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ORIGINAL RESEARCH
Synergistic effects ofhabitat fragmentation andhunting
ontheextinction risk ofneotropical primates
GiordanoMancini1 · AnaBenítez‑López2,3 · MorenoDiMarco1 ·
MichelaPacici1 · CarloRondinini1 · LucaSantini1
Received: 20 September 2022 / Revised: 24 March 2023 / Accepted: 11 May 2023 /
Published online: 26 May 2023
© The Author(s) 2023
Abstract
Habitat fragmentation and overexploitation of natural resources are the most prevalent and
severe threats to biodiversity in tropical forests. Several studies have estimated the effect of
these threats on species extinction risk, however the effect resulting from their interaction
remains poorly understood. Here, we assess whether and how habitat area, fragmentation,
and hunting can synergistically affect the extinction risk of neotropical primates (Platyr-
rhine). We use a Random Forest model to estimate the Red List extinction risk category of
147 primate species based on their biological traits and the environmental predictors they
are exposed to. We find that environmental variables are better predictors of extinction risk
than biological traits, and that hunting and fragmentation interact creating synergistic feed-
back that lead to higher extinction risk than when considered in isolation. We also show
that the effect of environmental predictors is mediated by biological traits, with large spe-
cies being sensitive to habitat area and fragmentation, and frugivorous species more threat-
ened by hunting. Our results increase the understanding of potentially interactive effects
between different threats, habitat area and species traits, supporting the idea that multiple
threats can reinforce each other and should be thus addressed simultaneously in conserva-
tion agendas.
Keywords IUCN Red List· Overhunting· Habitat area· Deforestation· Forest specialist
species
Introduction
Habitat loss and overexploitation of natural resources are the most prevalent biodiver-
sity threats globally (Maxwell etal. 2016), and are particularly severe in tropical forests
(Lewis etal. 2015). In fact, tropical ecosystems are increasingly encroached by crops or
livestock (Potapov etal. 2017), timber, energy production (Duden etal. 2020) and infra-
structure (Laurance etal. 2009), resulting in intense deforestation. Furthermore, seemingly
undisturbed forests may be under hunting pressure, with mammal populations reduced by
Communicated by David Hawksworth.
Extended author information available on the last page of the article
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more than 80% due to hunting (Benítez-López etal. 2017). These threats are intertwined
and may act in synergy (Brook etal. 2008; Romero-Muñoz etal. 2020), as road building
to access natural resources, deforestation and settlement expansion facilitate the access of
hunters to forest fragments (Benítez-López etal. 2017, 2019). Most studies have quantified
the effects of each threat in isolation, showing that their relative impact depends on spe-
cies ecology. In fact, while larger species are generally more targeted by hunters (Benítez-
López etal. 2017), forest specialist species are more vulnerable to land-use change due to
deforestation (Galán-Acedo etal. 2019a, b) and generalist species are usually able to cope
with disturbed habitat (Galán-Acedo etal. 2019b). In turn, species with low population
density would be highly affected by hunting, in terms of high probability of extirpation
due to low number of individuals. Recent studies have evaluated the effect of habitat loss
and hunting in combination, either at regional (Symes etal. 2018; Romero-Muñoz etal.
2020) or pantropical scale (Gallego-Zamorano etal. 2020), estimating that tropical mam-
mals have lost about 40% of their distribution due to the combined effects of habitat loss
and hunting (Gallego-Zamorano etal. 2020). However, habitat fragmentation has not been
formally considered in these studies (but see Peres 2001).
Habitat loss often results in habitat fragmentation, which can cause further decline in
biodiversity (Crooks etal. 2017) reducing the abundance of populations making them sen-
sitive to demographic and genetic stochasticity and local extinction, while hampering recol-
onization due to increased isolation (Haddad etal. 2015).Disentangling the effect of frag-
mentation from that of habitat loss presents some methodological challenges as these two
processes are intimately linked. Studies estimating habitat fragmentation separately from
habitat loss showed weak or positive effects on species abundance and richness (Fahrig
2019; Fahrig etal. 2019; Watling etal. 2020). However, these interpretations are currently
debated on both empirical and theoretical grounds (Fletcher etal. 2018; Betts etal. 2019).
