International Journal of Primatology

, Volume 37, Issue 1, pp 47–68 | Cite as

Anthropogenic and Climatic Effects on the Distribution of Eulemur Species: An Ecological Niche Modeling Approach

Article

Abstract

Several factors can influence primate distributions, including evolutionary history, interspecific competition, climate, and anthropogenic impacts. In Madagascar, several small spatial scale studies have shown that anthropogenic habitat modification affects the density and distribution of many lemur species. Ecological niche models can be used to examine broad-scale influences of anthropogenic impacts on primate distributions. In this study, we examine how climate and anthropogenic factors influence the distribution of 11 Eulemur species using ecological niche models. Specifically, we created one set of models only using rainfall and temperature variables. We then created a second set of models that combined these climate variables with three anthropogenic factors: distance to dense settlements, villages, and croplands. We used MaxEnt to generate all the models. We found that the addition of anthropogenic variables improved the climate models. Also, most Eulemur species exhibited reduced predicted geographic distributions once anthropogenic factors were added to the model. Distance to dense settlements was the most important anthropogenic factor in most cases. We suggest that including anthropogenic variables in ecological niche models is important for understanding primate distributions, especially in regions with significant human impacts. In addition, we identify several Eulemur species that were most affected by anthropogenic factors and should be the focus of increased conservation efforts.

Keywords

Biogeography Conservation Extinction risk Human impacts Macroecology Species distribution model 

Introduction

An understanding of the factors impacting the diversity and distribution of primates can inform on past evolutionary processes, help identify current conservation challenges and priorities, and help prevent future biodiversity loss (Kamilar and Beaudrot 2013). Primate distributions are driven by many social and ecological factors including climate and habitat type (Muldoon and Goodman 2010; Reed and Fleagle 1995), intra- and interspecies competition (Kamilar and Guidi 2010; Kamilar and Ledogar 2011), predator–prey interactions (Farris et al. 2014), geographic features (Goodman and Ganzhorn 2004), and evolutionary history (Kamilar 2009; Lehman 2006), as well as an interaction between several factors, including climate change (Wilmé et al. 2006). Increasingly, anthropogenic factors alter landscapes and reduce the effective space available for species to exist, and are also known to impact the distribution of species (Irwin et al. 2010).

Climate and habitat characteristics are among the strongest evolutionary forces acting on species. In Madagascar, habitat alteration affects the density and distribution of many lemur species (Irwin 2006; Johnson and Overdorff 1999). The structural characteristics and reproductive schedule of trees may impact not only an animal’s direct nutrient intake, but also its range and travel routes (Grassi 2001; Overdorff 1993), locomotion (Dagosto and Yamashita 1998), safety from predators (Curtis et al. 1999; Miller 2002), and activity pattern (Curtis et al. 1999). Habitat alteration can also influence behavior by modifying predator–prey interactions. For example, in northeastern Madagascar (Farris et al. 2014), lemurs are less active in fragmented landscapes where they are exposed to nonnative predators and humans, in comparison with lemurs in contiguous forest that have more interactions with endemic predators. Studies focused on individual species report that some taxa thrive in altered landscapes, e.g., Hapalemur griseus (Grassi 2001), but anthropogenic impacts are generally negative (Irwin et al. 2010).

Ecological niche modeling is another approach to understand better how anthropogenic factors influence primate distributions. Ecological niche models, i.e., species distribution models, most commonly use climate variables, such as various measures of rainfall and temperature, combined with the known occurrence of species to estimate where species should be distributed based on their known niche space (Elith et al. 2006; Kamilar and Beaudrot 2015; Phillips et al. 2006). A variety of topics can be addressed using this approach, including investigating patterns of speciation (Blair et al. 2013), taxonomy (Raxworthy et al. 2007), species diversity (Kamilar et al. 2015), and niche diversity (Johnson et al. 2015), and it is possible to model shifts in species ranges resulting from future climate change (Thorne et al. 2013). In many cases, these models overpredict known species distributions because other factors that may influence distribution patterns are not considered, such as geographic barriers and biotic interactions, though recent work is attempting to address some of these issues (Boulangeat et al. 2012; Kissling et al. 2012).

