, Republic of Panama. ⁸⁹Department of Environmental Sciences, Wageningen University and Research, Wageningen, the Netherlands.

⁹⁰Woodland Park Zoo, Seattle, WA, USA. ⁹¹Seattle University, Seattle, WA, USA. ⁹²National Institute of the Atlantic Forest, Santa Teresa, Brazil. ⁹³Virginia Tech, Blacksburg, VA, USA. ⁹⁴Institute of Ecology, Technische Universität Berlin, Berlin, Germany. ⁹⁵BUND Niedersachsen, Hanover, Germany. ⁹⁶University of Missouri, Columbia, MO, USA. ⁹⁷Department of Wildlife Ecology and Conservation, University of Florida, Gainesville, FL, USA. ⁹⁸Rutgers University, New Brunswick, NJ, USA. ⁹⁹Abilene Christian University, Abilene, TX, USA. ¹⁰⁰United States Department of Agriculture Forest Service, Pacific Northwest Research Station, Corvallis, OR, USA. ¹⁰¹Scientific Laboratory of Gorce National Park, Niedźwiedź, Poland. ¹⁰²South Dakota State University, Brookings, SD, USA. ¹⁰³Missouri State University, Springfield, MO, USA. ¹⁰⁴University of Michigan, Ann Arbor, MI, USA. ¹⁰⁵City of Issaquah, Issaquah, WA, USA. ¹⁰⁶CEFE, Univ Montpellier, CNRS, EPHE-PSL University, IRD, Montpellier, France. ¹⁰⁷Department of Migration, Max Planck Institute of Animal Behaviour, Konstanz, Germany. ¹⁰⁸Universidad de Santiago de Chile (USACH) and Institute of Ecology and Biodiversity (IEB), Santiago, Chile. ¹⁰⁹Mianus River Gorge, Bedford, MA, USA. ¹¹⁰World Wildlife Fund—USA, Washington, DC, USA. ¹¹¹Effigy Mounds National Monument, Harper's Ferry, WV, USA. ¹¹²University of Wisconsin-Whitewater, Whitewater, WI, USA. ¹¹³Southeastern Louisiana University, Hammond, LA, USA. ¹¹⁴Museo delle Scienze (MUSE), Trento, Italy.

¹¹⁵Carnívoros Australes, Talca, Chile. ¹¹⁶University of Castilla-La Mancha Instituto de Investigación en Recursos Cinegéticos, Ciudad Real, Spain.

¹¹⁷Department of Veterinary Sciences, University of Torino, Turin, Italy. ¹¹⁸Parks Canada—Waterton Lakes National Park, Waterton Park, Alberta, Canada.

¹¹⁹Stelvio National Park, Bormio, Italy. ¹²⁰United States Army, Fort Hood, TX, USA. ¹²¹Sageland Collaborative, Salt Lake City, UT, USA. ¹²²University of Hawai'i at Manoa, Honolulu, HI, USA. ¹²³Noble Research Institute, LLC, Ardmore, OK, USA. ¹²⁴Université de Toulouse, INRAE, CEFS, Castanet-Tolosan, France.

¹²⁵Iowa State University, Ames, IA, USA. ¹²⁶El Colegio de la Frontera Sur, Campeche, Mexico. ¹²⁷Centre for Integrative Ecology, School of Life and Environmental Sciences, Deakin University, Melbourne, Victoria, Australia. ¹²⁸West Virginia University, Morgantown, WV, USA. ¹²⁹Department of Biology, University of Florence, Florence, Italy. ¹³⁰McDowell Sonoran Conservancy, Scottsdale, AZ, USA. ¹³¹Northern Arizona University, Flagstaff, AZ, USA.

¹³²Centre for Biological Diversity, School of Biology, University of St Andrews, St Andrews, UK. ¹³³Northern and Yorke Landscape Board, Clare, South Australia, Australia. ¹³⁴United States Department of Agriculture Forest Service, Southern Research Station, Nacogdoches, TX, USA. ¹³⁵Arizona State University, West, Glendale, AZ, USA. ¹³⁶Stephen F Austin State University, Nacogdoches, TX, USA. ¹³⁷Koç University, Istanbul, Turkey. ¹³⁸Research, Ecology and Environment Dimension (D.R.E.A.M.), Pistoia, Italy. ¹³⁹University of Richmond, Richmond, VA, USA. ¹⁴⁰Planning and Environmental Services, City of Edmonton, Edmonton, Alberta, Canada. ¹⁴¹Instituto de Conservación, Biodiversidad y Territorio & Programa Austral Patagonia, Facultad de Ciencias Forestales y Recursos Naturales, Universidad Austral de Chile, Valdivia, Chile. ¹⁴²iWild, Department of Biological Sciences, University of Cape Town, Cape Town, South Africa. ¹⁴³Conservation Ecology Group, Department of Biosciences, Durham University, Durham, UK. ¹⁴⁴Department of Ecology, Institute of Functional Biology and Ecology, Faculty of Biology, University of Warsaw, Warsaw, Poland. ¹⁴⁵Parks Victoria, Melbourne, Victoria, Australia. ¹⁴⁶United Nations Environment Programme World Conservation Monitoring Centre (UNEP-WCMC), Cambridge, UK. ¹⁴⁷The King's University, Edmonton, Alberta, Canada. ¹⁴⁸Natural Resources Institute and Department of Rangeland, Wildlife and Fisheries Management, Texas A&M University, College Station, TX, USA. ¹⁴⁹Oeko-Log Freilandforschung, Friedrichswalde, Germany. ¹⁵⁰University of Illinois, Urbana, IL, USA. ¹⁵¹Tufts University, Grafton, MA, USA.

