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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 (Platyrrhine). 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 feedback 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 species being sensitive to habitat area and fragmentation, and frugivorous species more threatened 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 conservation agendas.
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Biodiversity and Conservation (2023) 32:2655–2669
https://doi.org/10.1007/s10531-023-02623-w
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ORIGINAL RESEARCH
Synergistic effects ofhabitat fragmentation andhunting
ontheextinction risk ofneotropical primates
GiordanoMancini1 · AnaBenítez‑López2,3 · MorenoDiMarco1 ·
MichelaPacici1 · CarloRondinini1 · LucaSantini1
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 etal. 2016), and are particularly severe in tropical forests
(Lewis etal. 2015). In fact, tropical ecosystems are increasingly encroached by crops or
livestock (Potapov etal. 2017), timber, energy production (Duden etal. 2020) and infra-
structure (Laurance etal. 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 etal. 2017). These threats are intertwined
and may act in synergy (Brook etal. 2008; Romero-Muñoz etal. 2020), as road building
to access natural resources, deforestation and settlement expansion facilitate the access of
hunters to forest fragments (Benítez-López etal. 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 etal. 2017), forest specialist species are more vulnerable to land-use change due to
deforestation (Galán-Acedo etal. 2019a, b) and generalist species are usually able to cope
with disturbed habitat (Galán-Acedo etal. 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 etal. 2018; Romero-Muñoz etal.
2020) or pantropical scale (Gallego-Zamorano etal. 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 etal. 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 etal. 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 etal. 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 etal. 2019; Watling etal. 2020). However, these interpretations are currently
debated on both empirical and theoretical grounds (Fletcher etal. 2018; Betts etal. 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 etal. 2021; Broekman etal. 2022).
Primates are among the mammal groups most affected by hunting (Ripple etal. 2016),
and are particularly sensitive to deforestation which usually results in the fragmentation
of continuous habitat in smaller patches (Estrada etal. 2017; Eppley etal. 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 etal. 2012; Bello
etal. 2015; Estrada etal. 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 etal. 2017), whereas Ateles is threatened by both hunting
and deforestation (Stafford etal. 2017; Aquino etal. 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
etal. 2017), increasing fragmentation (Crooks etal. 2017) and hunting pressure (Estrada
etal. 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 etal.
2016; Benítez-López et al. 2017, 2019), and species with low population density to be
more sensitive to amount of habitat area (Sykes etal. 2020), fragmentation (Eppley etal.
2020) and hunting. We also expect a positive interaction between environmental predictors
yielding a multiplicative effect on extinction risk (Romero-Muñoz etal. 2020).
Materials andmethods
Predictors
Species andbiological 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 etal. 2000; Cardillo etal. 2008; Davidson etal. 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 etal. 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 etal. 2018a, b; Eppley etal. 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 etal. 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 etal. 2007). Species traits
were extracted from different databases: PanTHERIA (Jones etal. 2009), EltonTraits (Wil-
man etal. 2014), Amniotes (Myhrvold etal. 2015), AnAge (de Magalhaes & Costa 2009)
and TetraDENSITY (Santini etal. 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 ofHabitat andFragmentation
We computed the amount of area of habitat within the IUCN Red List species ranges
(Brooks etal. 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 etal. (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 etal. 2007; Souza-Alves etal. 2019; Eppley etal. 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 etal. 2018; Eppley
etal. 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
etal. 2016). The area of habitat was measured at 90m resolution, matching the coarsest
resolution of the two raster layers (i.e. 30m forest coverage, 90m 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 etal. 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 etal. 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 etal. (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 etal. 2000; Car-
dillo etal. 2004; Polaina etal. 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 etal.
2015; Pacifici etal. 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 etal. 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 etal. 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 etal. 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 etal., 2014; Di Marco etal. 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 etal.
2005; Fritz etal. 2009; Hilbers etal. 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 etal. 2017; Ripple etal. 2017),
and hunting (Redford 1992; Ripple etal. 2016; Benítez-López etal. 2019). Matching the
results of previous studies (Eppley etal. 2020; Sykes etal. 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 etal.
