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Considering the Influence of Nonadaptive Evolution on Primate Color Vision

  • Rachel L. Jacobs ,

    Affiliations Department of Anthropology, Center for the Advanced Study of Human Paleobiology, The George Washington University, Washington, District of Columbia, United States of America, Interdepartmental Doctoral Program in Anthropological Sciences, Stony Brook University, Stony Brook, New York, United States of America, Centre ValBio Research Station, Ranomafana, Fianarantsoa, Madagascar

  • Brenda J. Bradley

    Affiliation Department of Anthropology, Center for the Advanced Study of Human Paleobiology, The George Washington University, Washington, District of Columbia, United States of America


Color vision in primates is variable across species, and it represents a rare trait in which the genetic mechanisms underlying phenotypic variation are fairly well-understood. Research on primate color vision has largely focused on adaptive explanations for observed variation, but it remains unclear why some species have trichromatic or polymorphic color vision while others are red-green color blind. Lemurs, in particular, are highly variable. While some species are polymorphic, many closely-related species are strictly dichromatic. We provide the first characterization of color vision in a wild population of red-bellied lemurs (Eulemur rubriventer, Ranomafana National Park, Madagascar) with a sample size (87 individuals; NX chromosomes = 134) large enough to detect even rare variants (0.95 probability of detection at ≥ 3% frequency). By sequencing exon 5 of the X-linked opsin gene we identified opsin spectral sensitivity based on known diagnostic sites and found this population to be dichromatic and monomorphic for a long wavelength allele. Apparent fixation of this long allele is in contrast to previously published accounts of Eulemur species, which exhibit either polymorphic color vision or only the medium wavelength opsin. This unexpected result may represent loss of color vision variation, which could occur through selective processes and/or genetic drift (e.g., genetic bottleneck). To indirectly assess the latter scenario, we genotyped 55 adult red-bellied lemurs at seven variable microsatellite loci and used heterozygosity excess and M-ratio tests to assess if this population may have experienced a recent genetic bottleneck. Results of heterozygosity excess but not M-ratio tests suggest a bottleneck might have occurred in this red-bellied lemur population. Therefore, while selection may also play a role, the unique color vision observed in this population might have been influenced by a recent genetic bottleneck. These results emphasize the need to consider adaptive and nonadaptive mechanisms of color vision evolution in primates.


Primate color vision is among the most oft-cited examples of adaptive molecular evolution [1]. It represents one of few primate traits in which variation in individual phenotypes (i.e., the ability to make or not make particular color discriminations) can be tied to small changes in single genes [24]. For example, shifts in the sensitivities of M/L (medium/long wavelength) cone photopigments (i.e., M/L opsins) result from just a few amino acid changes on the X-linked opsin-coding gene (M/L opsin gene) [24]. Accordingly, differences in color vision capacity have occurred either through gene duplication and subsequent differentiation or allelic variation of a single M/L opsin gene [2, 3, 5, 6]. The former mechanism is documented in Old World monkeys, apes, and humans, as well New World howling monkeys, and results in virtually all individuals having full trichromatic color vision [2, 5, 6]. Most other New World monkeys, on the other hand, have one M/L opsin gene with two or more alleles resulting in polymorphic color vision; heterozygous females are trichromatic, while hemizygous males and homozygous females are red-green color blind (i.e., dichromatic) ([3, 7, 8], reviews in [1, 9]). A monomorphic M/L opsin gene and dichromatic color vision appears to characterize tarsiers and some lemurs [10, 11].

Many theoretical studies suggest that these differences in color vision capacity are likely to influence specific fitness-related behaviors (e.g., foraging, predator detection) and ultimately account for observed color vision variation in primates [1220]. For example, trichromatic color vision is thought to be advantageous for foraging on reddish food items, such as ripe fruit and/or young leaves, among a background of green foliage [1319]. Red-green color blindness, on the other hand, may offer an advantage in detecting camouflaged objects, including some food items (e.g., insects, green fruit) and predators (e.g., snakes) [15, 2124]. Such hypotheses are attractive, particularly because they are intuitive and also testable given that many primate species have polymorphic color vision. Accordingly, much research has been aimed at identifying how differences in color vision might be adaptive under the assumption that observed variation is shaped by selection [23, 2531]. That said, there is mixed evidence for adaptive advantages of different color vision phenotypes in wild populations [23, 2527, 29], with few studies testing the null hypothesis of nonadaptive evolution shaping opsin variation in primates [32].

Lemurs represent an interesting lineage to examine evolutionary mechanisms (both adaptive and nonadaptive) underlying differences in color vision. Until relatively recently, it was thought that all lemurs were either completely color blind or strictly dichromatic with a single, non-variable M/L opsin gene [33]. It is now known that some species exhibit allelic variation of the M/L opsin gene (M and L alleles) and have polymorphic color vision [11, 3437]. In light of such studies, it appears that lemurs have more variation in color vision capacity compared to other primate lineages; monochromacy, dichromacy, and polymorphic color vision have each been documented in multiple species [11, 3640]. At the same time, lemurs vary widely in a number of ecological characteristics, including activity pattern (nocturnality, diurnality, and cathemerality–day-and night-active), diet (e.g., gummivory, folivory, frugivory), and habitat (e.g., spiny forest, deciduous dry forest, rainforest) [41]. Each of these features has been hypothesized to influence color vision evolution [13, 3943], and such differences are particularly relevant because color vision has been shown to vary among closely related taxa [11, 36, 37].

Within the genus Eulemur, for example, polymorphic trichromacy has been identified in captive Eulemur flavifrons [37], but other species for which published data are available (E. collaris, E. mongoz, and E. fulvus) appear to have dichromatic color vision, with only a single M opsin variant [11, 36, 43]. Species within this genus exhibit gross similarities in some ecological characteristics, being generally described as cathemeral and predominantly frugivorous [41]. However, the habitats in which Eulemur species live range from dry deciduous forest to rainforest [41]. Species also differ markedly in their pelage coloration and patterns, and most exhibit some degree of sexual dichromatism in coat color [41]. Eulemur therefore represents an ideal taxon for identifying selective pressures potentially shaping variation in color vision. Indeed, such differences in ecology, including habitat as well as potential subtle variation in activity patterns and diets, have been hypothesized to account for color vision variation among these closely related species [43].

The current published characterizations of color vision in Eulemur species are based on samples from captive individuals and a single wild population [11, 36, 37, 43] with sample sizes (NX chromosomes = 4–36) below that needed (NX chromosomes ≥ 59) to detect a low frequency allele (i.e., 0.95 probability of detecting an allele at 5% frequency based on cumulative binomial probabilities). That is, differentiating between an all-color blind population and a polymorphic population with variants at low frequencies requires greater sampling. Furthermore, in the case of captive populations (E. flavifrons, E. collaris, and E. mongoz) [11, 36, 37], it is unknown if documented M/L opsin allele frequencies are an accurate representation of those in wild populations or potentially result from founder effects.