¹⁵²Parks Canada, Banff, Alberta, Canada. ¹⁵³Department of Biology, University of Konstanz, Konstanz, Germany. ¹⁵⁴Wildlife Habitat Council, Silver Spring, MD, USA. ¹⁵⁵Environmental Studies Department, University of California Santa Cruz, Santa Cruz, CA, USA. ¹⁵⁶Parks Canada, Alberni-Clayoquot, British Columbia, Canada. ¹⁵⁷Victoria University of Wellington, Wellington, New Zealand. ¹⁵⁸Parks Canada, Ucluelet, British Columbia, Canada. ¹⁵⁹University of Mount Union, Alliance, OH, USA. ¹⁶⁰North Carolina State University, Raleigh, NC, USA. ¹⁶¹These authors contributed equally: A. Cole Burton, Christopher Beirne.

<figure><img src="image_10.png" /></figure>

e-mail: cole.burton@ubc.ca
# Reporting Summary

Nature Portfolio wishes to improve the reproducibility of the work that we publish. This form provides structure for consistency and transparency in reporting. For further information on Nature Portfolio policies, see our [Editorial Policies](#) and the [Editorial Policy Checklist](#).

## Statistics

For all statistical analyses, confirm that the following items are present in the figure legend, table legend, main text, or Methods section.

n/a | Confirmed

☐ ☑ The exact sample size (n) for each experimental group/condition, given as a discrete number and unit of measurement

☐ ☑ A statement on whether measurements were taken from distinct samples or whether the same sample was measured repeatedly

☐ ☑ The statistical test(s) used AND whether they are one- or two-sided
*Only common tests should be described solely by name; describe more complex techniques in the Methods section.*

☐ ☑ A description of all covariates tested

☐ ☑ A description of any assumptions or corrections, such as tests of normality and adjustment for multiple comparisons

☐ ☑ A full description of the statistical parameters including central tendency (e.g. means) or other basic estimates (e.g. regression coefficient) AND variation (e.g. standard deviation) or associated estimates of uncertainty (e.g. confidence intervals)

☐ ☑ For null hypothesis testing, the test statistic (e.g. F, t, r) with confidence intervals, effect sizes, degrees of freedom and *P* value noted
*Give *P* values as exact values whenever suitable.*

☑ ☐ For Bayesian analysis, information on the choice of priors and Markov chain Monte Carlo settings

☐ ☑ For hierarchical and complex designs, identification of the appropriate level for tests and full reporting of outcomes

☐ ☑ Estimates of effect sizes (e.g. Cohen's *d*, Pearson's *r*), indicating how they were calculated

*Our web collection on statistics for biologists contains articles on many of the points above.*

## Software and code

Policy information about [availability of computer code](#)

Data collection | No software was used to collect data

Data analysis | R Statistical Software, version 4.1.3. Code for data analysis are available on FigShare (link provided in Code Availability statement in paper).

For manuscripts utilizing custom algorithms or software that are central to the research but not yet described in published literature, software must be made available to editors and reviewers. We strongly encourage code deposition in a community repository (e.g. GitHub). See the Nature Portfolio [guidelines for submitting code & software](#) for further information.

## Data

Policy information about [availability of data](#)

All manuscripts must include a [data availability statement](#). This statement should provide the following information, where applicable:

- Accession codes, unique identifiers, or web links for publicly available datasets

- A description of any restrictions on data availability

- For clinical datasets or third party data, please ensure that the statement adheres to our [policy](#).

All data and code have been made available on FigShare with the links provided in the Data Availability and Code Availability statements in the paper.
# Human research participants

Policy information about studies involving human research participants and Sex and Gender in Research.

Reporting on sex and gender NA (our study did not involve human research participants)

Population characteristics NA

Recruitment NA

Ethics oversight NA

Note that full information on the approval of the study protocol must also be provided in the manuscript.

# Field-specific reporting

Please select the one below that is the best fit for your research. If you are not sure, read the appropriate sections before making your selection.