For example, Saura (2020) argued that even assuming the habitat amount hypothesis holds
(e.g. habitat fragmentation per se does not have negative effects on biodiversity, but spe-
cies richness is positively related to the amount of surrounding habitat), changes in habitat
configuration are expected to alter species richness (but see Fahrig 2021 and Saura 2021).
Recent studies instead highlighted the long-term effect of fragmentation (i.e. extinction
debt), therefore questioning the reliability of studies contrasting habitat loss and fragmen-
tation effects within short timeframes (Semper-Pascual etal. 2021; Broekman etal. 2022).
Primates are among the mammal groups most affected by hunting (Ripple etal. 2016),
and are particularly sensitive to deforestation which usually results in the fragmentation
of continuous habitat in smaller patches (Estrada etal. 2017; Eppley etal. 2020). The loss
of primate species is detrimental to the functioning of tropical forest ecosystems, due to
their key roles as seed dispersers, folivores, and, in cases, as seed predators influencing the
forest regeneration and diversity (Nuñez-Iturri & Howe 2007; Barnett etal. 2012; Bello
etal. 2015; Estrada etal. 2017). Approximately 40% of neotropical primates (Platyrrhine)
assessed by the International Union for Conservation of Nature (IUCN) are considered at
risk of extinction (IUCN 2020), in particular, Lagothrix is the most threatened Platyrrhine
genus due to hunting (Stafford etal. 2017), whereas Ateles is threatened by both hunting
and deforestation (Stafford etal. 2017; Aquino etal. 2018).
Here, we present a quantitative analysis of the relative effects of area of habitat, habitat
fragmentation, hunting pressure, species biological traits (body mass, population density,
diet, gestation length and litter size), and their interactions, on the extinction risk of Neo-
tropical primates. We expect extinction risk to increase with decreasing habitat area (Betts
etal. 2017), increasing fragmentation (Crooks etal. 2017) and hunting pressure (Estrada
etal. 2017), and that the effects of these factors are mediated by species traits. Specifically,
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we expect large and slow-reproducing species to be more sensitive to hunting (Ripple etal.
2016; Benítez-López et al. 2017, 2019), and species with low population density to be
more sensitive to amount of habitat area (Sykes etal. 2020), fragmentation (Eppley etal.
2020) and hunting. We also expect a positive interaction between environmental predictors
yielding a multiplicative effect on extinction risk (Romero-Muñoz etal. 2020).
Materials andmethods
Predictors
Species andbiological traits selection
We extracted the geographic ranges and conservation status of all neotropical primate spe-
cies from the IUCN Red List database in 2019. We excluded all data deficient (DD) neo-
tropical primates for which threat status is unknown. We only selected species that were
not assessed under criterion B of the IUCN Red List, which concerns the extent of occur-
rence (B1) and the area of occupancy (B2) which may be related with the area of habitat
used as model predictor, therefore introducing circularity. All the species we selected were
assessed under the Red List criteria A and C, which are based on species population trends
and are not directly calculated using species spatial information. None of the species was
assessed under criteria D and E.
Our final sample includes 147 species (83% of all Platyrrhine species) from all five neo-
tropical families: Aotidae, Atelidae, Callitrichidae, Cebidae, Pitheciidae, with 41% classi-
fied as threatened by the Red List (Appendix A1).