2015; Fletcher etal. 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 etal. 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 etal. 2018; Vieilledent
etal. 2018; Eppley etal. 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 etal. 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 etal. 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 etal. 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 etal. 2015, 2017; Benítez-López etal. 2017, 2019; Eppley etal. 2020; Sykes
etal. 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 etal. 2008;
Gallego-Zamorano etal. 2020; Romero-Muñoz etal. 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
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:// creat iveco mmons. org/ licen ses/ by/4. 0/.
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Authors and Aliations
GiordanoMancini1 · AnaBenítez‑López2,3 · MorenoDiMarco1 ·
MichelaPacici1 · CarloRondinini1 · LucaSantini1
* 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 ofBiology andBiotechnologies “Charles Darwin”, Sapienza University ofRome,
Viale Dell’Università 32, 00185Rome, Italy
2 Integrative Ecology Group, Estación Biológica de Doñana (EBDCSIC), Avda. Américo Vespucio
26, 41092Seville, Spain
3 Department ofBiogeography andGlobal Change, Museo Nacional de Ciencias Naturales (MNCN
CSIC), Calle José Gutiérrez Abascal 2, 28006Madrid, Spain
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... Many of these impacts extend over long periods, occur with a lag (Semper-Pascual et al. 2021;Tilman et al. 2017) and can trigger cascading effects throughout food webs ). In addition, multiple anthropogenic pressures may interact, such as the effects of habitat loss and hunting, and through this amplify biodiversity degradation (Mancini et al. 2023;Romero-Muñoz et al. 2019). This adds considerable complexity to the assessment of biodiversity change, making it difficult to attribute observed changes to specific pressures. ...
... Importantly, human population density should be interpreted to represent multiple direct and indirect effects on the diversity and distribution of large mammals. Direct effects include hunting for meat, tools and trophies (e.g., in Europe for lion and leopard during the Roman Empire), as well as persecution due to human-wildlife conflict (Mancini et al. 2023;Romero-Muñoz et al. 2019;Schmölcke 2023). These anthropogenic pressures had profound impacts on large mammal populations both in the deeper past and recently, in some cases leading to species extinction (Chapron et al. 2014;Dembitzer et al. 2022;Svenning, Buitenwerf, et al. 2024;Svenning, Lemoine, et al. 2024;Svenning, McGeoch, et al. 2024). ...
Article
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Aim People have strongly influenced the biosphere for millennia, but how their increasing activities have shaped wildlife distribution is incompletely understood. We examined how the distribution of European large (>8 kg), wild mammals has changed in association with changing anthropogenic pressures and climate change through the Holocene. Location Europe. Methods We used over 17,000 zooarchaeological records of 20 species spanning 12,000 years to develop time‐calibrated species distribution models, incorporating dynamic data on cropland extent, natural vegetation fragmentation, human population density and climate. We assessed habitat availability and potential species richness across time and within seven biogeographical regions. We also compared anthropogenic pressures at zooarchaeological record sites with present‐day habitats of remaining large mammals to evaluate recent increases in their potential for coexistence with human activities. Results We found a continuous decline in potential large mammal species richness, particularly linked to changes in human population density. Most habitat loss became evident continentally after 1500 AD, but in the Atlantic and Mediterranean bioregions, habitat loss reached 20% during the Iron/Roman Ages (1000 BC–500 AD) due to increasing human population density. Climate change initially boosted species richness (+0.67 species/km² on average) until the end of the Mesolithic but had negligible effects afterward. Today, large mammals appear to have a higher potential for coexisting with people compared to the past (e.g., herbivores today inhabit areas with a mean human population density of 95 people/km², compared to an average of 17 people/km² in the period 1500–2000 AD). Main Conclusions Our study emphasizes the crucial role of anthropogenic pressures over natural climate change in determining the distribution and diversity of large mammal communities throughout history. Additionally, our results indicate that contemporary anthropogenic trends like land‐use de‐intensification and stronger conservation policies can counteract the impact of past, higher anthropogenic pressures and reverse defaunation.