Anthropogenic impacts are another factor that can have an important impact on species distributions but have rarely been used in distribution modeling studies (cf. Junker et al. 2012). Anthropogenically altered landscapes are typically quantified according to measures of their biodiversity (e.g., richness and diversity), and human activity (e.g., distance to edge, hunting pressure, distance to village). However, the type of human settlement is not often considered, though human-use activities likely differ in urban areas, villages, and agricultural lands.

The genus Eulemur is ideal for modeling the impacts of climate and anthropogenic impacts on species distributions. Eulemur is a diverse clade consisting of 12 species (Markolf et al. 2013). They live in pairs and larger social groups and exhibit different levels of group cohesion (Kappeler and Fichtel 2015). They are known to be ecologically flexible primates with great dietary diversity (Donati et al. 2007; Ossi and Kamilar 2006), and they can distribute their activity throughout a 24-h period, i.e., cathemerality, and do so to varying degrees (Curtis and Rasmussen 2006; Tattersall 1987). The geographical distribution of Eulemur is broad, and they occupy all types of forest in Madagascar (Tattersall and Sussman 1998). At the same time, pair-living and group-living Eulemur appear to suffer when living in degraded habitat (Balestri et al. 2014; Tecot 2013). The density and distribution of Eulemur has been recorded in a number of studies throughout the island (Brown and Yoder 2015).

Conservation biology research focused on Malagasy primates is especially important because Madagascar is a biodiversity hotspot with astounding species richness and endemism (Goodman and Benstead 2005; Myers et al. 2000). Yet, lemurs were recently named the most threatened group of mammals on the planet, with an increase from 74% to 94% of species threatened with extinction, largely due to the continued loss of forest (Schwitzer et al. 2014). Habitat degradation outside of protected areas is widespread, and protected areas, e.g., national parks, are impacted by illegal activities as well (Schwitzer et al. 2014). Madagascar’s lemurs have faced extinction in the past; 17 subfossil lemur species have gone extinct subsequent to the arrival of humans on the island (Burney and Flannery 2005; Burney and MacPhee 1988). This loss was at least in some part due to human hunting activity and climate change (Crowley 2010), which may have resulted in reduced genetic diversity of these megafauna (Kistler et al. 2015). Today, habitat fragmentation appears to play a primary role in reducing lemur species richness (Irwin et al. 2010).

In this study, we used an ecological niche modeling approach to examine the effect of climatic and anthropogenic factors on Eulemur distributions. In particular, we compared models using only climate variables to those that used climate and anthropogenic factors. We examined three types of anthropogenically modified habitats: dense settlements, i.e., urban areas; villages; and croplands. If anthropogenic factors impact distributions of Eulemur, then we predicted that adding anthropogenic variables to climate models would result in an increased probability that species are absent from a particular location, thereby reducing the total predicted distribution of species. In addition, we expected that the addition of anthropogenic variables will increase the predictive power of the model compared with using climate variables only.

Methods

Data Collection

We collected data for 11 Eulemur species (Markolf and Kappeler 2013; Appendix S1: E. albifrons, E. cinereiceps, E. collaris, E. coronatus, E. flavifrons, E. fulvus, E. macaco (sensu stricto), E. rubriventer, E. rufifrons, E. rufus (sensu stricto), E. sanfordi. This dataset contained known localities of Eulemur used in Brown and Yoder (2015). Brown and Yoder (2015) present multiple versions of their data and we used only their “vetted and rarefied” dataset. One advantage of this dataset is that it contains only localities that are ≥5 km apart from each other, thereby reducing the effects of spatial autocorrelation in the subsequent niche models. Additional details about these data can be found in Brown and Yoder (2015).