We used biological traits to account for characteristics that can make species vulner-
able to threats (Purvis etal. 2000; Cardillo etal. 2008; Davidson etal. 2009). We excluded
biological variables with more than 50% of missing data (see below), therefore selected the
following biological traits: body mass, a proxy of vulnerability to hunting, as larger tropi-
cal mammals were predicted to be more hunted (Benítez-López etal. 2017, 2019); spe-
cies’ average population density, a measure of population level space use and vulnerability
to fragmentation, since species with large spatial requirements at the population level are
more sensitive to fragmentation (Santini etal. 2018a, b; Eppley etal. 2020); and percent-
age of frugivory in the diet (hereafter, frugivory), under the assumption that species with
more specialized diet generally need more space to find appropriate resources, being thus
more vulnerable to fragmentation (Eppley etal. 2020). We also selected two reproductive
traits as measures of population recovery potential: gestation length and litter size, which
represent reproductive timing and output, respectively (Bielby etal. 2007). Species traits
were extracted from different databases: PanTHERIA (Jones etal. 2009), EltonTraits (Wil-
man etal. 2014), Amniotes (Myhrvold etal. 2015), AnAge (de Magalhaes & Costa 2009)
and TetraDENSITY (Santini etal. 2018b).
The biological traits that we selected for our analysis had different proportions of
missing values—frugivory diet 4%, body mass 12%, population density 32%, litter size
44%, gestation length 45%. Therefore, we imputed the data following Penone et al.
(2014) using the “mice” package (Van Buuren & Groothuis-Oudshoorn 2011) in R
and the phylogenetic eigenvectors. We used phylogenetic eigenvectors to account for
latent traits and phylogenetic relatedness (Diniz-Filho et al. 1998). We obtained the
phylogenetic tree from the PHYLACINE database (Faurby et al. 2018). The source
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phylogeny was derived using a hierarchical Bayesian approach with a posterior distri-
bution of 1,000 trees. We extracted 10 random phylogenetic trees from the phylogeny
and extracted 20 eigenvectors from each tree, which we used to test the sensitivity of
our imputation to phylogenetic uncertainty. We repeated the imputation using 5, 10 and
20 phylogenetic eigenvectors. We produced 10 different imputed datasets each iteration
with the predictive mean matching method. To assess the imputation performance, we
calculated the Normalized Root Mean Squared Error (NRMSE), which ranges from 0 to
1, for each variable. Lower values of NRMSE indicate better estimates of the variables.
Area ofHabitat andFragmentation
We computed the amount of area of habitat within the IUCN Red List species ranges
(Brooks etal. 2019), to represent the amount of habitat available and potentially occu-
pied by species within their ranges. We estimated the area of habitat by combining the
raster layers of forest cover and loss from Hansen etal. (2013), the digital elevation
model from Jarvis et al. (2008), and the polygons of the species geographic ranges
available from the IUCN database (IUCN 2020). First, as forest layers are expressed as
percentage of canopy cover, we binarized these layers into forest and non-forest layers.
Although some neotropical primates can perform specific activities at the ground level
(Mourthé etal. 2007; Souza-Alves etal. 2019; Eppley etal. 2022), all Plathrrine mon-
keys are strictly arboreal, thus we followed previous studies and considered only areas
where the tree cover was > 75% (Aleman et al. 2018; Vieilledent etal. 2018; Eppley
etal. 2020). Then, we overlaid the geographic range areas with the binary forest maps
to calculate the amount of forest habitat for each species. Subsequently, we excluded the
portions of forest habitat outside the species altitudinal range of presence (Tracewski
etal. 2016). The area of habitat was measured at 90m resolution, matching the coarsest
resolution of the two raster layers (i.e. 30m forest coverage, 90m elevation model). The
values of the area of habitat were reported in km2.
We calculated two common fragmentation metrics from the area of habitat maps to
estimate the degree of the forest fragmentation: the mean patch area in km2 (Innes &
Koch 1998) and the distance from forest edge in km (Crooks etal. 2017), measured as
the average Euclidean distance of all cells within a species area of suitable habitat from
the nearest non-forest edge. The former takes into account the area of continuous habitat
and thus is a proxy of the potential size of isolated or semi-isolated population, while
the latter is a measure of habitat degradation in terms of habitat alteration due to edge
effects (Pfeifer etal. 2017). Low values of mean patch area and low values of distance
from forest edge indicate a more fragmented habitat.
