Received: 24 October 2018

| Revised: 17 May 2019 | Accepted: 20 May 2019

DOI: 10.1111/gcb.14708

PRIMARY RESEARCH ARTICLE

Scavenging in the Anthropocene: Human impact drives
vertebrate scavenger species richness at a global scale
Esther Sebastián‐González1| Jomar Magalhães Barbosa1,2 | Juan M. Pérez‐García1,3 |Zebensui Morales‐Reyes1 | Francisco Botella1 | Pedro P. Olea4,5 |Patricia Mateo‐Tomás6,7| Marcos Moleón8 | Fernando Hiraldo2 | Eneko Arrondo2 |José A. Donázar2 | Ainara Cortés‐Avizanda2,9 | Nuria Selva1011

Sergio A. Lambertucci

12,13| Aishwarya Bhattacharjee|| Alexis Brewer12,13 |José D. Anadón12,13 | Erin Abernethy14 | Olin E. Rhodes Jr15 | Kelsey Turner15 |James C. Beasley15 | Travis L. DeVault16 | Andrés Ordiz17 | Camilla Wikenros18
19

Barbara Zimmermann
21

19| Petter Wabakken
22| Corinne J. Kendall

Justine A. Smith

20| Christopher C. Wilmers
23,24| Darcy Ogada||| Evan R. Buechley25,26,27 |Ethan Frehner25 | Maximilian L. Allen28 | Heiko U. Wittmer29| James R. A. Butler30 |Johan T. du Toit31 | John Read32 | David Wilson33 | Klemen Jerina34 |Miha Krofel34| Rich Kostecke35 | Richard Inger36 | Arockianathan Samson37
1

Lara Naves‐Alegre|

1| José A. Sánchez‐Zapata

1

Departamento de Biología Aplicada, Universidad Miguel Hernández, Elche, Spain

2

Department of Conservation Biology, Doñana Biological Station‐CSIC, Seville, Spain

3

Department of Animal Science, Faculty of Life Sciences and Engineering, University of Lleida, Lleida, Spain

4

Centro de Investigación en Biodiversidad y Cambio Global (CIBC‐UAM), Universidad Autónoma de Madrid, Madrid, Spain

5

Departamento de Ecología, Universidad Autónoma de Madrid, Madrid, Spain

6

Centre for Functional Ecology, Department of Life Sciences, University of Coimbra, Coimbra, Portugal

7

Biodiversity Research Unit (UMIB), UO‐CSIC‐PA, Oviedo University, Mieres, Spain

8

Department of Zoology, University of Granada, Granada, Spain

9

Animal Ecology and Demography Group, IMEDEA (CSIC‐UIB), Esporles, Spain

10

Institute of Nature Conservation, Polish Academy of Sciences, Krakow, Poland

11

Grupo de Investigaciones en Bilogía de la Conservación, Laboratorio Ecotono, INIBIOMA (CONICET, Universidad Nacional del Comahue), Bariloche,
Argentina

12

Department of Biology, Queens College, City University of New York, Queens, New York

13

Biology Program, The Graduate Center, City University of New York, New York, New York

14

Integrative Biology Department, Oregon State University, Corvallis, Oregon

15

Savannah River Ecology Lab, Warnell School of Forestry and Natural Resources, University of Georgia, Aiken, South Carolina

16

National Wildlife Research Center, United States Department of Agriculture, Sandusky, Ohio

17

Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, Ås, Norway

18
19

Grimsö Wildlife Research Station, Department of Ecology, Swedish University of Agricultural Sciences, Riddarhyttan, Sweden

Faculty of Applied Ecology, Agricultural Sciences and Biotechnology, Inland Norway University of Applied Sciences, Elverum, Norway

20

Center for Integrated Spatial Research, Environmental Studies Department, University of California, Santa Cruz, California

21

Department of Environmental Science, Policy, and Management, University of California, Berkeley, California

22

North Carolina Zoo, Asheboro, North Carolina

Glob Change Biol. 2019;25:3005–3017.

wileyonlinelibrary.com/journal/gcb © 2019 John Wiley & Sons Ltd

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Ornithology Section, National Museums of Kenya, Nairobi, Kenya

The Peregrine Fund, Boise, Idaho

25

Department of Biology, University of Utah, Salt Lake City, Utah

26

HawkWatch International, Salt Lake City, Utah

27

Smithsonian Migratory Bird Center, Washington, District of Columbia

28

Illinois Natural History Survey, University of Illinois, Champaign, Illinois

29

School of Biological Sciences, Victoria University of Wellington, Wellington, New Zealand30

CSIRO Land and Water, Brisbane, Qld, Australia

31

Department of Wildland Resources, Utah State University, Logan, Utah

32

Department of Earth and Environmental Sciences, University of Adelaide, Adelaide, SA, Australia

33

The Biodiversity Consultancy, Cambridge, UK

34

Biotechnical Faculty, University of Ljubljana, Ljubljana, Slovenia

35

The Nature Conservancy, Austin, Texas

36

Environment and Sustainability Institute, University of Exeter, Penryn, UK

37

Department of Zoology and Wildlife Biology, Government Arts College, The Nilgiris, India

Correspondence
Esther Sebastián González, Departamento
de Biología Aplicada, Universidad MiguelHernández. Avda, Universidad s/n 03202
Elche, Spain.
Email: esebgo@gmail.com
Funding information
Gordon and Betty Moore Foundation;
Consejería de Economía, Innovación,
Ciencia y Empleo, Junta de Andalucía,
Grant/Award Number: RNM-1925;
Ministerio de Economía y Competitividad,
Grant/Award Number: CGL201240013-C02-01/02, CGL2015-66966-C21-R , CGL2017-89905-R, FJCI-2015-25632,
IJCI-2014-20744 , IJCI-2015-24947  andRYC-2015-19231; National Science
Foundation, Grant/Award Number:
1255913 and 1256065; U.S. Departmentof Agriculture, Grant/Award Number:
DE-EM0004391; Generalitat Valenciana,
Grant/Award Number: SEJI/2018/024;
Slovenian Research Agency, Grant/Award Number: P4-0059; Govern deles Illes Balears, Grant/Award Number:
PD/039/2017; La Caixa‐Severo Ochoa
International PhD Program; The Peregrine
Fund; National Science Centre Poland,
Grant/Award Number: 2013/08/M/
NZ9/00469; California Department of Fish
& Wildlife