We used six climate variables to characterize the climatic niche space occupied by species, and these variables served as the basis for the species distribution models: 1) precipitation of the coldest quarter; 2) precipitation of the driest quarter; 3) temperature annual range; 4) minimum temperature of coldest month; 5) temperature seasonality (standard deviation × 100); 6) isothermality, defined as mean diurnal range [mean of monthly (max temperature – min temp)] divided by temperature annual range. All of the climate variables were obtained from the Worldclim database (Hijmans et al. 2005). These variables were chosen for several reasons: They should well represent the range of climatic and habitat conditions experienced by Eulemur (Kamilar and Muldoon 2010); they correlate less strongly to each other than other measures of rainfall and temperature, therefore reducing the degree of multicollinearity in the models; quantifying rainfall and temperature variation should be related to lemur physiology and overall biology (Dewar and Richard 2007; Wright 1999); and these variables were used in a recent species distribution modeling study of Eulemur by Blair et al. (2013).

We used three variables to quantity possible anthropogenic effects on Eulemur distributions, distance to 1) dense settlements, 2) villages, and 3) croplands. We obtained these data from the Anthropogenic Biomes of the World (version 2) produced by the NASA Socioeconomic Data and Applications Center (Ellis et al. 2010, 2013). The original dataset was a GeoTIFF file at a 5 arc-minute resolution that assigned a single anthropogenic biome type to each pixel in the map. The biomes are based on data from the year 2000 and are defined by three main factors: population size (urban, nonurban), land use (percent area of pasture, crops, irrigation, rice, urban land), and land cover (percent area of trees and bare earth). Each broad biome classification was composed of multiple subcategories, e.g., rice villages, irrigated villages, that were collapsed for analysis. This was done to reduce the number of correlated predictor variables. Additional methodological details can be found in the original sources referenced previously. We used ArcMap and the Spatial Analyst toolbox to create three distance rasters from this original GeoTIFF file, with each raster representing one of the three anthropogenic biomes. We accomplished this by first reclassifying the original raster to three new rasters, with each representing one of the anthropogenic biomes. Then we used the Euclidian distance function to create the distance rasters.

Data Analysis

We used MaxEnt (version 3.3.3k) (Phillips and Dudık 2008; Phillips et al. 2006) to build species distribution models. We constructed two models for each species, one using climate variables only and a second using climate and anthropogenic variables. MaxEnt has become the most commonly used algorithm for species distribution modeling when only known occurrence data are used (as opposed to using both known occurrences and absences). Although methods that do not incorporate known absences have some limitations (Yackulic et al. 2013), a comparison of species distribution methods showed that MaxEnt performs as well or better than other models (Elith et al. 2006). In addition, this approach does not require assumptions about the processes driving the ecological patterns nor does it make assumptions about the forms of the relationships between variables (Frank 2009; Harte et al. 2008).

For each model, we set 75% of the species’ known localities as training data and 25% of the localities as test data. We used three different regularization multipliers (values of 1, 2, and 3) to examine the possible effects of model overfitting (Radosavljevic and Anderson 2013). The regularization multipliers had a minimal effect on the model outcomes, so we only present the results of the models using a value of 1. In addition, we used a fourfold cross-validation procedure that randomly splits the occurrence data into equal sized groups (Blair et al. 2013; Peterson et al. 2011). Using this method is important, especially when small datasets are present, because it uses all data for model validation. The fourfold data partitioning produces four models per dataset (climate only and climate + anthropogenic variables) per species. Other model options were set to the recommendations presented by the MaxEnt authors (Phillips et al. 2006; Phillips and Dudık 2008).

We judged model performance using two criteria. The first is the area under the curve (AUC) values of the receiver operating curve plots. An AUC value of 0.5 indicates that the model is no better than random at predicting the presence of a species at a locale (because the null expectation is based on a 50% absence and 50% presence probability). AUC values >0.5 indicate improved model performance, with values of 1.0 indicating a model with perfect predictive ability. Following Hosmer and Lemeshow (2000), we considered AUC values of 0.7–0.8 as an acceptable prediction, 0.8–0.9 as excellent, and >0.9 as outstanding. An AUC value is associated with each fold. Therefore, we present the mean test AUC for each dataset of each species, as well as the standard deviation for the models. Second, we used a binomial test of omission under a minimum training presence threshold to calculate the statistical significance of each model’s prediction (Anderson et al. 2002). AUC values can be affected by the niche space occupied by a species. Species occupying relatively narrow niches as defined by the predictor variables usually have higher AUC values compared to species occupying broad niches.