Defaunation index
As a proxy of hunting pressure, we used the hunting-induced defaunation index from
Benítez-López etal. (2019). This index is derived from a model predicting the hunting
pressure on tropical mammal species. The defaunation index ranges from 0 (no defau-
nation) to 1 (local extirpation). We extracted the average defaunation index within the
species available habitat in the distribution range, which represents how much a species
population size is reduced by hunting pressure.
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Modeling
We used the IUCN Red List category of species as the response variable represent-
ing extinction risk in our model. Following previous studies (Purvis etal. 2000; Car-
dillo etal. 2004; Polaina etal. 2016), we converted this categorical variable to numeric
assigning a value to each extinction risk category: Least Concern = 1, Near Threat-
ened = 2, Vulnerable = 3, Endangered = 4, Critically Endangered = 5. We also trans-
formed the area of habitat, mean patch area, distance from edge and population density
using natural logarithm for graphical purposes.
To quantify the relationship between the predictors and the species extinction risk we
used a Random Forest model. Random Forest is a machine learning approach that has
been successfully used in other ecological and conservation analyses (Di Marco etal.
2015; Pacifici etal. 2020). These models are more flexible than statistical linear models,
are robust to collinearity and structured data, and allow to estimate complex non-linear
relationships and interactions among predictors (Cutler etal. 2007). Also, the model
does not make any assumption on the distribution of the response variable, which is
generally problematic in linear models such as phylogenetic least square models (Lucas
et al. 2019; Cazalis et al. 2022), i.e. ordinal distribution with non-even increase in
extinction risk between Red List categories. We estimated the relative importance of
each variable in predicting extinction risk category by measuring the relative increase in
the mean square error of the Random Forest model when the values of the variables are
randomly permuted (Cutler etal. 2007).
To estimate the accuracy of the model, we performed a 5-repeated tenfold cross vali-
dation with an 80–20% training–testing sets and assessing each model using the Root
Mean Square Error (RMSE) at each iteration. We selected the model with the low-
est RMSE, which was the most accurate under cross-validation, as our final model.
Then, we assessed the performance of the final model using the percentage of variance
explained. We repeated the Random Forest model across all imputed datasets in order
to assess how uncertainty in the imputation procedure could influence our conclusions
(Conenna et al. 2021). To quantify the interaction between environmental predictors
and traits, we created partial response plots between the predicted extinction risk as a
function of an environmental predictor (area of habitat, mean patch area, distance from
edge or defaunation index), while modifying the value of the other interacting envi-
ronmental predictor or biological trait. The rest of predictors were maintained at aver-
age values. For example, to assess the interaction between the defaunation index and
the area of habitat, we plotted extinction risk as a function of increasing defaunation,
and for area of habitat at high (95th percentile) and low (5th percentile) values. To bet-
ter show the interaction effects, we generated a plot displaying the distribution of the
distances between two response curves at the two extremes of the range across the ten
imputed datasets (Fig. A.1). The median of the distribution indicates the effect size of
the interactions, and distributions not overlapping with zero are an indication of consist-
ent directional interaction effects.
Spatial analyses were conducted using GRASS GIS 7.4 (GRASS Development Team
2017), all statistical analyses were computed using R 4.0.2 (R Core Team 2020) in
RStudio 1.3.959 (RStudio Team 2020), and using the R packages ‘randomForest’ (Liaw
& Wiener 2002), ‘caret’ (Kuhn etal. 2020), ‘pdp’ (Greenwell 2017), ‘PVR’ (Santos
2018) and ‘ape’ (Paradis & Schliep 2019).
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Results
The best imputation procedure included 10 phylogenetic eigenvectors in addition to life
history trait variables, resulting in an average NRMSE across all imputed trait variables
ranging from 0.08 to 0.24, which indicates good imputation performance (Table A.1). The
Random Forest model was able to explain 44.5% (± 2.49%, 95% confidence interval) of the
variance. Overall, the environmental predictors were more important than biological pre-
dictors in our model, with all environmental variables among the top four important vari-
ables. The most important variables in our model were the area of habitat and the defauna-
tion index, followed by the fragmentation variables and body mass (Fig.1).