Abstract
Understanding the distribution of biodiversity across the Earth is one of the mostchallenging questions in biology. Much research has been directed at explaining thespecies latitudinal pattern showing that communities are richer in tropical areas; how‐ever, despite decades of research, a general consensus has not yet emerged. In addi‐tion, global biodiversity patterns are being rapidly altered by human activities. Here,
we aim to describe large‐scale patterns of species richness and diversity in terrestrial
vertebrate scavenger (carrion‐consuming) assemblages, which provide key ecosys‐
tem functions and services. We used a worldwide dataset comprising 43 sites, wherevertebrate scavenger assemblages were identified using 2,485 carcasses monitored
between 1991 and 2018. First, we evaluated how scavenger richness (number of spe‐cies) and diversity (Shannon diversity index) varied among seasons (cold vs. warm,
wet vs. dry). Then, we studied the potential effects of human impact and a set of mac‐
roecological variables related to climatic conditions on the scavenger assemblages.
Vertebrate scavenger richness ranged from species‐poor to species rich assemblages(4–30 species). Both scavenger richness and diversity also showed some seasonalvariation. However, in general, climatic variables did not drive latitudinal patterns, as
scavenger richness and diversity were not affected by temperature or rainfall. Rainfall
seasonality slightly increased the number of species in the community, but its ef‐
fect was weak. Instead, the human impact index included in our study was the main
predictor of scavenger richness. Scavenger assemblages in highly human‐impacted
areas sustained the smallest number of scavenger species, suggesting human activity
may be overriding other macroecological processes in shaping scavenger communi‐ties. Our results highlight the effect of human impact at a global scale. As species‐rich assemblages tend to be more functional, we warn about possible reductions inecosystem functions and the services provided by scavengers in human‐dominatedlandscapes in the Anthropocene.
KEYWORDS

carrion, climate, human footprint, latitudinal hypothesis, species diversity

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SEBASTIÁN‐GONZÁLEZ et al.

1 | I NTRO D U C TI O N

species, vertebrate scavengers in general and obligate scavengers inparticular, are especially important in terrestrial ecosystems because

Scientists have long tried to disentangle the processes driving the lat‐

they are able to consume large amounts of carrion in short time periods

itudinal biodiversity gradient showing that species diversity is great‐(DeVault et al., 2003; Morales‐Reyes et al., 2017; Sebastián‐González

est in the tropics and decreases toward the poles (e.g., Hawkins etet al., 2016). There is some evidence that vertebrate scavenger diversity

al., 2003; Schluter, 2016). In a review, Willig, Kauffman and Stevens

is lower in biomes with more extreme climatic conditions (e.g., desert or(2003) listed several hypotheses proposed to explain this pattern. For

tundra; Mateo‐Tomás et al., 2015), and a major impact of human distur‐

example, species diversity is expected to be higher in areas with morebance on the ecosystem functions supported by vertebrate scavenger

available environmental energy, in accordance with the Productivity

assemblages has been suggested (Mateo‐Tomás, Olea, Moleón, Selva,

Hypothesis (Pianka, 1966; Willig et al., 2003). This hypothesis posits& Sánchez‐Zapata, 2017). However, a comprehensive study evaluatingthat the amount of energy available to plants and water availability

macroecological patterns in scavenger assemblages is lacking, especiallylimit productivity of an ecosystem, affecting all species within trophic

in the context of the human drivers that make current (Anthropocene)

chains (Wright, 1983). Similarly, the Physiological Tolerance Hypothesis

conditions unique in evolutionary history.

suggests diversity is limited by the number of species able to tolerate

The goals of our study were twofold. First, we aimed to describe

local conditions (Currie et al., 2004). For example, extinction rates inlarge‐scale patterns of species richness (number of species) and diversity

tropical climates are low compared to temperate regions because of(Shannon diversity index) in terrestrial vertebrate scavenger assemblages.

climatic stability (Dynesius & Jansson, 2000). The Evolutionary Speed

We know from previous studies that scavenger communities have a dif‐

Hypothesis posits that speciation rates are higher in warmer (tropical)

ferent structure in warm and cold seasons (Selva & Fortuna, 2007) andenvironments because generation times are shorter, mutation rates

that season plays a critical role in how long carcasses are available to be

are higher, and interspecific competition and selection pressures arescavenged (e.g., Turner, Abernethy, Conner, Rhodes, & Beasley, 2017).

stronger (Allen, Brown, & Gillooly, 2002; Currie et al., 2004).

Also, several scavenger species are migratory, which also calls for the con‐

Because of the strong effect of latitude on climate, most hypoth‐sideration of different seasons. Thus, we also explored the effect of sea‐eses (such as those above) suggest climatic conditions are the mainson (cold vs. warm and wet vs. dry) in shaping the patterns of scavenger

drivers of variation in species richness, and this is supported by several

richness and diversity. We expected to have seasonal differences in theempirical studies. For example, an increase in the number of vertebrate

number and diversity of scavenger species. Second, we aimed to identify

species has been related to annual potential evapotranspiration, a mea‐

the main macroecological factors driving terrestrial vertebrate scavenger

sure of the energy available in the ecosystem (Currie, 1991). Similarly,richness and diversity and tested five climatic, scavenging, and human‐re‐productivity, rainfall and temperature explained broad‐scale vertebrate

lated hypotheses (Table 1). We expected scavenger species richness andrichness patterns (Hawkins et al., 2003). In the Anthropocene, however,

diversity to be higher in areas that are wetter (Productivity Hypothesis),human‐related factors in addition to climate are expected to influence

more stable and thus with lower seasonality (Physiological Tolerance

global species distribution patterns, due to the multitude of effects

Hypothesis), and warmer (Productivity Hypothesis, Physiologicalthat humans impose on the ecosphere. Because of its pervasiveness,

Tolerance Hypothesis, and Evolutionary Speed Hypothesis).

human impact may directly (e.g., by hunting, Benítez‐López et al., 2017)