We used two approaches to evaluate the effect of anthropogenically modified habitats on distributions of Eulemur. First, we calculated the percent contribution of each predictor variable for each model. If anthropogenic variables are important predictors of species distributions, then they should show relatively high values compared with climate variables. The percent contribution values we present are the means across the fourfold runs for each model. Second, for each model, we calculated the predicted species range size based on two presence probability cutoff values, 75% and 70%. We used two threshold criteria because there is no single method that best provides the correct threshold value and the specific goal of a study is an important consideration for setting a particular threshold (Jiménez-Valverde and Lobo 2007; Liu et al. 2005). In addition, we felt it was important to use both 75% and 70% values because many pixels exhibited probabilities between these two values. We quantified the number of pixels displaying these presence probabilities and multiplied this value by 21.62 km2, which is the estimated area of each map pixel corresponding to our raster resolution. For each species, we compared the predicted range size of the climate only models vs. the climate + anthropogenic variable models.

Results

We found that the climate only and climate plus anthropogenic factors models performed well for most species. For the climate models, mean test AUC values ranged from 0.787 for Eulemur fulvus to 0.994 for E. flavifrons (Table I), with a mean value of 0.930 across all species. Binomial tests for all of the fourfolds were significant for 10 of the 11 species. One species, E. cinereiceps, exhibited significant results for three of the fourfolds.
Table I

MaxEnt model results using climatic and anthropogenic variables to predict the distribution of Eulemur species