... Understanding the feeding ecology and activity budget of the species in human-dominated habitats contribute to understanding how changing environments influence primate ecology and evolution (Kebede et al. 2013;Kuo et al. 2021) and their capacity to coexist in the long term with their human neighbours. For example, in fragmented landscapes primate species have been highly vulnerable to local extinctions (Cowlishaw and Dunbar 2000;Mancini et al. 2023) and there is a need to understand the primates ecology in human-modified habitats to reverse local population extinctions. ...
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Grivet monkeys ( Chlorocebus aethiops aethiops ) are opportunistic omnivores and extremely adaptable in both rural and urban environments. A study was conducted out in human‐dominated fragmented moist Afromontane forest of wondo genet to investigate the diurnal activity budgets and feeding habits of grivet monkey. Data collection was carried out from February to September 2022 covering both wet and dry seasons. During each scan, individuals were recorded as performing one of the following activities: feeding, moving (searching for food), resting, grooming, playing and others such as drinking, vocalisation and defecation, or aggression and sexual activity. Dietary composition and preferences were assessed using scan sampling method. Proximate analysis was conducted to examine the nutritional makeup of feeding food items. The greatest proportion of the activity time budget of the grivet monkey was devoted to feeding, resting and moving, with relatively higher time devoted to feeding and moving and less time devoted to resting when compared with grivet monkeys inhabiting natural habitats. Grivet monkey utilised 42 food items grouped into 41 plant species and 1 insect. Psidium guava and Desmodium intortum , relatively with higher crude protein and less fibre, were the most preferred plants consumed. The study has pointed out that grivet monkeys in the human‐dominated landscape of Wondo Genet remnant moist afromontane forest tend to prefer to consume on fruit tress such as Persea americana, Mangifera indica and Psidium guava , which contain high nutritional content that are planted and managed around homesteads. It is recommended to plant and sustainably manage grivet monkeys' natural foods in human‐dominated landscape of Wondo Genet remnant moist afromontane forest to sustainably conserve the species and avoid/reduce human‐grivet monkey conflict.
... These changes include alterations in foraging strategies, social structures, and ranging patterns to cope with habitat fragmentation and resource scarcity (Schwitzer et al., 2011;Estrada et al., 2012;de Almeida et al., 2017;Ramsay et al., 2023). Additionally, shifts in reproductive behaviors and increased tolerance to human presence show an adaptive response to habitat disturbance (Tokuda et al. 2018;Mancini et al. 2023). While vulnerability increases over time due to habitat loss, some resilient species as Sapajus nigritus and Alouatta guariba clamitans underscore their capacity to persist in human-altered landscapes (Corrêa et al., 2018;Tokuda et al. 2018). ...
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We report a novel observation of ground nesting behavior in a couple of Aotus griseimembra within a successional inter-Andean Forest patch of Colombia. This behavior, previously unrecorded for strictly arboreal primates of the Genus Aotus, challenges conventional understanding. The monkeys exhibited typical species actions but sought refuge on the ground, possibly influenced by habitat alterations. Their visits to the ground sleeping site were monitored and confirmed the vulnerability to predators, competitors in the forest patch. These findings call the attention for further research into the response strategies of neotropical primates to environmental stressors and habitat disturbance.
... Finally, our results contribute signi cantly to advancing research on the synergistic and separate effects of habitat loss, fragmentation, and defaunation on plant populations, a highly complex theme with considerable challenges in situ (Fahrig, 2017;Mancini et al., 2023;Püttker et al., 2020). The methodology adopted proved robust and effective in recreating natural relationships and the underlying complexities of these processes. ...