Alternatively, vertebrate scavengers compete with microorgan‐

and indirectly (e.g., by altering the habitat and amount of food available

isms and invertebrates for carrion. The latter species may benefit fromto species) affect the number of species in a community and their in‐warm and wet climatic conditions, reducing the temporal window ofteractions. For example, human factors drive global avian species loss

­carcass consumption, and outcompeting vertebrates (DeVault, Brisbin,(Jetz, Wilcove & Dobson, 2007), affect macroecological patterns of& Rhodes, 2004; Ray, Seibold, & Heurich, 2014). Under this hypothesis,

seed‐dispersal assemblages (Sebastián‐González, Dalsgaard, Sandel, &termed “Competitive Hypothesis,” richness of the vertebrate assemblage

Guimarães, 2015), and restrict local and regional movements of terres‐would be reduced in the most warm and wet environments. Moreover,trial mammal species (Tucker et al., 2018). Thus, assessing the contri‐

modern human‐mediated factors may override evolved latitudinal or

bution of human impact on species richness and diversity is a pressing

ecological patterns, and affect scavenger richness and diversity in both

ecological challenge in an increasingly humanized world.

positive and negative ways. On the one hand, humans may increase theGiven that global consensus on the species latitudinal processes hasavailability of carrion from hunting, livestock, or roadkills (Lambertucci,

not emerged (e.g., Hillebrand, 2004), large‐scale investigations of under‐

Speziale, Rogers, & Morales, 2009; Oro, Genovart, Tavecchia, Fowler,

studied communities are of particular interest. Scavenger assemblages,& Martínez‐Abraín, 2013), as well as the predictability of carcasses

or species that include carrion in their diets (DeVault, Rhodes, & Shivik,

available through wild harvesting (Read & Wilson, 2004), or artificial

2003; Wilson & Wolkovich, 2011), have received comparatively little

feeding stations (Cortés‐Avizanda et al., 2016), ultimately benefitting

attention from the scientific community. Scavengers play pivotal roles

scavengers. On the other hand, habitat modification and loss, or directin ecosystems by stabilizing food webs (Moleón et al., 2014; Wilson &persecution may reduce the population viability of many scavenger spe‐

Wolkovich, 2011), providing regulating services for organic food wastecies, reducing community diversity and richness (Mateo‐Tomás, Olea,(O'Bryan et al., 2018), accelerating nutrient recycling (Wilson & Read,

Selva, & Sánchez‐Zapata, 2018). Here, we used the largest compilation

2003) and removing potential sources of infectious disease transmis‐of vertebrate scavenging studies to date to identify the major drivers ofsion (Ogada, Torchin, Kinnaird, & Ezenwa, 2012). Among all scavenger

scavenger richness and diversity at a global scale.

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TA B L E 1 Climatic, scavenging‐ andhuman‐related hypotheses explaining
large‐scale patterns in vertebratescavenger communities

Main associatedvariables

Main prediction

Productivity
Hypothesis

Temperature
RainfallHigher species richness and diversity in areas
with higher available environmental energy

PhysiologicalTolerance
Hypothesis

Temperature­seasonality
Rainfallseasonality

Larger species richness and diversity undermore stable climatic conditions

Evolutionary
Speed Hypothesis

Temperature

Larger species richness and diversity in warm
environments where speciation rates arehighTemperature
RainfallLower vertebrate scavenger richness anddiversity under warm and wet climatic condi‐
tions, as microorganisms and invertebrates
may overcompete vertebrate scavengers

Human footprint(HF)

Reduced species richness and diversity inareas with higher HF

Hypothesis
Climatic

Scavenging‐relatedCompetitive
Hypothesis

Human‐related
Human impact
overrides otherpatterns

Note: We indicate their associated predictions and the variables included in this study to evaluate
hypothesis importance.

2 | M E TH O DS

coordinates of the center of the study site. Carcass size ranged from

2.1 | Carcass monitoring and scavenger diversityof the 43 datasets for which quantitative information was available, werodents (e.g., 20 g, mice) to large ungulates (e.g., 900 kg, bison). For 37

We assembled a global dataset consisting of 43 study sites from 17
countries across 5 continents (average number of studies by conti‐
nent ± SD: 8.6 ± 6.8; range: 2–18) describing the vertebrate scaven‐ger assemblages, comprised of species observed consuming carrion.
Data originated from studies performed between 1991 and 2018
when carcasses were located in the field (Figure 1; Appendix S1).
Carcass monitoring in all study sites met some minimum require‐ments to be included in the analyses. All carcasses were either
fresh or had been frozen while fresh prior to placement in the field.
Only studies using herbivore carcasses (e.g., terrestrial ungulates,
rodents, and lagomorphs) were included (see Moleón et al., 2017).
Carcasses were monitored either by camouflaged automatic camera
traps, from observatories that were far enough to minimize scaven‐ger avoidance, or from indirect signs of scavenger presence at car‐cass sites. Carcasses were continuously monitored until scavenging
ended (only bones and/or skin remained) or the carcass disappeared
because a scavenger took it. We only included information on spe‐
cies that were detected consuming carrion. When consumption was
suspected, but not clearly recorded, we assumed consumption ifthat species had already been detected consuming other carcasses
in each particular study site. See details on the specific monitoringprocedures for each site in the references listed in Appendix S1.
For each study site, we collected information on vertebrate scav‐

computed the scavenger relative abundance as the maximum numberof unequivocally different individuals of each species detected at asingle carcass. This was calculated by identifying the highest numberof individuals of a scavenger species simultaneously observed or ap‐
pearing in a picture (e.g., Mateo‐Tomás et al., 2017; Moleón, Sánchez‐
Zapata, Sebastián‐González, & Owen‐Smith, 2015). For some species(e.g., lions Panthera leo and Andean condors Vultur gryphus), different
individuals visiting the same carcass were counted using identifying
features like skin patterns, injuries, and sexual dimorphism. As a mea‐
sure of scavenger diversity, we calculated the Shannon diversity index
for each study site based on scavenger relative abundance using thevegan package (Oksanen et al., 2017).
Because one of our goals was also to study seasonal differencesin the scavenger community, we assigned each carcass to one sea‐son depending on the main climatic changes in the region. We calcu‐
lated scavenger richness and diversity separately for each season for
those study sites monitored across seasons. For most study sites weconsidered two seasons: cold (fall and winter, N = 23) versus warm
(spring and summer, N = 31). However, for those areas where themain climatic seasonal changes are driven by rainfall, we divided
them into wet (N = 8) versus dry (N = 8).