Species

Na

Dataset

Mean Test AUC

Test AUC SD

Omission rate—fold 1b

Omission rate—fold 2b

Omission rate—fold 3b

Omission rate—fold 4b

E. albifrons

28

Climate

0.936

0.019

0.143, P = 0.0005

0, P < 0.0001

0.143, P = 0.0004

0, P < 0.0001

E. cinereiceps

12

Climate

0.899

0.038

0.667, P = 0.287

0, P = .0038

0, P = 0.0031

1, P = 1.000

E. collaris

27

Climate

0.948

0.022

0, P < 0.0001

0.143, P = 0.0001

0.286, P = 0.0026

0, P < 0.0001

E. coronatus

15

Climate

0.975

0.012

0.500, P = 0.0131

0.250, P = 0.0004

0, P < 0.0001

0, P = 0.0001

E. flavifrons

13

Climate

0.994

0.002

0, P < 0.0001

0, P < 0.0001

0, P < 0.0001

0.333, P = 0.0003

E. fulvus

61

Climate

0.787

0.016

0, P = 0.0002

0.067, P = 0.0010

0, P = 0.0010

0.133, P = 0.0178

E. macaco (sensu stricto)

22

Climate

0.974

0.008

0.333, P < 0.0001

0, P < 0.0001

0.200, P < 0.0001

0, P < 0.0001

E. rubriventer

40

Climate

0.926

0.016

0, P < 0.0001

0, P < 0.0001

0, P < 0.0001

0.300, P = 0.0004

E. rufifrons

82

Climate

0.895

0.005

0, P < 0.0001

0, P < 0.0001

0, P < 0.0001

0, P < 0.0001

E. rufus (sensu stricto)

30

Climate

0.923

0.009

0, P < 0.0001

0.125, P < 0.0001

0, P < 0.0001

0.143, P < 0.0001

E. sanfordi

11

Climate

0.970

0.009

0, P = 0.0001

0, P = 0.0002

0.333, P = 0.0051

0, P = 0.0024

E. albifrons

28

Climate + Anthro

0.935

0.032

0.429, P = .0004

0, P < 0.0001

0, P < 0.0001

0.167, P = 0.0004

E. cinereiceps

12

Climate + Anthro

0.930

0.024

0, P = 0.0016

0, P = 0.0033

0, P = 0.0018

0.333, P = 0.0542

E. collaris

27

Climate + Anthro

0.981

0.008

0, P < 0.0001

0.167, P < 0.0001

0, P < 0.0001

0, P < 0.0001

E. coronatus

15

Climate + Anthro

0.983

0.005

0.250, P = 0.0001

0.500, P = 0.0032

0, P < 0.0001

0, P < 0.0001

E. flavifrons

13

Climate + Anthro

0.997

0.002

0, P < 0.0001

0.667, P = 0.0149

0, P < 0.0001

0.333, P = 0.0001

E. fulvus

61

Climate + Anthro

0.855

0.01

0.067, P < 0.0001

0, P < 0.0001

0.133, P = 0.0001

0, P < 0.0001

E. macaco (sensu stricto)

22

Climate + Anthro

0.989

0.003

0.200, P < 0.0001

0, P < 0.0001

0, P < 0.0001

0, P < 0.0001

E. rubriventer

40

Climate + Anthro

0.948

0.009

0, P < 0.0001

0.100, P < 0.0001

0.100, P < 0.0001

0, P < 0.0001

E. rufifrons

82

Climate + Anthro

0.886

0.020

0, P < 0.0001

0, P < 0.0001

0, P < 0.0001

0.150, P < 0.0001

E. rufus (sensu stricto)

30

Climate + Anthro

0.943

0.019

0, P < 0.0001

0, P < 0.0001

0, P < 0.0001

0.286, P = 0.0003

E. sanfordi

11

Climate + Anthro

0.979

0.006

0.333, P = 0.0032

0, P < 0.0001

0.333, P = 0.0029

0.500, P = 0.0635

aTotal number of localities used to build model.

bBinomial omission test under a minimum training presence threshold.

The mean test AUC values were expectedly higher (due to the additional predictor variables) for the models that included both climatic and anthropogenic variables and ranged from a minimum of 0.855 for Eulemur fulvus to a maximum of 0.997 for E. flavifrons, with an average value of 0.948 across all species. Nine of 11 species exhibited statistically significant binomial tests for all of the fourfolds. Two species, E. cinereiceps and E. sanfordi, exhibited significant results for three of the four folds.

Different climate variables best predicted the distribution of Eulemur species based on the percent contribution values for the climate-only models (Table II). The precipitation of the driest quarter was the most important predictor of E. albifrons, E. collaris, and E. rufus distributions. Temperature seasonality was the best predictor of E. macaco. Another measure of temperature variation, temperature annual range, was the most important climate variable explaining the range of E. ciniercips, E. coronatus, E. fulvus, E. rubriventer, and E. sanfordi. Finally, isothermality best explained the modeled distribution of two species, E. rufifrons and E. flavifrons.
Table II

Variable percent contribution values associated with MaxEnt models

Species

Precipitation of driest quarter

Temperature seasonality

Isothermality

Min temperature of coldest month

Precipitation of coldest quarter

Temperature annual range

Distance to dense settlement

Distance to villages

Distance to cropland

E. albifronsa

62

26.2

6.5

0.4

4.9

0

   

E. albifronsb

44.9

8.8

5.9

0.1

3.7

0.1

9.2

20.7

6.7

E. cinereicepsa

0.9

43.8

4.6

0

0.4

50.3

   

E. cinereicepsb

0

31.6

3.7

1

0

62.7

0.9

0

0.1

E. collarisa

50.7

2.5

5.5

0.4

17.9

23

   

E. collarisb

22.8

0.2

0.6

3.2

0

0.2

41.6

30.5

0.8

E. coronatusa

0

2.2

7.4

0

0

90.4

   

E. coronatusb

0

21

1

0

0.2

48.5

0.3

0

29

E. flavifronsa

16

10

73.5

0

0.5

0

   

E. flavifronsb

7.4

1.5

9

1.3

10.2

0

70.5

0

0

E. fulvusa

8.3

5.7

11.1

8.5

23.1

43.3

   

E. fulvusb

1.3

1

0.9

1.7

7.3

16.7

27.2

8.8

35.2

E. macacoa

12.1

63.7

0.4

0.3

23.2

0.4

   

E. macacob

0.3

15.3

0

0

5.2

0

79.2

0

0

E. rubriventera

30.2

0.4

0.1

30.1

1.5

37.8

   

E. rubriventerb

27.6

0

0.8

25.3

3.4

17.3

17.8

4.4

3.4

E. rufifronsa

9.7

17.8

22.7

21.7

20.2

8

   

E. rufifronsb

16.3

12.9

20.5

19.3

19

3.6

0.5

4.8

3.1

E. rufusa

59

8.2

2.9

25.7

3.4

0.9

   

E. rufusb

37.3

18.9

3.2

13.7

0.5

0

22.1

0.6

3.7

E. sanfordia

0

1.2

17.6

0

0

81.2

   

E. sanfordib

0

35.7

13.7

0

0

32.2

0

0.2

18.3

Values are averaged across replicates for each species.

aModel using climate variables only.

bModel using climate and anthropogenic variables.