The influence of genetic drift may also be particularly relevant to understanding genetic variation in natural lemur populations. Many studies have identified genetic signatures of historical, large-scale (in some cases orders of magnitude) population collapse in lemur species/populations across Madagascar [4448]. Such results suggest that observed M/L opsin allele frequencies in lemurs may have been influenced by recent genetic bottlenecks and drift.

Thus, in order to avoid falsely inferring adaptive evolution, it is important to first explore the potential influence of nonadaptive mechanisms on present genetic variation. In this study, we characterize color vision in a wild population of red-bellied lemurs (Eulemur rubriventer) for which we also have detailed data on individual pelage/facial pigment variation (Fig 1) and foraging behavior. We examined coding variation in color vision (i.e., opsin genes) using an adequate sample to detect rare alleles (0.95 probability of detection at ≥ 3% frequency). We also characterized neutral genetic polymorphisms (i.e., microsatellite genotypes), which provide a baseline for understanding if the present genetic variation in this population may have been influenced by a recent genetic bottleneck.

Fig 1. Frontal facial photographs of red-bellied lemurs in Ranomafana National Park.

(A) Adult females. (B) Adult males. Photographs illustrate individual variation in facial pelage patterns used to identify individuals during data collection. Photo credits: Falinomenjanahary J., Lahitsara J.P, RLJ, and Velontsara J.B.

Materials and Methods

All methods were in compliance with and approved by Stony Brook University’s IACUC committee (IACUC #: 2010/1803, 2011/1895) and the government of Madagascar (permit #: 284/10, 157/11, 204/12, 056/13).

Study subjects and site

Red-bellied lemurs live in small, cohesive groups, ranging in size from 2–6 individuals ([49, 50], this study). Adults are usually pair-living, with groups generally composed of one adult male, one adult female, and immature individuals [51]. This study was conducted on a single population of E. rubriventer in Ranomafana National Park (RNP), which is an area of 41,000 ha of montane rainforest in southeastern Madagascar (E47°18'–47°37', S21°02'–21°25') [52]. Data were collected intermittently between January 2011 and May 2013 at five site localities within the park (Talatakely, Vatoharanana, Valohoaka, Sakaroa, and Sahamalaotra), and one site located just outside the park (Ambatolahy dimy) (Fig 2). All sites are within 8 km of each other and assumed to be in migratory contact. This population of red-bellied lemurs has been the subject of previous research projects [5355] and is exposed to ecotourism activities to varying degrees, making many individuals habituated to observer presence.

Fig 2. Study sites within and around RNP.

Reprinted with modifications from [56] under a CC BY license, with permission from Andrea Baden, original copyright 2011.

Fecal sample collection

Fresh fecal samples for genetic analyses were collected by RLJ and field assistants from 157 individual red-bellied lemurs opportunistically as part of behavioral data collection and/or survey of the red-bellied lemur population in RNP (S1 Table). Full-day group follows (time of group location through sunset) were conducted on red-bellied lemur groups that were located by searching within known home ranges or opportunistically. During surveys, groups were visited monthly to collect demographic data. More detailed data on feeding behavior were collected on nine groups as part of related studies on color vision in this taxon. During group follows, fecal samples were collected from individual lemurs with the aim to collect three independent fecal samples/individual. All samples were collected from the ground immediately following defecation. Each sample (~ 5 grams of wet weight) was placed directly into a 50 mL plastic centrifuge tube pre-filled with 30 mL silica gel beads (for desiccation) using latex gloves and the untouched end of a freshly broken twig [57]. Tubes were labeled and sealed with parafilm and stored at ambient temperature in the field and later at +4°C in the lab.

DNA extraction

Genomic DNA was extracted from dry fecal samples using the QIAamp® DNA Stool Mini Kit (Qiagen) following the manufacturer’s protocol plus an initial 48 hour lyses in ASL buffer at room temperature. Extractions were automated using a QIAcube® (Qiagen). Extraction protocols and all down-stream procedures always included negative controls.

DNA quantitation

DNA concentrations were quantified in duplicate for all samples using a Qubit® 2.0 Fluorometer (Invitrogen™) and the Qubit® dsDNA HS Assay Kit. As this method quantifies total genomic DNA (including plant and microbial DNA), we also quantified samples using a real-time quantitative polymerase chain reaction (qPCR) assay targeting a conserved region of the c-myc proto-oncogene in primates (i.e., not amplifying non-primate DNA in samples, such as bacterial DNA) following [58]. Reactions were carried out on a Rotor-Gene Q platform using SYBR Green RT-PCR Master Mix (Qiagen). DNA quantity scores were an average of two replicates (as in [58]), and results across replicates were generally consistent.


Each sample was genetically sex-typed by amplifying segments of the tetratricopeptide repeat protein gene on the Y chromosome (UTY), and the X-chromosomal homolog (UTX) using a multiplex (triple primer) PCR following [59]. If a sample yielded both X and Y fragments, the individual was typed male, and if a sample yielded only the X fragment, the individual was typed female. Sex-typing was based on multiple independent reactions.

M/L opsin genotyping

The opsin gene complement of individual red-bellied lemurs was determined by amplifying and sequencing a ~200 bp fragment of exon 5 of the X-linked M/L opsin gene. In platyrrhines, functional variation in M/L opsins results from amino acid site changes in both exons 3 and 5 [4]. This study focuses on the latter because functional variation in most lemur species is tied to site 285 in exon 5 [11]. Fragments were amplified using PCR with our forward primer (5’–GTAGCAAAGCAGCAGAAAGA– 3’) and a previously published reverse primer (5’–CTGCCGGTTCATAAAGACGTAGATAAT– 3’ [34]). PCR reactions were performed in 25 μl total volume and contained the Qiagen HotStarTaq Master Mix, 1.6 μM of bovine serum albumin, 0.4 μM each of forward and reverse primers, and included 3–5 μl of total template DNA. Cycling conditions were 95°C for 15 minutes and 36 cycles of 94°C for 30 seconds, 57°C for 40 seconds, and 72°C for 1 minute, with a final extension step of 72°C for 7 minutes. PCR fragments were visualized on 2% agarose gels using GelRed, and were sequenced (Sanger) in both directions using an Applied Biosystems 3730xl DNA Genetic Analyzer at the Yale DNA Analysis Facility. Sequence traces were scored by eye and color vision status was based on genotypes at amino acid site 285 (codon translations: GCC = alanine; ACC = threonine). All individuals were replicated 1–3 times using independent PCR reactions and 1–2 independent fecal extractions.

Microsatellite genotyping

Samples were amplified at seven variable, unlinked microsatellite loci using previously published primers (S2 Table; [60]). Loci were selected for short fragment lengths, simple repeat motifs, and high allelic variability based on [60]. PCR conditions were carried out in 12.5 μl total volume containing the Qiagen HotStarTaq Master Mix, 3.2 μM bovine serum albumin, 0.8 μM each of forward (fluorescently labeled) and reverse primers, and 2 μl of total template DNA. DNA template volume was adjusted based on DNA quantification (>25 pg) to minimize errors associated with allelic dropout [58]. Cycling conditions were 95°C for 15 minutes and 37 cycles of 95°C for 30 seconds, a locus-specific annealing temperature (S2 Table) for 30 seconds, and 72°C for 30 seconds, with a final extension step of 72°C for 10 minutes.