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Content Habitat degradation and hunting are the main causes of the reduction in fauna diversity, richness, and biomass, characterizing defaunation. Large animal species are the most affected by this process, compromising ecosystem services such as seed dispersal. Objectives We evaluated the effects of the nonrandomdefaunation of large seed dispersers, habitat loss and fragmentation on the expansion dynamics of tropical palms (Euterpe edulis) populations. Methods We modeled the spatial dynamics of the species via RangeShiftR in landscapes with different degrees of habitat percentage and fragmentation, simulating two distinct scenarios: nondefaunated, with a complete assembly of seed dispersers, and defaunated, with an impoverished assembly of large frugivores. Then, we developed linear regression models, and the best model was selected using the Akaike information criterion. Results Habitat loss, fragmentation, and defaunation synergistically affect the abundance and density of palm hearts. Furthermore, the interaction effect between defaunation and habitat percentage was significant, indicating that in nondefaunation scenarios, the abundance and occupation of palm hearts increase substantially. Additionally, habitat loss has a greater effect on population expansion than fragmentation, which has a lower predictive power. Conclusion These results help addressthe individual and synergistic effects of defaunation, habitat loss and fragmentation on the population expansion of palm hearts. Our models contribute to the strategic planning of actions aimed at the conservation of palm hearts, highlighting habitat loss as a central point in allocating efforts for the protection of this species, as well as the importance of considering fauna data in estimates of the population expansion capacity of plant species.
... We used the RL categories as response variable: least concern (LC), near threatened (NT), vulnerable (VU), endangered (EN), critically endangered (CR). Although RL categories inherently possess an ordinal nature, only CLM allows the use of ordinal factor variables (Henry et al., 2024;Lucas et al., 2019), so we followed previous studies to adapt the response variable to each model algorithm (Bland et al., 2015;Borgelt et al., 2022;González-Suárez et al., 2012;Mancini et al., 2023;Silva et al., 2022;Soto-Saravia et al., 2021;Zizka et al., 2021Zizka et al., , 2022. In RF models, we used RL categories as a factor variable. ...
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Assessing the extinction risk of species based on the International Union for Conservation of Nature (IUCN) Red List (RL) is key to guiding conservation policies and reducing biodiversity loss. This process is resource demanding, however, and requires continuous updating, which becomes increasingly difficult as new species are added to the RL. Automatic methods, such as comparative analyses used to predict species RL category, can be an efficient alternative to keep assessments up to date. Using amphibians as a study group, we predicted which species are more likely to change their RL category and thus should be prioritized for reassessment. We used species biological traits, environmental variables, and proxies of climate and land‐use change as predictors of RL category. We produced an ensemble prediction of IUCN RL category for each species by combining 4 different model algorithms: cumulative link models, phylogenetic generalized least squares, random forests, and neural networks. By comparing RL categories with the ensemble prediction and accounting for uncertainty among model algorithms, we identified species that should be prioritized for future reassessment based on the mismatch between predicted and observed values. The most important predicting variables across models were species’ range size and spatial configuration of the range, biological traits, climate change, and land‐use change. We compared our proposed prioritization index and the predicted RL changes with independent IUCN RL reassessments and found high performance of both the prioritization and the predicted directionality of changes in RL categories. Ensemble modeling of RL category is a promising tool for prioritizing species for reassessment while accounting for models’ uncertainty. This approach is broadly applicable to all taxa on the IUCN RL and to regional and national assessments and may improve allocation of the limited human and economic resources available to maintain an up‐to‐date IUCN RL.
... While our study provides valuable insights into the cryptic impacts of overhunting, it is important to note that, in many cases, hunting practices are compounded by the synergistic effects of other drivers of defaunation such as habitat loss, degradation or, in the future, climate change (Bogoni et al., 2022;Gallego-Zamorano et al., 2020;Mancini et al., 2023;Romero-Muñoz et al., 2020). ...
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Aim Wildlife overexploitation, either for food consumption or for the pet trade, is one of the main threats to bird species in tropical forests. Yet, the spatial distribution and intensity of harvesting pressure on tropical birds remain challenging to quantify. Here, we identify the drivers of hunting‐induced declines in bird abundance and quantify the magnitude and the spatial extent of avian defaunation at a pantropical scale. Location Pantropical. Methods We compiled 2968 abundance estimates in hunted and non‐hunted sites across the tropics spanning 518 bird species. Using a Bayesian modelling framework, we fitted species' abundance response ratios to a set of drivers of hunting pressure and species traits. Subsequently, we applied our model to quantify the spatial patterns of avian defaunation across tropical forests and to assess avian defaunation across biogeographic realms, and for species captured for the pet trade or for food consumption. Results Body mass and its interactions with hunter accessibility and proximity to urban markets were the most important drivers of hunting‐induced bird abundance declines. We estimated a mean abundance reduction of 12% across the tropics for all species, and that 43% of the extent of tropical forests harbour defaunated avian communities. Large‐bodied species and the Indomalayan realm displayed the greatest abundance declines. Further, moderate to high levels of defaunation extended over 24% of the pantropical forest area, with distinct spatial patterns for species captured for the pet trade (Brazil, China and Indonesia) and for food consumption (SE Asia and West Africa). Main Conclusions Our study emphasizes the role of hunter accessibility and the proximity to urban markets as major drivers of bird abundance declines due to hunting and trapping. We further identified hotspots where overexploitation has detrimental effects on tropical birds, encompassing local extinction events, thus underscoring the urgent need for conservation efforts to address unsustainable exploitation for both subsistence and trade.