2.2 | Macroecological drivers

enger richness (measured as the total number of scavenger species

We explored the proposed hypotheses on how climatic, scavenging‐

documented at monitored carcasses at each study site), and therelated, and human‐related factors were associated with scavenger

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SEBASTIÁN‐GONZÁLEZ et al.

F I G U R E 1 Map showing the location of the 43 study sites. Each point represents one study site. The size of the point is related toscavenger richness in the assemblage. In the lower left corner, we show a detail of the study sites conducted in the Iberian Peninsula
because of the high number of studies overlapping in this region. The map in the background represents the values of the human footprint
variable measured in 2009 [Colour figure can be viewed at wileyonlinelibrary.com]

richness and diversity (Table 1). For each site, we extracted five vari‐rainfall (mm/year) between 2001 and 2015. We calculated the mean

ables linked to one or several of the hypotheses that explained theannual rainfall by adding up all the rainfall in a pixel throughout thelatitudinal diversity gradient: (a) mean annual temperature (ºC); (b)year and then averaging this total annual rainfall across years. HF is anannual temperature seasonality, calculated as the standard deviationindex available in a global dataset of 1 km grid cells, created from global(SD) of mean daytime temperature during the year; (c) mean of thedata layers indicating human population pressure (population density),

total annual rainfall (mm); (d) annual rainfall seasonality, calculated as

human land use and infrastructure (built‐up areas, night‐time lights, land

the coefficient of variation (SD/mean) of the monthly rainfall during

use/land cover), and human access (coastlines, roads, railroads, naviga‐

the year; (e) human footprint (HF, Venter et al., 2016).

ble rivers). HF was downloaded from https​://datad​ryad.org/resou​rce/

Mean and SD of temperature (ºC) between 2001 and 2015, with

doi:10.5061/dryad.052q5​. This database provides information with thespatial resolution of ~5 km, were obtained from Oxford Daytime Land

HF at 2 years: 1993 and 2009, so we assigned to each study site, theSurface Temperature (Weiss et al., 2014). The dataset for this tem‐

HF value closest to the date when the study was performed.

perature product is Moderate Resolution Imaging Spectroradiometer

We calculated the average of all these variables within a spatial(MODIS) land surface temperature data (MOD11A2), which was gap

extent of 20 km buffer radio around the center of the coordinates

filled to eliminate missing data caused by factors such as cloud coverat each study site. This buffer area was selected to represent: (a)(see Weiss et al., 2014). Mean annual and SD of rainfall (mm/year) be‐

local conditions in the area where most of the experimental car‐tween 2001 and 2015 were obtained from the Climate Hazards Groupcasses were located, (b) regional conditions aiming to account for

InfraRed Precipitation with Station data (CHIRPS; Funk et al., 2015),

the landscape heterogeneity in the surroundings of the study site,which is a quasi‐global gridded rainfall time series with 0.05° spatialand (c) to reflect the habitat characteristics of the study sites atresolution. Where CHIRPS data were not available (latitudes higher

the biogeographical scale without dilution from nearby areas withthan 50°), we used Global Land Data Assimilation System (Rodell et al.,different land uses. Because the climatic variables are derived

2004), with 0.25° spatial resolution, to calculate mean annual and SD

from a model and have a large spatial resolution (5 km), we did not

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SEBASTIÁN‐GONZÁLEZ et al.

expect them to change significantly with different spatial extents.

macroecological variable and our dependent variables using an Akaike

However, we also calculated all the variables using a 10 and 30 km

Information Criteria (AIC)‐based model selection. We used this infor‐

buffer to understand the spatial consistency of our results, andmation to decide whether each macroecological predictor variable

found that the results were similar at all buffer scales (Appendix S2,

should be included as linear or quadratic in the models for scavenger

Tables S2.5–2.6).

richness and scavenger diversity.

We also calculated other covariates that could influence scaven‐

Finally, we fitted all the possible combinations and subsets of the pre‐ger richness and diversity, including: (a) carcass size; (b) sample size,

dictor variables for scavenger richness and scavenger diversity separately.

or number of carcasses monitored; and (c) spatial autocovariance,

We selected the model with the lowest AICc, but when there was morewhich is a term accounting for the spatial autocorrelation in the data.

than one model with a delta AICc < 2 with respect to the first ranked

Scavenger richness and diversity may depend on available carcass

model, we implemented a model‐averaging function in the MuMIn pack‐size, with large carcasses providing higher carrion biomass, a greaterage (Barton, 2013). This function averages parameter estimates acrossdiversity of distinct food types (e.g., meat, viscera, bone), and feed‐

all considered models for each dependent variable where the respective

ing opportunities (e.g., tearing, picking, bone‐crushing, stealing), as

parameter appeared, weighted by the relative importance of each model.

well as carrion availability for longer periods of time (Moleón et al.,

We used a Poisson distribution for modeling scavenger richness and a2015; Turner et al., 2017). Carcass size was categorized as: small

Gaussian distribution for scavenger diversity in all GLM analyses. All anal‐(<2 kg), medium (2–10 kg), and large (>10 kg) adapted from Moleón

yses were repeated using a database that included only those studies thatet al. (2015). Sample size was included because higher numbers ofused camera traps instead of signs or direct observations in the scaveng‐carcasses are expected to contain larger numbers of scavengers,

ing monitoring (N = 38), to account for the imperfect detection of theuntil the community is completely sampled. To account for the spa‐

latter methods. Finally, we calculated the percent of explained deviancetial autocorrelation in the structure of the scavenger communities,(i.e., the amount of variability explained by the model) of each model.

we added a spatial autocovariate (AC) term. AC was computed fromTo evaluate if carcass size, sample size and spatial autocovari‐

the weighted average distance of all neighboring samples, indicat‐

ance affected scavenger richness and diversity, we fitted one‐predic‐

ing the degree of spatial clustering among dependent variables. We

tor GLMs relating them (see results of this analysis in Appendix S2,

used the autocov_dist function from the spdep library (Bivand, 2015).