Fragment length analyses were carried out in the DNA Analysis Facility at Yale University via capillary electrophoresis with an ABI 3730xl 96-Capillary Genetic Analyzer. Genotypes were binned and scored by eye via GeneMapper® (Applied Biosystems) and GeneMarker® (SoftGenetics). Homozygous genotypes were confirmed with a minimum of four and up to seven independent replications, based on results of DNA quantitation [58]. Heterozygous individuals were confirmed when each allele was scored at least twice based on two or more independent PCR reactions [58, 61].

Microsatellite genotypes were screened for errors (i.e., scoring errors, allelic dropout, and null alleles) prior to data analysis using the software MICRO-CHECKER [62]. Genepop version 4.2 was used to test for linkage disequilibrium among all combinations of microsatellite loci using the log-likelihood ratio statistic and evaluated with 10,000 permutations [63, 64]. Summary statistics for each locus (e.g., the number of alleles—k, number of individuals typed, observed and expected heterozygosity, and polymorphic information criterion), as well as goodness-of-fit tests for Hardy-Weinberg equilibrium were calculated in CERVUS 3.0 [65, 66]. Allelic richness (the number of alleles per locus independent of sample size) for each locus was calculated using FSTAT version

Genetic bottleneck analyses

Heterozygosity excess.

The program BOTTLENECK was used to test for heterozygosity excess as a potential signal for a recent genetic bottleneck [6769]. The program computes expected heterozygosity (Heq) at mutation-drift equilibrium (based on allele number and sample size) for each locus under three mutation models: infinite allele model (IAM), stepwise mutation model (SMM), and the two-phase model (TPM) [68]. The program compares Heq to Hardy-Weinberg heterozygosity (He) with the expectation that in recently bottlenecked populations, there will be significant excess He compared to Heq, because allele number should be reduced faster than heterozygosity [67, 69].

BOTTLENECK performs multiple tests, but the Wilcoxon test is considered to be robust when using a small number of polymorphic loci (< 20) and most appropriate for microsatellite data [69], and therefore is used in this study. Heq is calculated under TPM, as this mutation model is also considered the most appropriate model for microsatellite loci, with IAM and SMM representing more extreme mutation models [69, 70].

Importantly, tests for heterozygosity excess have the potential to produce both type I and type II errors based in part on incorrect assumptions of mutation model parameters [71]. Specifically, TPM assumes that mutations during microsatellite evolution can result in small changes in a single repeat motif (i.e., single-step mutations, which characterize most mutations), as well as larger changes in multiple repeat motifs (i.e., multi-step mutations, which characterize fewer mutations) [70, 71]. Consequently, TPM requires knowledge (or assumptions) about the proportion and size of multi-step mutations in the microsatellite data of interest [71]. The program BOTTLENECK requires the proportion of multi-step mutations and the variance in the mean size of multi-step mutations to be specified [69], and it has been shown that type I and type II errors can result from errors in assumed values for these parameters [71, 72]. One way to help avoid such errors is to use reasonable and appropriate values for the mutation model parameters [71]. A review of 18 studies of microsatellite evolution in vertebrates suggests 0.22 and 12 are appropriate values for the proportion of multi-step mutations and variance in mean size of multi-step mutations, respectively [71]. The former value deviates from the more commonly used proportion of 0.10 [71]. Because overestimating this value increases the likelihood of a type I error in heterozygosity excess tests, 0.10 may be considered a more conservative value [71, 72]. Therefore, the Wilcoxon test was run twice under the TPM and setting the proportion of multi-step mutations to 0.22 and 0.10, respectively, with a variance of 12 for each analysis. Significance (p < 0.05) was assessed using 10,000 iterations.


A signature of a population bottleneck was also assessed using the M-ratio test implemented in the program M_P_val [73]. This test computes M, which is the ratio of k (total number of alleles) to r (range in allele size) averaged across all microsatellite loci, and compares this ratio to a simulated distribution of M values at mutation-drift equilibrium [73]. In populations that have experienced a bottleneck, the expectation is that observed M should be lower than M values at equilibrium, because rare alleles are likely to be lost in bottlenecked populations but should not be biased toward the smallest or largest allele sizes [73]. Therefore, k is expected to reduce faster than r [73].

Calculating M requires three input parameters and, similar to heterozygosity excess tests, incorrect assumptions about these parameters can produce both type I and type II errors [71]. M-ratio tests require assumptions about ps (the proportion of one-step mutations) and g (the average size of one-step mutations) [73]. Following the recommendation in [71] and similar to heterozygosity excess tests, the proportion of multi-step mutations was set to 0.22, as well as the more commonly used 0.10 (i.e., ps = 0.78 and 0.90, respectively); g was set to 3.1. The M-ratio test also requires the input parameter pre-bottleneck θ (θ = 4Neμ; Ne = effective population size; μ = mutation rate) [73]. Given that pre-bottleneck Ne is unknown as is μ, a range of values for θ (0.2–20) was tested [48, 74]. If one assumes μ = 5.0 × 10−4, which is a commonly used microsatellite mutation rate [73, 75], these values correspond to pre-bottleneck Ne values: 100–10000 individuals. Observed M is considered significant and indicative of a population bottleneck if < 5% of simulated values fall below the observed M [73].

All bottleneck analyses were run using a data set including only adult red-bellied lemurs. In addition, because sex-biases in dispersal can influence the interpretation of bottleneck analyses (e.g., mask bottleneck signatures through introducing new alleles) [74, 76], all analyses were run using adult-female-only and adult-male-only data sets. Both data sets were used because behavioral observations in red-bellied lemurs suggest that both sexes disperse [51, 77], but it is unknown if there is sex bias in dispersal distance.


Sex and M/L opsin genotyping

Sequences for exon 5 of the M/L opsin gene were obtained for 87 adult and immature red-bellied lemurs (Nfemale = 47, Nmale = 40, Nadult = 58, Nimmature = 29; NX chromosomes = 134; see S3 Table). All individuals yielded codon ACC (amino acid = threonine) at site 285. Sex genotypes were consistent with color vision genotypes (i.e., males were not heterozygous) as well as sex assignments based on field observations. Given the final sample NX chromosomes = 134, the frequency of the M allele is 0% and the L allele is 100%, indicating that all individuals are dichromats with the L opsin. This sample is more than sufficient to detect a color vision polymorphism present at a low (≥ 3%) frequency. That is, using cumulative binomial probability calculations, the probability of not detecting a rare (≥ 3%) allele given the sample size is < 0.05 (p < 0.001 for an allele at 5% frequency), which suggests the L opsin is effectively fixed in the population of red-bellied lemurs in RNP. E. rubriventer sequence data for exon 5 of the M/L opsin gene are available in FASTA format (S1 File).