... Understanding the influence of land use on large mammal distribution is paramount due to its profound impact on habitat quality and species persistence. Studies by Blom et al. (2005), Wang et al. (2015), Smith et al. (2018), and Shanee et al. (2023) have highlighted the correlation between land use changes and the decline in forest patches, exacerbated by direct anthropogenic pressures like hunting, which poses imminent threats to forest dynamics and biodiversity, as emphasized by Rovero et al. (2014), Rich et al. (2016, and Mancini et al. (2023). ...
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Preserving landscape connectivity in the Omo-Shasha-Oluwa Forest Reserves is crucial due to human-induced fragmentation, shrinking habitats, and disrupted migration routes for wildlife. From 2014 to 2016, we conducted surveys to gather large mammal presence data, mapping their distribution using the MaxEnt algorithm. Employing Circuitscape software and circuit theory concepts, we predicted connectivity patterns for six large mammal species. Our results consistently showed robust predictive performance, with Area Under the Curve (AUC) values exceeding 0.75 for species distribution models. Notably, we identified suitable habitat patches for seven key species, spanning 1760 km ² for C. civetta , 1515 km ² for T. Scriptus , 729 km ² for L. cyclotis , 1693 km ² for P. porcus , 1350 km ² for C. mona , 1406 km ² for P. maxwellii , and 1379 km ² for C. torquatus . Our analysis highlighted distance to human settlements as the most significant predictor for habitat models concerning T. Scriptus , C. civetta , P. maxwellii , C. torquatus , P. porcus , and C. mona , whereas land use type emerged as a critical factor for L. cyclotis . Furthermore, examination of maximum current flow patterns revealed varying degrees of connectivity among habitat patches, indicating potential bottlenecks to species movement, particularly across major rivers and in areas affected by human activities. These findings offer crucial insights for conservation efforts, guiding strategies to preserve wildlife metapopulation dynamics in the Omo-Shasha-Oluwa Forest Reserves landscape
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Background The document presents an extensive set of data on the occurrence of Neuroptera and Raphidioptera in some regions of European Russia. The results of our own research, as well as scientific collections, have been processed. The data were collected in 17 regions. In our own research, we used different ways to obtain information, which allowed us to collect extensive material for the dataset. This dataset provides valuable information about the biodiversity of Neuroptera and Raphidioptera, the abundance of each taxon collected and the time of taxon collections. New information Our dataset contains up-to-date information on the occurrence of Neuroptera and Raphidioptera in the Volga River and Don River Basins located in the Russian Plain of European Russia (17 regions of European Russia). The dataset consists of 4,826 occurrence records. All of them are georeferenced (17,373 individuals were studied). A total of 83 species of Neuroptera (8 families, 36 genera) and four species of Raphidioptera (2 families, 4 genera) were recorded within the investigated area.