We found interesting results when anthropogenic variables were combined with climatic factors in the species distribution models (Table II). The sum of the contribution values for the three anthropogenic variables was greater than 70% for E. collaris, E. flavifrons, E. fulvus, and E. macaco. In addition, E. albifrons, E. coronatus, E. rubriventer, and E. rufus exhibited summed values >25%. The remaining species exhibited values <20%. The importance of the specific anthropogenic variables also differed. The across-species mean contribution value for the distance to dense settlement variable was 24.5%. In contrast, the same measurement was 9.1% for distance to cropland and 6.4% for distance to villages. Distance to dense settlement was the most important anthropogenic predictor for six species, whereas distance to croplands was the best anthropogenic predictor for three species, and distance to villages was the best predictor for two species.

Compared with the models using climate variables only, most Eulemur species exhibited smaller predicted distributions when anthropogenic factors were considered in combination with climate (Table III and Figs. 1, 2, 3, 4, 5, and 6). This was especially true when we used a 70% probability as the criterion for accepting a predicted species presence. The average predicted distribution across Eulemur species based on climate alone was 7994 km2 compared to 5773 km2 when anthropogenic effects were include in the model (a range reduction of 27.8%). Ten of the 11 species showed smaller total range sizes for the climate + anthropogenic models compared with using climate variables alone (E. cinereiceps was the only exception). The amount of range reduction varied from 4.1% for E. albifrons to 58.8% for E. collaris. In addition, the mean predicted geographic ranges of species decreased when accounting for anthropogenic effects and using 75% as the accepted probability level of a species’ presence. Climate-only models produced a mean predicted species distribution of 3340 km2 compared to 2858 km2 for climate + anthropogenic models. Yet, we obtained more mixed results when examining individual species. Five of the 10 species exhibited slightly greater predicted ranges when anthropogenic effects were included in the model, one species exhibited a noticeably greater increase (E. albifrons), and five species exhibited decreased ranges. Only one of the 11 species we examined, E. ciniercips, showed a consistent increase in predicted range size when anthropogenic effects were included in the distribution model. This increase was quite modest, at just over 16%.
Table III

Predicted geographic range of Eulemur species based on a climate model vs. climate and anthropogenic impact model

Species

Climatea

Climate + Anthroa

% change in range size from anthropogenic impactsa

Climateb

Climate + Anthrob

% change in range size from anthropogenic impactsb

E. albifrons

6876

6595

–4.1

1514

4065

168.5

E. cinereiceps

9990

11,568

15.8

4973

5816

17.0

E. collaris

8714

3589

–58.8

4000

2184

–45.4

E. coronatus

4562

3957

–13.3

1297

1406

8.4

E. flavifrons

1492

1124

–24.7

1254

497

–60.4

E. fulvus

12,995

8822

–32.1

6249

3979

–36.3

E. macaco (sensu stricto)

3049

2378

–22.0

151

195

29.1

E. rubriventer

7525

4692

–37.6

2227

2616

17.5

E. rufifrons

13,968

9276

–33.6

6703

4779

–28.7

E. rufus (sensu stricto)

7524

6141

–18.4

2227

2573

15.5

E. sanfordi

11,244

5362

–52.3

6140

3330

–45.8

Predicted geographic range values are in square kilometers. Species in bold are most negatively affected by anthropogenic impacts. Negative percentages indicate that anthropogenic impacts reduce species range size. Range size calculations are based on each pixel representing 21.6225 km2.

aBased on 70% probability that species is present in each pixel.

bBased on 75% probability that species is present in each pixel.

Fig. 6

Species distribution models produced for Eulemur sanfordi(A, B) using climate variables (A) and climate + anthropogenic factors (B). Warmer colors indicate a greater certainty of species being present. Cooler colors indicate a greater certainty of species being absent. Green pixels indicate the areas of greatest uncertainty.