Microsatellite analysis

Of 59 adult individuals used in microsatellite genotyping analyses, 55 yielded confident genotypes at a minimum of 4 microsatellite loci and comprise the final data set (Table 1). Genotypes were 91% complete for 7 microsatellite loci (range 60–100% complete) across the 55 adult red-bellied lemurs (S4 Table). MICRO-CHECKER found no evidence for scoring errors, allelic dropout, or the presence of null alleles across each of the 7 loci. Of the 21 locus combinations, no combinations showed evidence for linkage after Bonferroni’s correction (p < 0.002). Summary statistics for all loci are presented in S4 Table. Across the 7 microsatellite loci, the mean number of alleles (k) was 5.86 (range 3 to 9). Mean allelic richness was 5.66 (range 3.00 to 8.31). Mean observed heterozygosity for the population was 0.69 (range 0.52 to 0.80) and mean expected heterozygosity was 0.70 (range 0.59 to 0.81). Goodness-of-fit tests showed no significant deviations from Hardy-Weinberg equilibrium for all loci.

Table 1. Sample used in microsatellite analysis includes adult individuals that yielded confident genotypes at a minimum of 4 microsatellite loci.

Genetic bottleneck analyses

Heterozygosity excess.

Results of the Wilcoxon test for heterozygosity excess indicate that under TPM, the population of red-bellied lemurs in RNP exhibits significant excess heterozygosity compared to mutation-drift equilibrium. This was true when the proportion of multi-step mutations was set to 0.22 (p < 0.01) and the more conservative 0.10 (p < 0.05). Results were similar using an adult-female-only data set: p < 0.01 and p < 0.05 for proportions of multi-step mutations set to 0.22 and 0.10, respectively. Results approached significance using an adult-male-only data set: p = 0.055 when the proportion of multi-step mutations was set to 0.22 and 0.10.


M-ratio tests revealed that observed average M values in the population of red-bellied lemurs in RNP were high (0.94–0.97) and not significantly lower than expected under mutation-drift equilibrium (Table 2). This was the case for the combined male and female, female-only, and male-only data sets. Results also held under both scenarios for the proportion of multi-step mutations (0.22 and 0.10; ps = 0.78 and 0.90, respectively).

Table 2. Results of M-ratio tests for the population of red-bellied lemurs in RNP.

M = observed average M calculated across all loci for the combined male-female, female-only, and male-only data sets. The percentage of M values falling below observed M are given for both ps = 0.78 (proportion of multi-step mutations = 0.22) and ps = 0.90 (proportion of multi-step mutations = 0.10).


Results of this study indicate that color vision in the population of E. rubriventer in Ranomafana National Park (RNP) is unique compared to other members of the genus Eulemur for which published data are available (Fig 3) [11, 36, 37, 43]. Specifically, all sequences for exon 5 of the M/L opsin gene in this population have the amino acid threonine at site 285 (L opsin: peak spectral sensitivity ~ 558 nm). The sample size used in this study is one of the most exhaustive samples of M/L opsins in lemurs to date and suggests that the L opsin is likely fixed in this population, although we cannot exclude the possibility that the M allele is present at a very low frequency (~2% or lower). Based on current sample sizes (Fig 3), other species of Eulemur appear to be either monomorphic for the M opsin (peak spectral sensitivity ~ 543 nm) or polymorphic [11, 36, 37, 43], which raises the question as to why our study population exhibits a different pattern of color vision than other Eulemur. Given the most recent Eulemur phylogeny [78] and using the principle of parsimony, it also appears that this population (and potentially E. rubriventer as a species) may have lost a color vision polymorphism (Fig 3). We note, however, that phylogenetic relationships of Eulemur remain unresolved (see also [79]), and we cannot discount the possibility that dichromacy for the L opsin is the ancestral Eulemur condition (e.g., see [80] on the ancestral primate color vision state).

Fig 3. Phylogenetic distribution of opsin variation based on the current study (*) and published data.

Numbers represent current sample sizes (X chromosomes). Those denoted with “+” are from wild populations. All other samples are from captive individuals. References (from the top): E. flavifrons [37], E. fulvus [43], E. collaris [36], E. rubriventer (this study), E. mongoz [11]. Phylogeny from [78]. Eulemur illustrations copyright 2015 Stephen D. Nash / Conservation International / IUCN SSC Primate Specialist Group and used in figure with permission from Stephen D. Nash.

It is, however, intriguing that color vision in E. rubriventer, as well as potentially other Eulemur species, might represent loss of color vision variation. Polymorphic color vision is thought to be maintained in many primate populations, particularly of New World monkeys, either through adaptive advantages of trichromatic color vision or advantages of trichromatic and dichromatic color vision (i.e., balancing selection) [1, 9, 12]. Although empirical support for such advantages remains limited [23, 2527, 29, 32].

While not discounting the potential for selective processes, such as disruptive and/or directional selection, including a selective sweep, to account for loss of color vision variation [81], results of this study suggest that nonadaptive processes may also play a role. Specifically, heterozygosity excess tests indicate that the population of red-bellied lemurs in RNP may have experienced a genetic bottleneck. Interestingly, heterozygosity excess has also been documented in populations of E. collaris [82], a species that is presently considered to be monomorphic for the M opsin (although this is based on captive individuals [36]). Furthermore, evidence for bottlenecks has been found across a number of lemur species, which might be related to anthropogenic disturbances throughout Madagascar [4448]. Taken together, such results suggest genetic bottlenecks may be widespread among wild lemur populations. Given that the impact of genetic drift increases in small populations, a recent population crash could result in loss of allelic variation, and this can occur even in the presence of positive selection [81]. That is, loss of color vision variation could occur in bottlenecked populations under multiple scenarios including relaxed selection to maintain color vision variation, selection favoring one opsin allele over another, and even selection favoring allelic variation.

While our results suggest that a genetic bottleneck might have occurred in our study population, it is important to note that these results are not unequivocal; heterozygosity excess tests indicate a potential genetic bottleneck, but M- ratio tests do not. Increasing the number of loci might help resolve inconsistent results by increasing power [71]. Nonetheless, a similar pattern (significant heterozygosity excess coupled with high M-ratios) has been identified in other vertebrate species (e.g., ornate box turtles: [83]; northern spotted owls: [84]; Siberian tigers: [85]). The opposite pattern, in which M-ratio tests but not heterozygosity excess tests show signatures of bottlenecks, has also been observed (e.g., tiger salamanders: [86]; copperbelly water snakes: [87]; elk: [88]; bottlenose dolphins: [89]). One proposed explanation for ambiguous results suggests that different tests are better able to detect bottlenecks that vary in timing/duration and/or severity [72]. For example, heterozygosity excess tests may be better at detecting recent or less severe bottlenecks, while the M-ratio test may be better able to detect bottlenecks in populations that have had some recovery time, or in those that have experienced longer-term bottlenecks (i.e., multiple generations) [72]. Accordingly, mixed results are not necessarily inconsistent with a genetic bottleneck, and, in fact, a more recent bottleneck would accord well with the recent large-scale forest destruction that has occurred across the eastern rainforests, including the Ranomafana region [90]. However, these analyses do not date or quantify population bottlenecks. Additional analyses designed to evaluate the timing and scale of population decline, such as the Bayesian method of [91], may help clarify whether and to what extent a genetic bottleneck occurred in this population.