Thesis
Primates are a highly threatened group among terrestrial vertebrates and offer valuable insights into studying extinction risk. Some species inhabit areas of low human activity (mainly in South America), but even those will face increasing pressure in the near future. Consequently, identifying easily accessible and cost-effective surrogates for anthropogenic pressure is essential for understanding and anticipating these changes. We aim to test if low-impact areas are good predictors of extinction risk. We tested the total human footprint, a composite index-harder to interpret and maybe to obtain accurately for the entire globe, and a more straightforward index, the roadless areas. Because the extinction risk is also affected by life history traits, we also integrated info on body mass, home range, and frugivory. Roadless areas (RAs) have been observed to enhance species conservation status. This study focuses on primates as a case study to investigate the following: 1) whether RAs serve as a better correlate of extinction risk compared to HFI, 2) if RAs can be utilized for predicting extinction risk, and 3) whether RAs can aid in establishing a list of priority species for conservation efforts. To address these questions, a phylogenetic comparative analysis was conducted. Our results indicate that the extent of roadless areas strongly correlates with primate extinction risk and proves to be a better indicator than the total human footprint. Furthermore, this study reveals a positive relationship between female body mass and extinction risk and frugivory, aligning with the scientific literature. Future directions include the prediction of extinction risk and the assessment of a list of priority species.
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Among mammals, the order Primates is exceptional in having a high taxonomic richness in which the taxa are arboreal, semiterrestrial, or terrestrial. Although habitual terrestriality is pervasive among the apes and African and Asian monkeys (catarrhines), it is largely absent among monkeys of the Americas (platyrrhines), as well as galagos, lemurs, and lorises (strepsirrhines), which are mostly arboreal. Numerous ecological drivers and species-specific factors are suggested to set the conditions for an evolutionary shift from arboreality to terrestriality, and current environmental conditions may provide analogous scenarios to those transitional periods. Therefore, we investigated predominantly arboreal, diurnal primate genera from the Americas and Madagascar that lack fully terrestrial taxa, to determine whether ecological drivers (habitat canopy cover, predation risk, maximum temperature, precipitation, primate species richness, human population density, and distance to roads) or species-specific traits (body mass, group size, and degree of frugivory) associate with increased terrestriality. We collated 150,961 observation hours across 2,227 months from 47 species at 20 sites in Madagascar and 48 sites in the Americas. Multiple factors were associated with ground use in these otherwise arboreal species, including increased temperature, a decrease in canopy cover, a dietary shift away from frugivory, and larger group size. These factors mostly explain intraspecific differences in terrestriality. As humanity modifies habitats and causes climate change, our results suggest that species already inhabiting hot, sparsely canopied sites, and exhibiting more generalized diets, are more likely to shift toward greater ground use.
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Biodiversity is severely threatened by habitat destruction. As a consequence of habitat destruction, the remaining habitat becomes more fragmented. This results in time‐lagged population extirpations in remaining fragments when these are too small to support populations in the long term. If these time‐lagged effects are ignored, the long‐term impacts of habitat loss and fragmentation will be underestimated. We quantified the magnitude of time‐lagged effects of habitat fragmentation for 157 nonvolant terrestrial mammal species in Madagascar, one of the biodiversity hotspots with the highest rates of habitat loss and fragmentation. We refined species’ geographic ranges based on habitat preferences and elevation limits and then estimated which habitat fragments were too small to support a population for at least 100 years given stochastic population fluctuations. We also evaluated whether time‐lagged effects would change the threat status of species according to the International Union for the Conservation of Nature (IUCN) Red List assessment framework. We used allometric relationships to obtain the population parameters required to simulate the population dynamics of each species, and we quantified the consequences of uncertainty in these parameter estimates by repeating the analyses with a range of plausible parameter values. Based on the median outcomes, we found that for 34 species (22% of the 157 species) at least 10% of their current habitat contained unviable populations. Eight species (5%) had a higher threat status when accounting for time‐lagged effects. Based on 0.95‐quantile values, following a precautionary principle, for 108 species (69%) at least 10% of their habitat contained unviable populations, and 51 species (32%) had a higher threat status. Our results highlight the need to preserve continuous habitat and improve connectivity between habitat fragments. Moreover, our findings may help to identify species for which time‐lagged effects are most severe and which may thus benefit the most from conservation actions.