Discussion

Using a species distribution modeling approach, we found that both climate-only and climate and anthropogenic models are excellent or outstanding at predicting distributions of Eulemur. Importantly, we found that the geographic ranges of several Eulemur species are influenced by anthropogenic factors. In particular, five species (E. collaris, E. fulvus, E. flavifrons, E. rufifrons, and E. sanfordi) exhibited substantially reduced distributions once anthropogenic impacts are accounted for in the model. In addition, we found that the distance to dense settlements contributed the most to explaining distributions of Eulemur, followed by distance to cropland, and then distance to villages. Interestingly, there was evidence to support the idea that one species, E. cinereiceps, exhibited an increased modeled distribution once anthropogenic effects were quantified. A detailed visual examination of its predicted distribution (Fig. 1C), however, yields only slight differences between the two models.

Our findings broadly support prior research examining human impacts on primate distributions and extinction risk. On a global scale, Harcourt and Parks (2003) found that human population density was higher within the geographic range of threatened primate taxa compared with low-risk species. Junker et al. (2012) showed that the suitable environmental conditions of African great apes from the 1990s to the 2000s were negatively impacted by human-modified landscapes and the intensity of these impacts varied by species. In addition, Brown and Yoder (2015) found that lemur ranges are likely to shift in accordance with future climate change. Yet, similar to our results, the degrees to which lemur ranges shift vary across species. About 60% of the 57 species they examined will experience range contractions, yet other taxa will experience range stability or range increases. These differential impacts are independent of phylogeny, with closely related taxa, e.g., Eulemur spp., experiencing very different responses to climate change. Although anthropogenic impacts are usually detrimental to primates, there is some evidence suggesting positive effects are possible, at least on a limited basis. For lemurs, Overdorff (1991) noted that some Eulemur species will use fruit tree groves when forest food resources are scare.

Several mechanisms may be responsible for the negative impact of anthropogenically modified areas on distributions of Eulemur. At a basic level, most types of anthropogenically modified areas are associated with reduced habitat availability. Eulemur are forest-dwelling primates, and as such are negatively impacted by forest modification, degradation, and loss. Dense settlements, i.e., urban areas, in particular are likely to be associated with the complete elimination of natural habitats, thereby resulting in the extirpation of Eulemur species (and other lemurs). Even in areas with less extreme habitat destruction, forest fragmentation is widespread throughout Madagascar (Harper et al. 2007). Most of this widespread habitat alteration is due to large-scale, and often illegal, industrial logging and mining (Gore et al. 2013; Horning 2012).

There is increasing evidence showing that Eulemur species are negatively impacted by habitat disturbance. Recent research examining hormone levels supports the idea that living in degraded forest impacts the physiology of some Eulemur species. Balestri et al. (2014) found that E. collaris in degraded forest exhibited higher fecal glucocorticoid metabolite levels compared with individuals living in nearby intact forest. In addition, a study focused on E. rubriventer (Tecot 2013) found that individuals in disturbed habitat did not respond behaviorally or physiologically to seasonal changes in food availability and climate, in contrast with those in undisturbed forest. Groups in disturbed forest reproduced out of season and had much higher infant mortality rates as well, which could lead to a long-term population decline (Tecot 2008, 2010).

The anthropogenic variables we examined in our analysis may also be related to hunting intensity. Being in close proximity to anthropogenic areas, especially villages and croplands, may increase the likelihood of being hunted. Hunting of lemurs has been increasing in recent years, possibly due to the nutritional needs of local people (Golden et al. 2011; Jenkins et al. 2011). Eulemur species are known to be hunted throughout the island (García and Goodman 2003; Golden 2009; Johnson and Overdorff 1999). Additional research is necessary to determine how increased hunting intensity may impact Eulemur density and distribution.