It should also be acknowledged that multiple confounding factors can produce spurious bottleneck signatures, one of which is population substructure (Fst ≥ 0.1) [92]. All samples used in this study were collected from multiple groups at four sites located within 8 km of each other and appear to be in migratory contact, which is supported by low pairwise Fst values (Fst< 0.1; S5 Table). However, genetic differentiation between sites was significant in some cases, with the greatest differentiation occurring between Sahamalaotra and two sites located in the southern parcel (Fig 2). If we remove Sahamalaotra from the analyses, however, significant heterozygosity excess remains under most conditions (S1 Text).

That said, there are additional factors that might result in type I errors, such as sampling scheme and immigration. Chikhi et al. [92] found that in highly structured populations, false bottleneck signatures were more likely to be obtained when sampling from a single “deme”. In order to counter this, they suggested sampling from multiple demes [92]. Although our study used samples collected from multiple localities and groups, likely minimizing this potential effect, it is possible that the population of E. rubriventer in RNP exhibits larger-scale structure, with the samples used here representing a single deme.

Finally, one of the assumptions of the heterozygosity excess test is the absence of migration between populations [67]. This assumption is often violated, and while low-level immigration likely masks a bottleneck effect [74], high levels of immigration can actually mimic a population bottleneck [93]. Although RNP is disconnected from forest tracts to the north [94], a narrow corridor to larger tracts of forest to the south remained as of 2000 [90]. Whether or not this physical connectivity actually facilitates migration is unknown, but if there is a high level of immigration from southern populations, this could potentially result in a bottleneck signal without population collapse. At the same time, a spurious bottleneck effect can be obtained when once-connected populations become completely disconnected, without actual population collapse [95]. Such a scenario may be applicable to RNP, which was historically connected to larger and continuous tracts of forest [90, 94]. Future studies incorporating simulations, as well as additional data from RNP and other populations of red-bellied lemurs, will help tease apart the potential effects of population collapse, population structure, and migration on the excess heterozygosity observed in this study.

This study has identified E. rubriventer to be unique among other Eulemur in being dichromatic with the L opsin based on data from a wild population that might have experienced a recent genetic bottleneck. Sampling additional populations throughout E. rubriventer’s range will be important to determine if dichromatic color vision with the L opsin characterizes the species or is specific to particular populations. Understanding opsin variation within the larger context of genomic variation [32] will also help clarify the roles of selection and drift in wild lemur populations. Such studies are increasingly feasible as methods advance for conducting genomic-level variation analyses using non-invasive samples [96].

Supporting Information

S1 File. Eulemur rubriventer M/L opsin gene, exon 5 and partial cds (FASTA format).


S1 Table. Number of individual red-bellied lemurs for which fecal samples were collected in RNP.

Samples were collected between January 2012 and May 2013.


S2 Table. Characteristics of 7 variable microsatellite loci for E. rubriventer that were used in this study.

Locus names, primer sequences, and repeat motifs are from [60]. Size ranges represent size ranges obtained in this study. Annealing temperatures (T) were modified when necessary from [60].


S3 Table. E. rubriventer samples from RNP that were genotyped at exon 5 of the M/L opsin gene.


S4 Table. Summary statistics for 7 microsatellite loci (N = 55 individuals) for the red-bellied lemur population in RNP.


S5 Table. Pairwise Fst values for each sample locality within RNP.

Fst values are above the diagonal and p values are below. p values that were below the Bonferroni-corrected significance value of 0.05 (p < 0.008) are in bold.


S1 Text. Results of heterozygosity excess tests excluding samples from Sahamalaotra.



All methods were in compliance with and approved by Stony Brook University’s IACUC committee (IACUC #: 2010/1803, 2011/1895) and the government of Madagascar (permit #: 284/10, 157/11, 204/12, 056/13). We would like to thank Benjamin Andriamihaja and MICET, Ministre des Eaux et Forets, Madagascar National Parks, Eileen Larney and the Centre ValBio, and the University of Antananarivo for providing logistical support and research permissions in Madagascar. We would also like to thank S. Ambler, C. Angyal, B. Chowdhury, Falinomenjanahary J., Lahitsara J.P., Minoasy A., N. Phelps, Randriarimanga T., Razafimandimby E., Razafindraibe D., Razafindramasy J., Telo A., Tombotiana A.V., Velomaharavo, and Velontsara J.B. for assisting with data collection in Madagascar. Gary Aronsen provided substantial logistical support in the Yale Molecular Anthropology Lab, and Riley Rice assisted with microsatellite analyses. Earlier versions of this manuscript benefited from discussion with and/or comments from Patricia Wright, Andreas Koenig, and John Fleagle. We would also like to thank two anonymous reviewers for constructive comments. Fig 2 includes a map of Madagascar and RNP that is included with permission generously provided by Andrea Baden. Stephen D. Nash generously provided digital copies of lemur illustrations for Fig 3.

Author Contributions

Conceived and designed the experiments: RLJ BJB. Performed the experiments: RLJ. Analyzed the data: RLJ. Contributed reagents/materials/analysis tools: RLJ BJB. Wrote the paper: RLJ BJB.