Table S2.1). We included the variables that were significantly related

Finally, because of the different sample sizes among study sites, we(p < 0.05) to scavenger richness and diversity as covariates in the mul‐calculated the sample coverage as the number of scavenging species

tivariate models relating them with latitude and the macroecologicalrecorded at each site by using both the presence/absence and individ‐

variables. Therefore, the most complicated model tested included theual‐based abundance data (Chao et al., 2014). With the sample cover‐significant covariates (carcass size and sample size for scavenger rich‐age, we estimated the number of species in each site under a complete

ness, and carcass size for scavenger diversity, see Results) and the fivesurvey (i.e., when all the species present in an area are surveyed), andmacroecological variables. All analyses were performed in R version

we then compared the observed with the estimated species richness to

3.4.1 (R Development Core Team, 2017).

evaluate if we had monitored most of the species present.

2.3 | Statistical analyses
We first used generalized linear models (GLMs) to test if there wasa latitudinal pattern in scavenger richness and diversity, by relatingthem with the latitude of each study site.
We then evaluated the relationships between scavenger rich‐

3 | R E S U LT S
3.1 | Global‐scale diversity patterns in scavenger
communities
Our global dataset included information from 2,485 carcasses in 43
study sites (Figure 1; Appendix S1). We found high variability in scav‐

ness and diversity and macroecological variables critical to our hy‐

enger richness and diversity depending on the study site (Table 2).

potheses using GLMs. To do so, we first calculated variance inflation

For example, scavenger richness ranged from 4 (in developed areas

factors (VIF) for the macroecological predictor variables using thein UK and Australia) to 30 vertebrate species (in a Polish temperate

car package (Fox & Weisberg, 2011) to assess collinearity. Variables

old‐growth forest) from a total of 174 different species (7 reptiles,

exhibiting VIF values exceeding 3 (temperature for scavenger rich‐

79 mammals, and 88 birds; Appendix S3).

ness and rainfall for scavenger diversity, Zuur, Ieno, & Elphick, 2010)

The scavenger assemblage also changed depending on the season

were eliminated from the model. In each model, predictor variables

when the study was performed. We found higher scavenger richness

were standardized to the same scale (meaning that they were trans‐

and slightly higher scavenger diversity in cold compared to warm sea‐

formed to have a mean of 0 and standard deviation of 1) and sample

sons, but these variables did not differ between dry and wet seasonssize (the number of experimental carcasses used in the study) was(Figure 2). Finally, when all the assemblages were standardized to an

log‐transformed prior to analyses to improve normality.

equal sample coverage (0.95), the estimated species richness was

As the relationships between scavenger richness or diversity

highly correlated with the observed richness for both the abundance

and our predictor variables could be nonlinear, we first compared(Spearman's correlation rs = 0.68, p < 0.001) and the incidence datalinear and quadratic one‐predictor models for latitude and for each(rs = 0.84, p < 0.001, Appendix S4).

| 3011SEBASTIÁN‐GONZÁLEZ et al.

TA B L E 2 Scavenger richness and diversity (Shannon diversity
index) of vertebrate scavenger communities
Mean ± SD

Range

Latitudecoefficient

Latitudep-value

Scavenger
richness

12.4 ± 6.07

4–30

−0.001

0.536

Scavenger
diversity

1.40 ± 0.49

0.43–2.29

0.002

0.317

Note: Mean, standard deviation (SD), and range (minimum–maximum)
values are provided. We also show the coefficients and p‐values forthe generalized linear models relating these variables with latitude. The
model for scavenger richness included both carcass size (small, medium,or large; see main text for further explanations) and sample size ­(log‐transformed number of carcasses monitored) as covariates, while
the model for scavenger diversity included only carcass size (seecomplete results for these models in Appendix S2, Table S2.2).

3.2 | Macroecological trends in scavenger diversity
Contrary to our expectations, scavenger richness and diversity did notshow any latitudinal trends and were not affected by most of the cli‐
matic variables (Tables 2 and 3; Supporting Information Appendix S2,Table S2.3–2.4 and Figure S2.1). However, the best models for scav‐
enger richness included the variable describing human impact. HF
was the main factor influencing scavenger richness in an assemblage,
showing a quadratic relationship. When HF was lower than 7, wefound more species‐poor than species‐rich assemblages, which were
found more frequently with HF values between 7 and 15; contrast‐
ingly, scavenging assemblages at very high HF values (>15) exhibited
low scavenger richness (Table 3, Figure 3a; Appendix S2, Tables S2.3–
2.4). There also seems to be a reduction in the variability of scavenger
richness values around the mean, with an increase in HF (Appendix
S2, Figure S2.2). Rainfall seasonality was also included in the aver‐aged model for species diversity, but, as for species richness, its effectwas not significant (Figure 3b). The results were similar at the other(a)p = 0.018

p = 0.401

two spatial extents (10 and 30 km, Appendix S2, Tables S2.5–2.6),
supporting our alternative hypothesis that human impact overrides
other patterns.
The only variable that affected both species richness and diver‐sity was carcass size (Table 3, Figure 4a,b). Large carcasses wereconsumed by more scavenger species, but scavenger diversity was
greater at medium‐sized carcasses. Finally, scavenger richness also
increased with sample size (number of experimental carcasses usedin each study, Table 3, Figure 4c). The results did not change when
only studies using camera traps were used for the analyses (N = 38,
Appendix S5).