<figure><img src="image_11.png" /></figure>

Life sciences

<figure><img src="image_12.png" /></figure>

Behavioural & social sciences

<figure><img src="image_13.png" /></figure>

Ecological, evolutionary & environmental sciences

For a reference copy of the document with all sections, see [nature.com/documents/nr-reporting-summary-flat.pdf](https://nature.com/documents/nr-reporting-summary-flat.pdf)

# Ecological, evolutionary & environmental sciences study design

All studies must disclose on these points even when the disclosure is negative.

**Study description**
Comparison of amount and timing of animal activity between paired treatment (higher human activity) and control (lower human activity) time periods using detections from 5400 camera traps for 163 species across 102 survey areas. Unit of comparison was the population (species-project), with 1065 for amount of activity and 499 for timing of activity.

**Research sample**
Sample of terrestrial mammals detected at motion-triggered camera traps across 102 survey areas around the world. Surveys were identified opportunistically as those with active camera trap sampling before and during the COVID-19 lockdowns in 2020. Within surveys, cameras were deployed randomly or systematically to detect medium- and large-bodied terrestrial mammals (≥ 1 kg), including humans.

**Sampling strategy**
We included surveys for which: most or all camera trap stations were deployed in the same area of interest before and during COVID-19-related restrictions; a minimum of 7 unique camera trap deployment locations were sampled; a minimum sampling effort of at least 7 days per camera period; and trends in human detections were recorded from camera trap data or human activity was available from other sources.

**Data collection**
Mammals photographed by camera traps were identified from images by researchers from each project. The date and time of each detection was recorded, as was the location of each camera trap.

**Timing and spatial scale**
The spatial scale includes the entire world as we considered camera trap surveys from anywhere that met our criteria (listed above under sampling strategy). The timing of sampling varied by project, and across all projects spanned from 2017-2020, with most sampling between 2019-2020.

**Data exclusions**
We received data submissions from 146 projects, of which 112 met our sampling criteria (described above). We analyzed data from 102 projects, excluding 10 projects that did not show any change in human activity (i.e., no treatment effect).

**Reproducibility**
This was not a controlled experiment but rather a quasi-experiment based on changes in human activity in response to COVID-19 policies. Our samples were therefore not reproducible.

**Randomization**
Our comparisons of human and animal activity were between time periods within survey areas, thus controlling for variation between survey areas.

**Blinding**
Animals are detected by passive infrared cameras triggered by animal motion and body heat. There is no researcher bias in detecting animals.

Did the study involve field work?

<figure><img src="image_14.png" /></figure>

Yes

<figure><img src="image_15.png" /></figure>

No
## Field work, collection and transport

Field conditions

Our study covered 102 survey areas around the world, and compared animal detections between time periods. Field conditions thus varied substantially

Location

Our study covered 102 survey areas around the world.

Access & import/export

Camera trap sampling is non-invasive and does not involve any capture, handling or collection of animal specimens.

Disturbance

Camera trap sampling is non-invasive and causes minimal disturbance to animals.

## Reporting for specific materials, systems and methods

We require information from authors about some types of materials, experimental systems and methods used in many studies. Here, indicate whether each material, system or method listed is relevant to your study. If you are not sure if a list item applies to your research, read the appropriate section before selecting a response.

### Materials & experimental systems

<table><thead><tr><th>n/a</th><th>Involved in the study</th></tr></thead><tbody><tr><td>☑</td><td>Antibodies</td></tr><tr><td>☑</td><td>Eukaryotic cell lines</td></tr><tr><td>☑</td><td>Palaeontology and archaeology</td></tr><tr><td>☐</td><td>Animals and other organisms</td></tr><tr><td>☑</td><td>Clinical data</td></tr><tr><td>☑</td><td>Dual use research of concern</td></tr></tbody></table>

### Methods

<table><thead><tr><th>n/a</th><th>Involved in the study</th></tr></thead><tbody><tr><td>☑</td><td>ChIP-seq</td></tr><tr><td>☑</td><td>Flow cytometry</td></tr><tr><td>☑</td><td>MRI-based neuroimaging</td></tr></tbody></table>

### Animals and other research organisms

Policy information about studies involving animals; ARRIVE guidelines recommended for reporting animal research, and Sex and Gender in Research

Laboratory animals

The study did not involve laboratory animals.

Wild animals

No animals were captured or handled. Animals were observed through photographic records obtained by passive infrared remotely triggered cameras

Reporting on sex

Both sexes for all species were recorded by photographs. There is no reason to expect any sex bias in sampling.

Field-collected samples

Study did not involve samples collected from the field (only photographs)

Ethics oversight

No ethical approval is required for non-invasive photographic sampling by passive, remote infrared cameras

Note that full information on the approval of the study protocol must also be provided in the manuscript.