  1. 1. Surridge AK, Osorio D, Mundy NI. Evolution and selection of trichromatic vision in primates. Trends Ecol Evol. 2003;18: 198–205.
  2. 2. Nathans J, Thomas D, Hogness DS. Molecular genetics of human color vision: The genes encoding blue, green, and red pigments. Science. 1986;232: 193–202. pmid:2937147
  3. 3. Jacobs GH, Neitz J. Inheritance of color vision in a New World monkey (Saimiri sciureus). P Natl Acad Sci USA. 1987;84: 2545–2549.
  4. 4. Neitz M, Neitz J, Jacobs GH. Spectral tuning of pigments underlying red-green color vision. Science. 1991;252: 971–974. pmid:1903559
  5. 5. Kainz PM, Neitz J, Neitz M. Recent evolution of uniform trichromacy in a New World monkey. Vision Res. 1998;21: 3315–3320.
  6. 6. Dulai KS, von Dornum M, Mollon JD, Hunt DM. The evolution of trichromatic color vision by opsin gene duplication in New World and Old World primates. Genome Res. 1999;9: 629–638. pmid:10413401
  7. 7. Williams AJ, Hunt DM, Bowmaker JK, Mollon JD. The polymorphic photopigments of the marmoset: Spectral tuning and genetic basis. Embo J. 1992;11: 2039–2045. pmid:1534748
  8. 8. Jacobs GH, Neitz J, Neitz M. Genetic basis of polymorphism in the color vision of platyrrhine monkeys. Vision Res. 1993;33: 269–274. pmid:8447099
  9. 9. Kawamura S, Hiramatsu C, Schaffner CM, Melin AD, Aureli F, Fedigan LM. Polymorphic color vision in primates: Evolutionary considerations In: Hirai H, Imai H, Go Y, editors. Post-genome biology of primates. Tokyo: Springer; 2012. pp. 93–120.
  10. 10. Melin AD, Matsushita Y, Moritz GL, Dominy NJ, Kawamura S. Inferred L/M cone opsin polymorphism of ancestral tarsiers sheds dim light on the origin of anthropoid primates. P Roy Soc B. 2013;280:1759.
  11. 11. Tan Y, Li WH. Vision—Trichromatic vision in prosimians. Nature. 1999;402: 36. pmid:10573416
  12. 12. Mollon JD, Bowmaker JK, Jacobs GH. Variations of color vision in a New World primate can be explained by polymorphism of retinal photopigments. P Roy Soc B. 1984;222: 373–399.
  13. 13. Mollon JD. "Tho' She Kneel'd in That Place Where They Grew…" the uses and origins of primate color vision. J Exp Biol. 1989;146: 21–38. pmid:2689563
  14. 14. Osorio D, Vorobyev M. Colour vision as an adaptation to frugivory in primates. P Roy Soc B. 1996;263: 593–599.
  15. 15. Osorio D, Ruderman DL, Cronin TW. Estimation of errors in luminance signals encoded by primate retina resulting from sampling of natural images with red and green cones. J Opt Soc Am A. 1998;15: 16–22.
  16. 16. Lucas PW, Darvell BW, Lee PKD, Yuen TDB, Choong MF. Colour cues for leaf food selection by long-tailed macaques (Macaca fascicularis) with a new suggestion for the evolution of trichromatic colour vision. Folia Primatol. 1998;69: 139–152. pmid:9595683
  17. 17. Sumner P, Mollon JD. Catarrhine photopigments are optimized for detecting targets against a foliage background. J Exp Biol. 2000;203: 1963–1986. pmid:10851115
  18. 18. Dominy NJ, Lucas PW. Ecological importance of trichromatic vision to primates. Nature. 2001;410: 363–366. pmid:11268211
  19. 19. Osorio D, Smith AC, Vorobyev M, Buchanan-Smith HM. Detection of fruit and the selection of primate visual pigments for color vision. Am Nat. 2004;164: 696–708.
  20. 20. Pessoa DMA, Maia R, Ajuz R, De Moraes P, Spyrides MHC, Pessoa VF. The adaptive value of primate color vision for predator detection. Am J Primatol. 2014;76: 721–729. pmid:24535839
  21. 21. Morgan MJ, Adam A, Mollon JD. Dichromates detect color-camouflaged objects that are not detected by trichromates. P Roy Soc B. 1992;248: 291–295.
  22. 22. Saito A, Mikami A, Kawamura S, Ueno Y, Hiramatsu C, Widayati KA, et al. Advantage of dichromats over trichromats in discrimination of color-camouflaged stimuli in nonhuman primates. Am J Primatol. 2005;67: 425–436. pmid:16342068
  23. 23. Melin AD, Fedigan LM, Hiramatsu C, Sendall CL, Kawamura S. Effects of colour vision phenotype on insect capture by a free-ranging population of white-faced capuchins, Cebus capucinus. Anim Behav. 2007;73: 205–214.
  24. 24. Caine NG, Osorio D, Mundy NI. A foraging advantage for dichromatic marmosets (Callithrix geoffroyi) at low light intensity. Biol Letters. 2010;6: 36–38.
  25. 25. Vogel ER, Neitz M, Dominy NJ. Effect of color vision phenotype on the foraging of wild white-faced capuchins, Cebus capucinus. Behav Ecol. 2007;18: 292–297.
  26. 26. Hiramatsu C, Melin AD, Aureli F, Schaffner CM, Vorobyev M, Matsumoto Y, et al. Importance of achromatic contrast in short-range fruit foraging in primates. PLoS ONE. 2008;3: e3356. pmid:18836576
  27. 27. Melin AD, Fedigan LM, Hiramatsu C, Kawamura S. Polymorphic color vision in white-faced capuchins (Cebus capucinus): Is there foraging niche divergence among phenotypes? Behav Ecol Sociobiol. 2008;62: 659–670.
  28. 28. Hiramatsu C, Melin AD, Aureli F, Schaffner CM, Vorobyev M, Kawamura S. Interplay of olfaction and vision in fruit foraging of spider monkeys. Anim Behav. 2009;77: 1421–1426.
  29. 29. Melin AD, Fedigan LM, Hiramatsu C, Hiwatashi T, Parr N, Kawamura S. Fig foraging by dichromatic and trichromatic Cebus capucinus in a tropical dry forest. Int J Primatol. 2009;30: 753–775.
  30. 30. Bunce JA, Isbell LA, Grote MN, Jacobs GH. Color vision variation and foraging behavior in wild Neotropical titi monkeys (Callicebus brunneus): Possible mediating roles for spatial memory and reproductive status. Int J Primatol. 2011;32: 1058–1075.
  31. 31. Smith AC, Surridge AK, Prescott MJ, Osorio D, Mundy NI, Buchanan-Smith HM. Effect of colour vision status on insect prey capture efficiency of captive and wild tamarins (Saguinus spp.). Anim Behav. 2012;83: 479–486.
  32. 32. Hiwatashi T, Okabe Y, Tsutsui T, Hiramatsu C, Melin AD, Oota H, et al. An explicit signature of balancing selection for color vision variation in New World monkeys. Mol Biol Evol. 2010;27: 453–464. pmid:19861643
  33. 33. Jacobs GH. The distribution and nature of color vision among the mammals. Biol Rev. 1993;68: 413–471. pmid:8347768
  34. 34. Jacobs GH, Deegan JF, Tan Y, Li WH. Opsin gene and photopigment polymorphism in a prosimian primate. Vision Res. 2002;42: 11–18. pmid:11804627