The area of habitat and the defaunation index yielded, respectively, a strong negative
and strong positive effect on extinction risk (Fig.2a,b). Thus, extinction risk increased as
the amount of habitat decreased, and with increasing hunting-induced defaunation. The
two variables of fragmentation also showed a negative influence on extinction risk, with
low values of mean patch area and low values of distance from the edge associated with
higher extinction risk (Fig.2c,d). The average extinction risk was higher for primates with
large body mass, high gestation length and low population density, while species with
higher litter size showed a slight increase in extinction risk (Fig.2e–h). The percentage of
frugivory showed a slightly non-linear trend, with the highest risk of extinction for species
consuming very low, or very high percentage of fruit in their diets (Fig.2i).
The interactions between the amount of area of habitat, fragmentation and hunting vari-
ables showed synergistic effects on species extinction risk (Fig.3). The effect of hunting
was stronger with decreasing average distance from edge (Figs.3a,4) and with smaller val-
ues of mean patch areas (Figs.3b,4), indicating that species living in fragmented areas are
also more vulnerable to hunting. The effect of mean patch area was stronger when the area
of habitat was low, indicating a stronger effect of fragmentation in species distributed in
small areas (Figs.3d,e,4). Hunting pressure and area of habitat did not show a clear inter-
action (Figs.3c,4).
Area of habitat, fragmentation and hunting variables also exhibited variable interaction
effects with biological traits (Fig.4). For example, the extinction risk of large-sized species
Fig. 1 Variable importance in the Random Forest model predicting species extinction risk. Variable impor-
tance, with 95% confidence intervals, is represented as the increase in Mean Square Error (MSE) associated
with random permutation of its values. Red bars indicate environmental predictors, blue bars indicate spe-
cies biological traits
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decreased as the mean patch area and distance to edge increased (indicating less frag-
mentation), but at a lower rate than in the case of small-sized species (Fig. A.2), suggest-
ing higher sensitivity to fragmentation in large species. Similarly, large species showed a
stronger positive effect of area of habitat. The effect of area of habitat was more important
Fig. 2 Partial responses plots displaying the relationship between extinction risk and each predictor, while
all the other variables are kept to their average value. Shaded colours represent 95% confidence intervals.
Environmental predictors are represented in red and biological predictors in blue
Fig. 3 Interaction between area of habitat and threat variables. The plots show the response of one variable
at two fixed values of the interacting variable, corresponding to its 5th percentile (green) and 95th percen-
tile (purple). The shaded areas represent the 95% confidence intervals of the responses calculated across the
responses of the ten imputed datasets. Different slopes in the two curves represent the interaction between
the two variables
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for species with long gestation length (Fig. A.3) and living at low density (Fig. A.5). Fur-
ther, the defaunation index had a stronger negative effect on more frugivorous species (Fig.
A.6). Finally, frugivory and species density were also associated with higher sensitivity to
distance from edge (Fig. A.5, A.6). Other traits did not show clear interactions with any of
the environmental predictors (Fig. A.4, A.5, A.6).
Discussion
In this study we estimated the effect of the area of habitat, fragmentation, and hunting on
the extinction risk of Neotropical primates, while considering differences in their biologi-
cal traits. We found that reduced area of habitat, increased hunting pressure and increased
habitat fragmentation were associated with higher extinction risk. Additionally, larger,
slow-reproducing, and low-density species were more threatened on average. Furthermore,
larger species appeared to respond more negatively to hunting and fragmentation, and be
more sensitive to a lower area of habitat than small-sized species. Finally, we report clear
Fig. 4 Size of the interaction effects between pairs of variables on the extinction risk of neotropical pri-
mates. The effect size of the interactions is calculated by measuring the delta in extinction risk prediction
distances between two response curves at the two extremes of their distribution (see Fig. S1). This proce-
dure is repeated across the 10 imputed datasets to estimate a distribution of effect sizes. Distributions whose
at least 90% of the values were above or below 0 were represented with labels in bold. Interactions between
environmental predictors are represented in red and interactions between an environmental predictor and a
biological predictor in blue
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interactions between fragmentation and hunting pressure, and between area of habitat and
fragmentation, suggesting their synergistic effect in determining species extinction risk.