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Habitat loss is the leading cause of the global decline in biodiversity, but the influence of human pressure within the matrix surrounding habitat fragments remains poorly understood. Here, we measure the relationship between fragmentation (the degree of fragmentation and the degree of patch isolation), matrix condition (measured as the extent of high human footprint levels), and the change in extinction risk of 4,426 terrestrial mammals. We find that the degree of fragmentation is strongly associated with changes in extinction risk, with higher predictive importance than life-history traits and human pressure variables. Importantly, we discover that fragmentation and the matrix condition are stronger predictors of risk than habitat loss and habitat amount. Moreover, the importance of fragmentation increases with an increasing deterioration of the matrix condition. These findings suggest that restoration of the habitat matrix may be an important conservation action for mitigating the negative effects of fragmentation on biodiversity.
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In Saura (Journal of Biogeography, 48, 11–22, 2021), I showed that the habitat amount hypothesis (HAH) has been considerably misinterpreted in several ways. In her response to my findings, Fahrig (Journal of Biogeography, 2021) admits that some predictions that were previously attributed to the HAH do not logically derive from it. She has however one main objection to my conclusions: that there are some cases where the HAH predicts higher site‐level species richness with more fragmentation in a region. I here explain why this is a partial and questionable observation that distracts and potentially misleads our understanding of the HAH predictions. It does not appropriately represent the fundamental and overwhelming negative effects of fragmentation on site‐level species richness that are predicted by the HAH. The HAH predicts that the highest site‐level species richness in a region will happen when all habitat is found in a single and compact habitat patch. Any departure from this zero‐fragmentation case, as well as any additional increase in the number, isolation, elongation or perforation of patches per se, will always have negative effects on site‐level species richness according to the HAH. I more briefly discuss a relatively minor comment by Fahrig on the possible slopes of the species–area relationship when the HAH holds. I conclude that the views and interpretations that have prevailed since the HAH was proposed—that the HAH negates the importance of fragmentation—can no longer be maintained. The HAH predicts that habitat configuration matters for conservation and that fragmentation is a threat to biodiversity.
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Aim Our understanding of the biological strategies employed by species to cope with challenges posed by aridity is still limited. Despite being sensitive to water loss, bats successfully inhabit a wide range of arid lands. We here investigated how functional traits of bat assemblages vary along the global aridity gradient to identify traits that favour their persistence in arid environments. Location Global. Time period Contemporary. Major taxa studied Bats. Methods We mapped the assemblage‐level averages of four key bat traits describing wing morphology, echolocation and body size, based on a grid of 100‐km resolution and a pool of 915 bat species, and modelled them against aridity values. To support our results, we conducted analyses also at the species level to control for phylogenetic autocorrelation. Results At the assemblage level, we detected a rise in values of aspect ratio, wing loading and forearm length, and a decrease in echolocation frequency with increasing aridity. These patterns were consistent with trends detected at the species level for all traits. Main conclusions Our findings show that trait variation in bats is associated with the aridity gradient and suggest that greater mobility and larger body size are advantageous features in arid environments. Greater mobility favours bats’ ability to track patchy and temporary resources, while the reduced surface‐to‐volume ratio associated with a larger body size is likely to reduce water stress by limiting cutaneous evaporation. These findings highlight the importance of extending attention from species‐specific adaptations to broad scale and multispecies variation in traits when investigating the ability of species to withstand arid conditions.
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Land-use change is a root cause of the extinction crisis, but links between habitat change and biodiversity loss are not fully understood. While there is evidence that habitat loss is an important extinction driver, the relevance of habitat fragmentation remains debated. Moreover, while time delays of biodiversity responses to habitat transformation are well-documented, time-delayed effects have been ignored in the habitat loss versus fragmentation debate. Here, using a hierarchical Bayesian multi-species occupancy framework, we systematically tested for time-delayed responses of bird and mammal communities to habitat loss and to habitat fragmentation. We focused on the Argentine Chaco, where deforestation has been widespread recently. We used an extensive field dataset on birds and mammals, along with a time series of annual woodland maps from 1985 to 2016 covering recent and historical habitat transformations. Contemporary habitat amount explained bird and mammal occupancy better than past habitat amount. However, occupancy was affected more by the past rather than recent fragmentation, indicating a time-delayed response to fragmentation. Considering past landscape patterns is therefore crucial for understanding current biodiversity patterns. Not accounting for land-use history ignores the possibility of extinction debt and can thus obscure impacts of fragmentation, potentially explaining contrasting findings of habitat loss versus fragmentation studies.