  35. 35. Jacobs GH, Deegan JF. Photopigment polymorphism in prosimians and the origins of primate trichromacy. In: Mollon JD, Pokorny J, Knoblauch K, editors. Normal and defective colour vision. Oxford: Oxford University Press; 2003. pp. 14–20.
  36. 36. Leonhardt SD, Tung J, Camden JB, Leal M, Drea CM. Seeing red: Behavioral evidence of trichromatic color vision in strepsirrhine primates. Behav Ecol. 2009;20: 1–12.
  37. 37. Veilleux CC, Bolnick DA. Opsin gene polymorphism predicts trichromacy in a cathemeral lemur. Am J Primatol. 2009;71: 86–90. pmid:18837042
  38. 38. Jacobs GH, Deegan JF. Diurnality and cone photopigment polymorphism in strepsirrhines: Examination of linkage in Lemur catta. Am J Phys Anthropol. 2003;122: 66–72. pmid:12923905
  39. 39. Veilleux CC, Louis EE, Bolnick DA. Nocturnal light environments influence color vision and signatures of selection on the OPN1SW opsin gene in nocturnal lemurs. Mol Biol Evol. 2013;30: 1420–1437. pmid:23519316
  40. 40. Veilleux CC, Jacobs RL, Cummings ME, Louis EE, Bolnick DA. Opsin genes and visual ecology in a nocturnal folivorous lemur. Int J Primatol. 2014;35: 88–107.
  41. 41. Mittermeier RA, Ganzhorn JU, Konstant WR, Glander K, Tattersall I, Groves CP, et al. Lemur diversity in Madagascar. Int J Primatol. 2008;29: 1607–1656.
  42. 42. Heesy CP, Ross CF. Evolution of activity patterns and chromatic vision in primates: Morphometrics, genetics and cladistics. J Hum Evol. 2001;40: 111–149. pmid:11161957
  43. 43. Valenta K, Edwards M, Rafaliarison RR, Johnson SE, Holmes SM, Brown KA, et al. Visual ecology of true lemurs suggests a cathemeral origin for the primate cone opsin polymorphism. Funct Ecol. 2015;
  44. 44. Fredsted T, Schierup MH, Groeneveld LF, Kappeler PM. Genetic structure, lack of sex-biased dispersal and behavioral flexibility in the pair-living fat-tailed dwarf lemur, Cheirogaleus medius. Behav Ecol Sociobiol. 2007;61: 943–954.
  45. 45. Olivieri GL, Sousa V, Chikhi L, Radespiel U. From genetic diversity and structure to conservation: Genetic signature of recent population declines in three mouse lemur species (Microcebus spp.). Biol Conserv. 2008;141: 1257–1271.
  46. 46. Craul M, Chikhi L, Sousa V, Olivieri GL, Rabesandratana A, Zimmermann E, et al. Influence of forest fragmentation on an endangered large-bodied lemur in northwestern Madagascar. Biol Conserv. 2009;142: 2862–2871.
  47. 47. Brenneman RA, Johnson SE, Bailey CA, Ingraldi C, Delmore KE, Wyman TM, et al. Population genetics and abundance of the Endangered grey-headed lemur Eulemur cinereiceps in south-east Madagascar: Assessing risks for fragmented and continuous populations. Oryx. 2012;46: 298–307.
  48. 48. Parga JA, Sauther ML, Cuozzo FP, Jacky IAY, Lawler RR. Evaluating ring-tailed lemurs (Lemur catta) from southwestern Madagascar for a genetic population bottleneck. Am J Phys Anthropol. 2012;147: 21–29. pmid:22052208
  49. 49. Wright PC. Primate ecology, rainforest conservation, and economic development: Building a national park in Madagascar. Evol Anthropol. 1992;1: 25–33.
  50. 50. Overdorff DJ. Ecological and reproductive correlates to range use in red-bellied lemurs (Eulemur rubriventer) and rufous lemurs (Eulemur fulvus rufus). Lemur social systems and their ecological basis. In: Kappeler PM, Ganzhorn JU, editors. New York: Plenum Press; 1993. pp. 167–178.
  51. 51. Overdoff DJ, Tecot SR. Social pair-bonding and resource defense in wild red-bellied lemurs (Eulemur rubriventer). In: Gould L, Sauther ML, editors. Lemurs: Ecology and adaptation. New York: Springer; 2006. pp. 235–254.
  52. 52. Wright PC, Erhart EM, Tecot SR, Baden AL, Arrigo-Nelson S, Morelli TL, et al. Long-term lemur research at Centre ValBio, Ranomafana National Park, Madagascar. In: Kappeler PM, Watts DP, editors. Long-term field studies of primates. Berlin Heidelberg: Springer; 2012. pp. 67–100.
  53. 53. Overdorff DJ. Ecological correlates to social structure in two prosimian primates: Eulemur fulvus rufous and Eulemur rubriventer in Madagascar. Ph.D. dissertation. Duke University. 1991.
  54. 54. Durham DL. Variation in responses to forest disturbance and the risk of local extinction: A comparative study of wild Eulemurs at Ranomafana National Park, Madagascar. Ph.D. dissertation. University of California, Davis. 2003.
  55. 55. Tecot SR. Seasonality and predictability: The hormonal and behavioral responses of the red-bellied lemur, Eulemur rubriventer, in southeastern Madagascar. Ph.D. dissertation. Universty of Texas at Austin. 2008.
  56. 56. Baden AL. Communal infant care in black-and-white ruffed lemurs (Varecia variegata). Ph.D. dissertation. Stony Brook University. 2011.
  57. 57. Bradley BJ, Boesch C, Vigilant L. Identification and redesign of human microsatellite markers for genotyping wild chimpanzee (Pan troglodytes verus) and gorilla (Gorilla gorilla gorilla) DNA from faeces. Conserv Genet. 2000;1: 289–292.
  58. 58. Morin PA, Chambers KE, Boesch C, Vigilant L. Quantitative polymerase chain reaction analysis of DNA from noninvasive samples for accurate microsatellite genotyping of wild chimpanzees (Pan troglodytes verus). Mol Ecol. 2001;10: 1835–1844. pmid:11472550
  59. 59. Villesen P, Fredsted T. Fast and non-invasive PCR sexing of primates: Apes, Old World monkeys, New World monkeys and strepsirrhines. BMC Ecol. 2006;6: 8. pmid:16762053
  60. 60. Andriantompohavana R, Morelli TL, Behncke SM, Engberg SE, Brenneman RA, Louis EE Jr. Characterization of 20 microsatellite marker loci in the red-bellied brown lemur (Eulemur rubriventer). Mol Ecol Notes. 2007;7: 1162–1165.
  61. 61. Taberlet P, Griffin S, Goossens B, Questiau S, Manceau V, Escaravage N. Reliable genotyping of samples with very low DNA quantities using PCR. Nucleic Acids Res. 1996;24: 3189–3194. pmid:8774899
  62. 62. van Oosterhout C, Hutchinson WF, Wills DPM, Shipley P. MICRO-CHECKER: Software for identifying and correcting genotyping errors in microsatellite data. Mol Ecol Notes. 2004;4: 535–538.
  63. 63. Raymond M, Rousset F. GENEPOP (version 1.2): Population genetics software for exact tests and ecumenicism. J Hered. 1995;86: 248–249.
  64. 64. Rousset F. GENEPOP ' 007: A complete re-implementation of the GENEPOP software for Windows and Linux. Mol Ecol Resour. 2008;8: 103–106. pmid:21585727