Overall, threats and the extent of the area of habitat and body mass were the most
important drivers of extinction risk, with other biological predictors showing a minor con-
tribution to extinction risk. This result supports the idea that extrinsic variables (environ-
mental or threat variables) should be included in comparative risk analyses in addition to
biological predictors (Murray etal., 2014; Di Marco etal. 2018). This, however, ultimately
depends on the group under investigation, the variability in biological traits and knowl-
edge of significant external pressures on the group of species under study. Nonetheless,
our study supports previous comparative extinction risk analyses in concluding that larger,
slow reproducing, and low-density species are more threatened on average (Cardillo etal.
2005; Fritz etal. 2009; Hilbers etal. 2016). It also supports previous studies on the role of
traits in mediating the effects of fragmentation and hunting, showing that larger species are
more sensitive to habitat area and fragmentation (Crooks etal. 2017; Ripple etal. 2017),
and hunting (Redford 1992; Ripple etal. 2016; Benítez-López etal. 2019). Matching the
results of previous studies (Eppley etal. 2020; Sykes etal. 2020), we found that species
living at low population density showed higher sensitivity to habitat area and fragmenta-
tion. Frugivory showed a weak non-linear effect, with species at a slightly higher risk of
extinction being at the extremes of the frugivory continuum. The species considered in this
study were mostly folivores or frugivores, so species at the extreme of the frugivory contin-
uum showed a more specialized diet, whereas species at the center had a more diverse diet.
Frugivores also exhibited a slightly higher sensitivity to distance from edge. Indeed, frugiv-
orous species require larger areas of continuous habitat to forage as their trophic resources
are more sparse and clumped in space compared to those of folivorous and omnivorous
species (Milton & May 1976), resulting in a reduced tolerance to fragmented habitats. Our
result may thus suggest that species with more diverse diets can better cope with degraded
habitat. Finally, we found that frugivorous species were more threatened by hunting than
omnivores and folivores, probably because many highly frugivorous primates in the neo-
tropics are large-bodied species that are heavily hunted (e.g. Ateles, Lagothrix, Alouatta).
Several recent studies have questioned the individual role of habitat fragmentation in
determining species risk of extinction. Some studies have argued that habitat fragmentation
generally has no, or even positive, effects on species richness and abundance (Fahrig 2019;
Fahrig et al. 2019), while others support the traditional view that habitat configuration,
irrespective of habitat amount, yields negative effects on species persistence (Haddad etal.
2015; Fletcher etal. 2018; Saura 2020). In this work we show that fragmentation, either
expressed as mean patch area or distance from edge, is positively related to the extinc-
tion risk of neotropical primates, and these measures interact negatively with the amount
of habitat area, reinforcing the idea that habitat configuration, not just amount, is a key
parameter for species persistence (Ramírez-Delgado etal. 2022) particularly in the case of
habitat specialists with limited mobility across non-habitat areas such as arboreal primates.