Another anthropogenically mediated mechanism that may negatively impact populations of Eulemur is pathogens. Several studies have shown that pathogen transmission between humans and wild nonhuman primates occurs in many areas (Köndgen et al. 2008; Nunn 2012). In particular, lemurs in anthropogenically disturbed areas of Ranomafana National Park exhibited higher prevalence rates of gastrointestinal helminths, protozoa (Rasambainarivo et al. 2013; Wright et al. 2009), and disease-causing enterobacteria (Bublitz et al. 2014), but the effects on Eulemur species have not been studied. It is still unknown whether direct contact with humans or human-associated animals, e.g., cattle, rodents, is the source of these pathogens.

Future work should focus on explicitly examining the possible biological traits that allow Eulemur cinereiceps to be less affected by anthropogenic impacts (at least in terms of their modeled geographic distribution), while most other species show clear negative impacts. Eulemur are known to be ecologically flexible primates ( Donati et al. 2007; Ossi and Kamilar 2006), including showing a relatively high degree of variation in activity pattern, diet, activity budget, and social organization. Body size is often an important predictor of rarity and extinction risk, though Eulemur species do not vary a great deal in this trait (Kamilar et al. 2012). Previous work has shown that some leaf eating primates are more resistant to habitat disturbance (Johns and Skorupa 1987; Kamilar and Paciulli 2008; Oates et al. 1990) compared with frugivorous species. Therefore, investigating dietary variation across species, as well as the degree of seasonal variation in diet, may be a fruitful avenue of exploration. Interestingly, a recent study of population size and genetic diversity of E. cinereiceps lends support to our results. Brenneman et al. (2012) unexpectedly found evidence of gene flow across intact and fragmented forests, even those separated by anthropogenic grasslands. In addition, they did not find a statistically significant decline in the genetic diversity of populations living in fragmented habitats. These results may indicate that E. cinereiceps is more resilient to anthropogenic impacts compared with other Eulemur species. An additional factor to consider is the timing of anthropogenic impacts. Populations of Eulemur that have only recently been exposed to anthropogenic factors may not currently show reductions in population size, genetic diversity, or geographic range size because of time lag effects. Our anthropogenic variables were based on data from the year 2000, whereas the localities of Eulemur are reflecting current or recently known locations. Therefore, this discrepancy in temporal sampling may introduce additional error into our models and underestimate the impact of anthropogenic factors on some species.

Additional factors that may influence distributions of Eulemur should also be considered in the future. Biotic interactions, either among congeners, other lemur taxa, and/or other potential competitors could influence biogeography of Eulemur (Ganzhorn 1997; Kamilar and Ledogar 2011) by reducing their geographic ranges. Also, geographic barriers, such as rivers, may limit species ranges through reduced dispersal (Ayres and Clutton-Brock 1992; Harcourt and Wood 2012). The modeled distribution of species may change if these factors are explicitly quantified.

We hope that our approach can be useful for setting conservation priorities in the face of growing anthropogenic impacts on Madagascar. Conservation personnel and funding are limited and our findings suggest that some Eulemur are differentially affected by human-modified habitats. Conserving Eulemur species, as well as other lemurs (especially frugivorous species), serves to not only preserve primate diversity on Madagascar, but also acts as a mechanism to maintain the extreme biodiversity on the island (Wright et al. 2011). The importance of lemurs for maintaining plant diversity was nicely demonstrated for E. rubriventer, which was shown to be an important seed disperser of a long-lived Malagasy rainforest tree species, Cryptocarya crassifolia (Razafindratsima and Dunham 2015). In sum, the results of our study should be combined with other information (Schwitzer et al. 2013) to create more holistic conservation plans.

Notes

Acknowledgments

We thank Steig Johnson and Guiseppe Donati for inviting us to contribute to this special issue. Our article was improved by helpful comments provided by four anonymous reviewers and Steig Johnson.

Supplementary material

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ESM 1

(JPEG 4384 kb)

10764_2015_9875_MOESM1_ESM.xls (64 kb)
ESM 2(XLS 64 kb)

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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Department of AnthropologyUniversity of MassachusettsAmherstUSA
  2. 2.Graduate Program in Organismic and Evolutionary BiologyUniversity of MassachusettsAmherstUSA
  3. 3.School of Human Evolution and Social ChangeArizona State UniversityTempeUSA
  4. 4.School of AnthropologyUniversity of ArizonaTucsonUSA

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