  65. 65. Marshall TC, Slate J, Kruuk LEB, Pemberton JM. Statistical confidence for likelihood-based paternity inference in natural populations. Mol Ecol. 1998;7: 639–655. pmid:9633105
  66. 66. Kalinowski ST, Taper ML, Marshall TC. Revising how the computer program CERVUS accommodates genotyping error increases success in paternity assignment. Mol Ecol. 2007;16: 1099–1106. pmid:17305863
  67. 67. Cornuet JM, Luikart G. Description and power analysis of two tests for detecting recent population bottlenecks from allele frequency data. Genetics. 1996;144: 2001–2014. pmid:8978083
  68. 68. Luikart G, Cornuet JM. Empirical evaluation of a test for identifying recently bottlenecked populations from allele frequency data. Conserv Biol. 1998;12: 228–237.
  69. 69. Piry S, Luikart G, Cornuet JM. BOTTLENECK: A computer program for detecting recent reductions in the effective population size using allele frequency data. J Hered. 1999;90: 502–503.
  70. 70. Di Rienzo A, Peterson AC, Garza JC, Valdes AM, Slatkin M, Freimer NB. Mutational processes of simple-sequence repeat loci in human populations. P Natl Acad Sci USA. 1994;91: 3166–3170.
  71. 71. Peery MZ, Kirby R, Reid BN, Stoelting R, Doucet-Beer E, Robinson S, et al. Reliability of genetic bottleneck tests for detecting recent population declines. Mol Ecol. 2012;21: 3403–3418. pmid:22646281
  72. 72. Williamson-Natesan EG. Comparison of methods for detecting bottlenecks from microsatellite loci. Conserv Genet. 2005;6: 551–562.
  73. 73. Garza JC, Williamson EG. Detection of reduction in population size using data from microsatellite loci. Mol Ecol. 2001;10: 305–318. pmid:11298947
  74. 74. Busch JD, Waser PM, DeWoody JA. Recent demographic bottlenecks are not accompanied by a genetic signature in banner-tailed kangaroo rats (Dipodomys spectabilis). Mol Ecol. 2007;16: 2450–2462. pmid:17561905
  75. 75. Weber JL, Wong C. Mutation of human short tandem repeats. Hum Mol Genet. 1993;2: 1123–1128. pmid:8401493
  76. 76. Lawler RR. Historical demography of a wild lemur population (Propithecus verreauxi) in southwest Madagascar. Popul Ecol. 2011;53: 229–240.
  77. 77. Merenlender AM. The effects of sociality on the demography and genetic structure of Lemur fulvus rufus (polygamous) and Lemur rubriventer (monogamous) and the conservation implications. Ph.D. dissertation. The University of Rochester. 1993.
  78. 78. Markolf M, Kappeler PM. Phylogeographic analysis of the true lemurs (genus Eulemur) underlines the role of river catchments for the evolution of micro-endemism in Madagascar. Front Zool. 2013;10: 1–16.
  79. 79. Springer MS, Meredith RW, Gatesy J, Emerling CA, Park J, Rabosky DL, et al. Macroevolutionary dynamics and historical biogeography of primate diversification inferred from a species supermatrix. PLoS ONE. 2012;7: e49521. pmid:23166696
  80. 80. Melin AD, Wells K, Moritz GL, Kistler L, Orkin JD, Timm RM, et al. Euarchontan opsin variation brings new focus to primate origins. Mol Biol Evol. 2016;
  81. 81. Futuyma DJ. Evolutionary biology, third edition. Sunderland, MA: Sinauer Associates, Inc.; 1998.
  82. 82. Ranaivoarisoa JF, Brenneman L, McGuire SM, Lei R, Ravelonjanahary SS, Engberg SE, et al. Population genetic study of the red-collared brown lemur (Eulemu collaris É. Geoffroy) in southeastern Madagascar. The Open Conservation Biology Journal. 2010;4: 1–8.
  83. 83. Kuo CH, Janzen FJ. Genetic effects of a persistent bottleneck on a natural population of ornate box turtles (Terrapene ornata). Conserv Genet. 2004;5: 425–437.
  84. 84. Funk WC, Forsman ED, Johnson M, Mullins TD, Haig SM. Evidence for recent population bottlenecks in northern spotted owls (Strix occidentalis caurina). Conserv Genet. 2010;11: 1013–1021.
  85. 85. Alasaad S, Soriguer RC, Chelomina G, Sushitsky YP, Fickel J. Siberian tiger's recent population bottleneck in the Russian Far East revealed by microsatellite markers. Mamm Biol. 2011;76: 722–726.
  86. 86. Spear SF, Peterson CR, Matocq MD, Storfer A. Molecular evidence for historical and recent population size reductions of tiger salamanders (Ambystoma tigrinum) in Yellowstone National Park. Conserv Genet. 2006;7: 605–611.
  87. 87. Marshall JCJ, Kingsbury BA, Minchella DJ. Microsatellite variation, population structure, and bottlenecks in the threatened copperbelly water snake. Conserv Genet. 2009;10: 465–476.
  88. 88. Hundertmark KJ, Van Daele LJ. Founder effect and bottleneck signatures in an introduced, insular population of elk. Conserv Genet. 2010;11: 139–147.
  89. 89. Galov A, Kocijan I, Lauc G, Gomercic MD, Gomercic T, Arbanasic H, et al. High genetic diversity and possible evidence of a recent bottleneck in Adriatic bottlenose dolphins (Tursiops truncatus). Mamm Biol. 2011;76: 339–344.
  90. 90. Harper GJ, Steininger MK, Tucker CJ, Juhn D, Hawkins F. Fifty years of deforestation and forest fragmentation in Madagascar. Environ Conserv. 2007;34: 325–333
  91. 91. Storz JF, Beaumont MA. Testing for genetic evidence of population expansion and contraction: An empirical analysis of microsatellite DNA variation using a hierarchical Bayesian model. Evolution. 2002;56: 154–166. pmid:11913661
  92. 92. Chikhi L, Sousa VC, Luisi P, Goossens B, Beaumont MA. The confounding effects of population structure, genetic diversity and the sampling scheme on the detection and quantification of population size changes. Genetics. 2010;186: 983–995. pmid:20739713
  93. 93. Pope LC, Estoup A, Moritz C. Phylogeography and population structure of an ecotonal marsupial, Bettongia tropica, determined using mtDNA and microsatellites. Mol Ecol. 2000;9: 2041–2053. pmid:11123617
  94. 94. Irwin MT, Johnson SE, Wright PC. The state of lemur conservation in south-eastern Madagascar: Population and habitat assessments for diurnal and cathemeral lemurs using surveys, satellite imagery and GIS. Oryx. 2005;39: 204–218.
  95. 95. Broquet T, Angelone S, Jaquiery J, Joly P, Lena J-P, Lengagne T, et al. Genetic bottlenecks driven by population disconnection. Conserv Biol. 2010;24: 1596–1605. pmid:20666803
  96. 96. Snyder-Mackler N, Majoros WH, Yuan ML, Shaver AO, Gordon JB, Kopp GH, et al. Efficient genome-wide sequencing and low coverage pedigree analysis from non-invasively collected samples; 2015. Preprint. Available: bioRxiv: Accessed 20 January 2016.