Our results should be interpreted considering some limitations. First, in this study we
measured fragmentation using a binary forest layer, thresholding the forest layer at 75% of
canopy cover following previous papers on tropical forests (Aleman etal. 2018; Vieilledent
etal. 2018; Eppley etal. 2020). This process resulted in a simplification of the landscape,
as species can experience a wide variety of canopy covers within their range and the lower
boundary likely differs among species. Second, the response variable in our model, the
IUCN Red List extinction risk category, reflects a coarse categorization of species extinc-
tion risk (but see Mooers etal. 2008). This likely reduces our ability to capture nuances in
the data and is expected to flatten the estimated effects of drivers. Finally, we worked under
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the assumption that these categories were correctly assigned. It is possible, however, that
Red List categories are incorrectly assigned because of uncertainty of data at the time of
the assessment (Santini etal. 2019). This may have introduced noise in the model, however
it is unlikely to have biased the results as we only focused on one mammalian taxon in one
region, which is assessed consistently by the same group of experts during collective work-
shops (IUCN Primate Specialist Group, Neotropic section).
Overall our study supports the role of habitat area, fragmentation and hunting as impor-
tant drivers of primate extinction risk (Estrada etal. 2017). However, it also shows that
these factors do not act consistently across all species in our sample, but rather their impact
may be exacerbated or mitigated by species traits, such as body mass and trophic ecol-
ogy (Ripple etal. 2015, 2017; Benítez-López etal. 2017, 2019; Eppley etal. 2020; Sykes
etal. 2020). Finally, it supports the idea that the amount of area of habitat and threats
like fragmentation and hunting are self-reinforcing and may lead to more severe reduc-
tions in mammal distributions and their extinction risk than anticipated (Brook etal. 2008;
Gallego-Zamorano etal. 2020; Romero-Muñoz etal. 2020). These results suggest that con-
servation efforts aimed at restoring habitat and connectivity for large species can also indi-
rectly reduce the impact of hunting, provided that human accessibility to restored areas is
restricted to local communities, and that these should be actively involved in the design,
implementation, monitoring, and evaluation of their hunting activities to ensure sustain-
ability. However, considering the reported interactions among threats, addressing threats in
isolation would unlikely result in effective conservation outcomes. Effective conservation
of threatened primates will require adopting a more holistic approach.
Supplementary Information The online version contains supplementary material available at https:// doi.
org/ 10. 1007/ s10531- 023- 02623-w.
Acknowledgements MDM and LS acknowledge support from the MUR Rita Levi Montalcini pro-
gram. ABL acknowledges support from a Ramón y Cajal grant (RYC2021-031737-I) funded by MCIN/
AEI/10.13039/501100011033 and the EU (“NextGenerationEU”/PRTR).
Author contributions GM led the study, LS and CR conceived the original idea, LS designed the analysis,
GM collected the data, GM and AB analyzed the data, GM and LS drafted the first version of the manu-
script. All authors contributed intellectually on the study design, interpretation of the results, and writing.
Funding Open access funding provided by Università degli Studi di Roma La Sapienza within the CRUI-
CARE Agreement. The authors declare that no funds, grants, or other support were received during the
preparation of this manuscript.
Declarations
Competing interest The authors have not disclosed any competing interests.
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 Com-
mons 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
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from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/.
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Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and
institutional affiliations.
Authors and Aliations
GiordanoMancini1 · AnaBenítez‑López2,3 · MorenoDiMarco1 ·
MichelaPacici1 · CarloRondinini1 · LucaSantini1
* Giordano Mancini
giordano.mancini@uniroma1.it
Ana Benítez-López
ana.benitez@mncn.csic.es
Moreno Di Marco
moreno.dimarco@uniroma1.it
Michela Pacifici
michela.pacifici@uniroma1.it
Carlo Rondinini
carlo.rondinini@uniroma1.it
Luca Santini
luca.santini@uniroma1.it
1 Department ofBiology andBiotechnologies “Charles Darwin”, Sapienza University ofRome,
Viale Dell’Università 32, 00185Rome, Italy
2 Integrative Ecology Group, Estación Biológica de Doñana (EBD‐CSIC), Avda. Américo Vespucio
26, 41092Seville, Spain
3 Department ofBiogeography andGlobal Change, Museo Nacional de Ciencias Naturales (MNCN‐
CSIC), Calle José Gutiérrez Abascal 2, 28006Madrid, Spain
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
1.
2.
3.
4.
5.
6.
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