(b)

TA B L E 3 GLMs relating scavenger richness and diversity(Shannon diversity index) with the macroecological variables
calculated at 20 km buffer extent

p = 0.021

p = 0.509

F I G U R E 2 Boxplot representing (a) scavenger richness and (b)scavenger diversity (Shannon diversity index) by season. We show
the significance of the tests comparing wet versus dry (n = 16) andcold versus warm (n = 54) seasons by means of pairwise t tests,
with significant p‐values (<0.05) in bold [Colour figure can beviewed at wileyonlinelibrary.com]

Scavenger
richness

Scavenger
diversity

Sample size (log)0.362***—

Carcass size: small−0.804***−0.361

Carcass size: medium−0.1820.367*

Carcass size: large

2.585***

1.355***

Human footprint−0.081—

Human footprint 2−0.144**—

Rainfall seasonality

0.054

0.090

n

43

37

% Explained deviance

57.53

19.86

Note: We present the model averaged coefficients of each variable formodels with delta AICc < 2, with respect to the model with the lowest
AICc.
Significant p‐values are highlighted in bold. We also show the­percentage of explained deviance of the model (i.e., the proportionof the variability explained by the model) and the number of studies
included in the analyses (n). Human Footprint 2 is the quadratic term of
the human footprint variable.
***p < 0.001, **p < 0.01, *p < 0.05

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SEBASTIÁN‐GONZÁLEZ et al.

Scavenger richness(a) 30

20

10

10

20
Human footprint(b)

Scavenger richness

2.0

1.5

1.0

0.5
0.4

0.6

0.8
1.0
Rainfall seasonality

1.2

F I G U R E 3 Relationships between scavenger richness and thetwo macroecological variables included in the averaged model: (a)human footprint and (b) rainfall seasonality. The plots show the
relationships predicted by the averaged model (black line) and the
95% confidence interval for scavenger richness (gray shade)

4 | D I S CU S S I O N
Our results provide evidence that human impact is a dominantfactor shaping animal communities worldwide (Jetz et al.., 2007;
Sebastián‐González et al., 2015; Tucker et al., 2018). The human‐
related factor was the only macroecological variable included in
the best models for scavenger richness. Our data (Figure 3) clearlyshow that regions with low human impact contained both spe‐cies‐rich and species‐poor vertebrate scavenger assemblages,

F I G U R E 4 Relationships between scavenger richness anddiversity, and the covariates, as inferred from generalized linear
models averaging: (a, b) carcass size, (c) sample size. For sample size,
we also show the regression line for the relationships. Note that thegraph represents the pure relationships between the two variables[Colour figure can be viewed at wileyonlinelibrary.com]

while highly developed areas always had low vertebrate scavenger
species. HF combines information on human population density,
harvest, livestock, land use, land change, and human accessibil‐

4.1 | Effect of human impact

ity. All of these factors are known to affect vertebrates (e.g.,

The loss of animal species, or defaunation, in humanized regions occurs

Tucker et al., 2018), to predict extinction risk (Di Marco, Venter,at the global scale (Dirzo et al., 2014), and it also seems to be an im‐

Possingham, & Watson, 2018), and to negatively affect particu‐

portant process shaping the observed macroecological patterns in ter‐lar scavenger species, at least at the local scale (e.g., Bogoni etrestrial vertebrate scavenger species richness. Indeed, two of the mainal., 2016; Lambertucci et al., 2009). However, this is the first timescavenger functional groups, obligate scavengers and top predators, arethat human impacts have been shown to be more important thanamong the most threatened species worldwide (IUCN, 2018). More im‐

the climatic attributes in driving scavenger species richness at theportantly, human impact (e.g., human population density, intensification

global scale.

in land use, and land accessibility) is expected to increase, threatening

| 3013SEBASTIÁN‐GONZÁLEZ et al.

the maintenance of the ecosystem functions and services provided by

impact may be overriding the effect of environmental variables (e.g.,scavengers (DeVault et al., 2016). In areas where the most endangered

Nogués‐Bravo, Araújo, Romdal, & Rahbek, 2008). Additionally, highspecies in the scavenger community (i.e., obligate scavengers and top

temperatures are linked to increased productivity or evolutionary

predators) have become rare or absent, carrion may remain in the field

speed (Allen et al., 2002; Currie et al., 2004; Pianka, 1966; Willig

for longer periods of time, leading to an increase in generalist scaven‐et al., 2003), which may lead to higher scavenger richness. However, at

gers that are less efficient at removing carcasses (Mateo‐Tomás et al.,the same time, high temperatures may be enhancing the competitive

2017; Morales‐Reyes et al., 2017). This may have several consequences,interactions of vertebrates with microorganisms and invertebrates,

such as increasing the risk of pests and infectious disease transmission

because the latter are benefitted from warm temperatures (DeVault(Buechley & Şekercioğlu, 2016; Ogada et al., 2012) or slowing down

et al., 2004; Ray et al., 2014). These two forces may be affecting

the cycling of nutrients (Moore et al., 2004). For example, the crash of­scavenger species richness concurrently with opposite effects, thus

vulture populations in the Indian subcontinent due to a veterinary drug

counteracting each other and preventing the appearance of an effectseemed to increase the population of feral dogs and consequently theof temperature on scavenger species richness.

incidence of rabies (Markandya et al., 2008). Such changes in vertebratescavenger guilds may also have consequences for ecosystem function‐
ing as species‐rich communities are typically more efficient (Sebastián‐

4.3 | Effect of covariates

González et al., 2016), promoting the stability of ecosystem processes

As expected, the size of the carcasses in each site affected the mac‐and the services provided to humans (Moleón et al., 2014).

roecological patterns described in this study. Carcass size has alreadyMoreover, we found a quadratic relationship between scavenger

been described as a major driver of scavenger assemblage richness

richness and HF, so that the highest values of species richness wereand structure (Moleón et al., 2015; Turner et al., 2017). Larger car‐found in areas with a certain level of human impact. The quadratic

casses are detected faster and consumed at higher rates, but therelationship suggests a decrease in scavenger richness in areas withlonger availability of the carrion resource permits its consumption

low to medium HF (<7). In some cases, moderate human impact can

by a larger number of scavenger species compared to smaller car‐increase availability of carcasses through roadkill, livestock, or off‐casses, which are usually consumed entirely by a single scavenger

cuts from wild harvests (Lambertucci et al., 2009; Read & Wilson,(Moleón et al., 2015). Moreover, large carcasses also allow niche

2004), thus benefiting scavengers. For example, in Guinea‐Bissau,

specialization where different species utilize different carcass parts

hooded vultures Necrosyrtes monachus were more frequently found(e.g., lappet‐faced vultures Torgos tracheliotos feeding on skin andin densely populated areas where there is a higher garbage availabil‐