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The habitat amount hypothesis (HAH) predicts that species richness in a habitat site increases with the amount of habitat in the 'local landscape' defined by an appropriate distance around the site, with no distinct effects of the size of the habitat patch in which the site is located. It has been stated that a consequence of the HAH, if supported , would be that it is unnecessary to consider habitat configuration to predict or manage biodiversity patterns, and that conservation strategies should focus on habitat amount regardless of fragmentation. Here, I assume that the HAH holds and apply the HAH predictions to all habitat sites over entire landscapes that have the same amount of habitat but differ in habitat configuration. By doing so, I show that the HAH actually implies clearly negative effects of habitat fragmentation, and of other spatial configuration changes, on species richness in all or many of the habitat sites in the landscape, and that these habitat configuration effects are distinct from those of habitat amount in the landscape. I further show that, contrary to current interpretations , the HAH is compatible with a steeper slope of the species-area relationship for fragmented than for continuous habitat, and with higher species richness for a single large patch than for several small patches with the same total area (SLOSS). This suggests the need to revise the ways in which the HAH has been interpreted and can be actually tested. The misinterpretation of the HAH has arisen from confounding and overlooking the differences in the spatial scales involved: the individual habitat site at which the HAH gives predictions, the local landscape around an individual site and the landscapes or regions (with multiple habitat sites and different local landscapes) that need to be analysed and managed. The HAH has been erroneously viewed as negating or diminishing the relevance of fragmentation effects, while it actually supports the importance of habitat configuration for biodiversity. I conclude that, even in the cases where the HAH holds, habitat fragmentation and configuration are important for understanding and managing species distributions in the landscape.
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Understanding changes in species distributions is essential to disentangle the mechanisms that drive their responses to anthropogenic habitat modification. Here we analyse the past (1970s) and current (2017) distribution of 204 species of terrestrial non-volant mammals to identify drivers of recent contraction and expansion in their range. We find 106 species lost part of their past range, and 40 of them declined by >50%. The key correlates of this contraction are large body mass, increase in air temperature, loss of natural land, and high human population density. At the same time, 44 species have some expansion in their range, which correlates with small body size, generalist diet, and high reproductive rates. Our findings clearly show that human activity and life history interact to influence range changes in mammals. While the former plays a major role in determining contraction in species’ distribution, the latter is important for both contraction and expansion. Understanding why many species ranges are contracting while others are stable or expanding is important to inform conservation in an increasingly human-modified world. Here, Pacifici and colleagues investigate changes in the ranges of 204 mammals, showing that human factors mostly explain range contractions while life history explains both contraction and expansion.
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The International Union for Conservation of Nature (IUCN) Red List of Threatened Species is central in biodiversity conservation, but insufficient resources hamper its long-term growth, updating, and consistency. Models or automated calculations can alleviate those challenges by providing standardised estimates required for assessments, or prioritising species for (re-)assessments. However, while numerous scientific papers have proposed such methods, few have been integrated into assessment practice, highlighting a critical research–implementation gap. We believe this gap can be bridged by fostering communication and collaboration between academic researchers and Red List practitioners, and by developing and maintaining user-friendly platforms to automate application of the methods. We propose that developing methods better encompassing Red List criteria, systems, and drivers is the next priority to support the Red List.
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Saura (2020) used the habitat amount hypothesis (HAH) to predict negative effects of fragmentation per se on mean species density per site over a region. This prediction is valid but incomplete; the HAH can also predict positive effects of fragmentation on mean species density per site over a region. Saura also stated, "the HAH is compatible with a steeper slope of the species–area relationship for fragmented than for continuous habitat, and with higher species richness for a single large patch than for several small patches with the same total area (SLOSS)." Importantly, the HAH does not predict species‐area relationship (SAR) slopes or SLOSS. These require information about how species composition changes over space, while the HAH only predicts species density per site. The HAH is therefore equally compatible with a steeper or shallower SAR slope for fragmented than continuous habitat, and the HAH is equally compatible with either outcome of SLOSS.