Egyptian vultures eating remains on bones). Interestingly, in contrast

ity (Henriques et al., 2018). Also, the highest values of habitat suit‐

to species richness, scavenger diversity reached the highest values

ability for the endangered Egyptian vulture Neophron percnopterus

in medium‐sized carcasses, maybe because of the lower ability ofwere found in areas of intermediate livestock density (Mateo‐Tomáslarge dominant scavengers (i.e., top predators and vultures) to gather& Olea, 2015). However, highly human‐impacted areas become un‐

at such carcasses and exclude other species (Moleón et al., 2015;

available for many scavenger species because of habitat reduction

Pardo‐Barquín, Mateo‐Tomás, & Olea, 2019). Besides, top preda‐

and fragmentation, pollution, or direct persecution (e.g., Huijbers,

tors and vultures can frequently monopolize large carcasses, thus

Schlacher, Schoeman, Weston, & Connolly, 2013; Lambertucci et al.,resulting in lower species richness and/or diversity (Pardo‐Barquín2018). Combined, our results suggest that scavenger communities

et al., 2019), while small carcasses are mainly used by mesopredators

may benefit from some degree of human impact, but are sensitive(Moleón et al., 2015).

to high human perturbation (Oro et al., 2013). From a conservationand management perspective, finding that human factors are more
important than the climatic attributes for species richness at the

4.4 | Seasonal changes

global scale is yet another reminder that human decisions may favor

Our study also shows that species richness and diversity of terres‐or reduce the capacity of ecosystems to retain species richness andtrial vertebrate scavenger assemblages vary among seasons. Several

associated functions and services.

factors may be increasing the scavenger richness and diversity inthe cold season. For example, the cold season typically has less food

4.2 | Lack of support for latitudinal hypotheses
In general, the hypotheses previously proposed to explain latitudinal

resources and harsher climatic conditions in temperate ecosystems(e.g., Selva & Fortuna, 2007). This is reflected in greater availabil‐
ity of carrion through natural deaths and a higher number of spe‐

patterns in species richness were not supported by our data, as scav‐cies adding carrion as a food source (Turner et al., 2017). In someenger richness and diversity were not affected by temperature orareas, dominant scavengers known to monopolize carcasses such as

rainfall. Rainfall seasonality slightly increased the number of species

bears (Ursus spp.) hibernate during the cold seasons (Allen, Elbroch,in the community, contrary to the predictions of the Physiological

Wilmers, & Wittmer, 2014), enabling other species to use carcasses

Tolerance Hypothesis (Currie et al., 2004). However, its effect was

as a resource. Also, in highly seasonal areas, facultative scaven‐overall very weak. Scavenging‐related and human‐related hypoth‐gers may be more predatory during the productive season because

eses seemed more appropriate in explaining this pattern as humanprey is more easily available (e.g., vulnerable neonates, migrating

3014

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SEBASTIÁN‐GONZÁLEZ et al.

species), and so they may be less dependent on scavenging (Pereira,relative species abundance for future studies would be to estimate

Owen‐Smith, & Moleón, 2014). Finally, carrion decomposition by

the mean visit time of an individual to a carcass and use this esti‐

invertebrates and microorganisms slows down when temperaturesmate to minimize recounts of the same individuals. Unfortunately,

are low, and carrion is therefore available over a longer time period

the information currently available is still scarce to do it for most of(DeVault et al., 2004). Conversely, we did not find significant differ‐

the species studied. Moreover, data based on observations cannot

ences between the wet and dry season. Carrion availability in warm

be measured using this approach, so we decided not to use it in our

regions tends to peak at the end of the dry season (Pereira et al.,study. We also acknowledge that the population and community2014). However, the differences in resource availability between

metrics of abundance, richness, and diversity can often obscure

wet and dry seasons may be less extreme than in regions where theconsiderable shifts within species assemblages (Read, Parkhurst,

seasons are characterized by cold and warm periods, especially in& Delean, 2015) and hence nonsignificant effects of variables onareas where long‐distance ungulate migrations are absent or artifi‐

population metrics does not necessarily mean these same variables

cially prevented.

will not exert profound influence on species composition and con‐servation status.

4.5 | Caveats and future directions

The spatial resolution of the climate (rainfall and temperature)datasets was low and heterogeneous. Low spatial resolution and

We only considered a species to be a scavenger in a region if it was

imbalanced scale in remote‐sensing data can propagate noise into

documented consuming carrion during one of the experimental

models and mask local‐level effects of environmental conditions,studies used in our analyses (Appendix S1). Our approach thus ig‐

which may negatively impact the percentage of explained deviancenores other species known to be present in the areas that are likely

in model. Nevertheless, the value of these global remote‐sensing

scavengers, such as species that are known from other studies todatasets to macroecology is well documented and their hypertem‐

consume carrion. An alternative approach would be to use lists

poral availability provides valuable insights into temporal oscillationof scavenger species present in different regions rather than onlyof climate conditions.

those species documented to scavenge during our evaluations ofAnother consideration is that our metric of human impact (HF)

carcass consumption. This alternative approach might eliminate theis based on information on human population density, land use, land

potential bias of differences in the experimental approach used inchange, and human access, but not other specific factors that directly

each empirical study, such as the differences in carcass size or type.

affect scavengers, such as animals killed through vehicle collisions or

However, we chose not to pursue this alternative approach for two

the use of poisons. Thus, our analysis does not link the HF with spe‐reasons. First, lists of species present in a region, especially in under‐cies‐specific threats known to cause declines (i.e., Allan et al., 2019).

studied regions, are often incomplete and in some cases not avail‐The HF metric combines several variables and we cannot separate

able. Second, and perhaps more importantly, even if a species has

the relative importance and effect that each have on scavengers

been documented as a member of the scavenging community in one

using only this variable. Thus, we encourage further studies focusing

region it does not necessarily mean that is also scavenges in otheron disentangling how the different aspects of human pressure affectportions of its range due to changing dietary choices or competi‐

scavenger communities.

tive interactions (e.g., Sebastián‐González et al., 2016). Therefore,

Finally, despite considerable effort invested in searching for

we chose to use only data from studies that documented scavenging

data on scavenger assemblages, our dataset is clearly unbalanced

behavior under the conditions of the study site.
Relative scavenger abundance, and thus diversity, was calculated

in space. There is an overrepresentation of study sites in Western
Europe and the United States, whereas other regions such asas the maximum number of different individuals of each species de‐Australia, Asia, and South America contributed a small number oftected at a single carcass. This is the best measure of abundancestudies (see Figure 1). Similarly, we lack study sites on large extents

we could get, but is unavoidably biased for solitary species lackingof the tropical vegetation, deserts and boreal forests, so some partic‐

skin patterns or sexual dimorphism because it is impossible to dif‐ularities of these biomes may not be considered here. For example,

ferentiate among individuals, and so their relative abundances will

there are large areas such as the Sahel, for instance, with light HFs

always appear low. An alternative method of measuring abundance(low road and infrastructure densities) but high human pressure on

would be to count the total number of individuals of a given species

scavengers (Anadón, Sánchez‐Zapata, Carrete, Donázar, & Hiraldo,

in each photograph. However, this measure may count the same2010). Finally, it is also important to notice that most of the regionsindividual several times and might bias toward species that visit theassessed in this study are located in human‐dominated areas, andcarcasses for longer periods of time. More importantly, in our study,

we lack sites from the most diverse regions in the planet. Despiteusing the total number of individuals of a species may be misleading

having an unbalanced sample distribution toward areas in temper‐

because data were collected using different experimental designs.

ate latitudes, the importance of HF over climatic and latitudinal

For example, authors set camera traps to have refractory periods

factors could be expected to increase with a more balanced sample

from 30 s to 5 min. This may result in a large variance in the number

since most of the study sites considered are located in regions withof individuals detected, which is related to the experimental design

higher human pressure than underrepresented regions (e.g., tropi‐

instead of real biological differences. An alternative for estimating

cal and subtropical biomes). Thus, more complete gradients of the| 3015SEBASTIÁN‐GONZÁLEZ et al.

environmental conditions should be investigated using new datasets

MEC (CGL2012‐40013‐C02‐01/02, CGL2015‐66966‐C2‐1‐R andfrom understudied regions.

CGL2017‐89905‐R) and from the Junta de Andalucía (RNM‐1925).
We are grateful to Orr Spiegel and two anonymous reviewers for

5 | CO N C LU S I O N
Our study highlights a novel consequence of the global changes
driven by human impact in the Anthropocene. Latitudinal or climatic
patterns did not seem to be the main drivers of the number of terres‐trial vertebrate scavenger species in the assemblages, but we foundthat scavenger species richness was low in highly human‐impacted

very constructive reviews.

ORCID
Esther Sebastián‐González

https://orcid.org/0000-0001-6762-9514

Patricia Mateo‐Tomás
Nuria Selva

https://orcid.org/0000-0001-7229-1845

https://orcid.org/0000-0003-3389-201X

areas. This loss of scavengers may have detrimental consequences

Camilla Wikenros

https://orcid.org/0000-0002-2825-8834

for the conservation and functioning of the ecosystems where these

Heiko U. Wittmer

https://orcid.org/0000-0002-8861-188X

species live, reducing the quality and efficiency of the ecosystem

Miha Krofel

services provided by scavengers. Thus, we urge for specific man‐agement and conservation actions to preserve scavengers and theirhttps://orcid.org/0000-0002-2010-5219

Arockianathan Samson

https://orcid.org/0000-0002-0804-7941

functions worldwide.
Actions should include effective conservation plans of the mostglobally endangered scavengers (vultures, raptors, and top preda‐tors) threatened by poisoning, veterinary drugs, persecution, andmortality associated with infrastructures (power lines, wind farms,
and roads; e.g., Botha et al., 2017). Furthermore, actions favoring
traditional extensive farming systems and strengthening the link
between farmers and nature can be a strategic tool for fostering
positive perceptions of scavengers (Morales‐Reyes et al., 2018),
and promoting their conservation and the ecosystem services
they provide. Both actions match within the strategic goals of UN
Biodiversity Targets (https​://www.cbd.int/sp/targe​t s/, particularly
those of strategies C & D) and should be addressed and reinforcedon their upcoming renewal.

AC K N OW L E D G E M E N T S
ESG, JMPG, and ACA were supported by Juan de la Cierva contracts(Ministerio de Economía y Competitividad, MEC; IJCI‐2015‐24947,
FJCI‐2015‐25632, and IJCI‐2014‐20744, respectively). ESG was also
supported by Generalitat Valenciana (SEJI/2018/024), ACA by theGovern de les Illes Balears (PD/039/2017), and MM by a Ramón yCajal contract (MEC; RYC‐2015‐19231). EA was supported by La
Caixa‐Severo Ochoa International PhD Program 2015. NS was partly
supported by the National Science Centre in Poland (2013/08/M/
NZ9/00469). SAL thanks PICT (BID) 0725/2014. MK and KJ
were supported by the Slovenian Research Agency (P4‐0059).
Contributions of KT and JCB were partially supported throughfunding from U.S. Department of Agriculture, the U.S. Departmentof Energy under (DE‐EM0004391) to the University of Georgia
Research Foundation. EB and EF were supported by the USA National
Science Foundation. CK completed study under research permit
NCST/5/002/R/448 with support from Hawk Mountain Sanctuary,
The Peregrine Fund, and via Pompeo M. Maresi Memorial Fund via
Princeton University. JAS and CCW were supported by the USA
National Science Foundation #1255913 and the Gordon and Betty
Moore Foundation. Several authors were funded by funds from the

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S U P P O R T I N G I N FO R M AT I O N
Additional supporting information may be found online in the
Supporting Information section at the end of the article.     How to cite this article: Sebastián‐González E, Barbosa JM,
Pérez‐García JM, et al. Scavenging in the Anthropocene:
Human impact drives vertebrate scavenger species richness at
a global scale. Glob Change Biol. 2019;25:3005–3017. https​://doi.org/10.1111/gcb.14708​