Influence of social structure and landscape features on
genetic population dynamics of spider monkeys (Ateles
spp.) using an agent-based simulation
—————————————————————————
Juan Pablo Riveros
1*, Anthony di Fiore
2, Andrés Link
131Departamento de Ciencias Biológicas, Universidad de los Andes, Bogotá, Colombia, Carrera 1ª # 18A-10; 2Department of Anthropology, University of Texas at Austin, Austin, Texas 78712, USA; 3Facultad de Administración, Universidad de los Andes, Bogotá, Colombia Carrera 1ª # 18A-10.
ABSTRACT:
Brown spider monkeys are large bodied, long-lived Neotropical primates. They play an important role on the dynamics of ecosystems through their seed dispersal services. They are distributed in the central region of Colombia on both sides of the Magdalena river. Their habitat is highly degraded due to forest clearing by settlers over the past four centuries. Because of their habitat destruction the IUCN classifies this species as Critically Endangered. The use of computer programs in biological science has increased since the start of this current millennium. We gathered the genotype data from a population of Ateles belzebuth ranging in Tiputini Biodiversity Station (Orellana Province, Ecuador). Here, we use GENESYS which is a spatially explicit agent-based population genetics simulation toolkit in order to model the population genetic consequences under three different scenarios: 1) Actual Conditions (AC) where current landscape features are maintained intact as the simulation runs; 2) Degraded Habitat (DH) where pasture lands increase through a lower permeability; and 3) Matrix Enhancement (ME) where permeability between patches is improved. This toolkit requires us to enter initial genetic population data as well as a life table where probabilities of dispersion and mortality are given depending on the age class. After running the simulation, it was necessary to test Hardy-Weinberg equilibrium. Given a non-significant result we proceeded to analyze the population’s heterozygocity, gene flow and inbreeding coefficient. Our results show that under all scenarios the populations are going to decrease to the point where given the best conditions they will last as much as 200 years. Overall heterozygocity decreases drastically as time elapses and is strongly related with the number of populations present. FST (Wright’s fixation index) values indicate how different populations are between each other, as we expected higher values are achieved by AC and DH scenarios. Finally, the inbreeding coefficient (Fsi) revealed a general tendency of lower values further in the simulation. This implies that individuals are forced to reproduce with family members and is related with the population’s heterozygocity. Based on our results, biased by a series of assumptions and modelling limitations, if the real population undergoes the same situation, we recommend to take immediate actions for the survival of the species.
KEYWORDS: Agent-based simulation, Ateles, Inbreeding coefficient, Landscape Genetics, Modeling, Population genetics, Wright’s fixation index
1. INTRODUCTION:
N o w a d a y s , t h a n k s t o t h e development of new technologies, computer science is more widely used to model different variables influencing population’s dynamics. We focused precisely in evaluating the implications of how landscape features together with a given social structure will influence population’s genetics of spider monkeys. This will include microevolutionary processes such as gene flow, genetic drift and genetic differentiation between populations. In regards to landscape features it is possible to modify matrix quality, and include barriers such as rivers or pasture lands that interfere with the connection between forest patches d e t e r m i n i n g g e n e f l o w a c r o s s populations. These types of studies are increasingly growing among the scientific community, with the advantage of giving an insight of how a population will respond to future changes in the environment. (Epperson et al. 2010; Landguth et al. 2010) Using agent-based simulations is very advantageous for modeling biological systems because it is possible to view and analyze each individual by itself (Gilbert, 2008; Railsback & Grimm 2012).
The family Atelidae includes several new world primates including the genus Ateles which are widely spread across the Neotropics, ranging from upper Mexico to the northern part of Argentina. In particular, brown spider monkeys (Ateles hybridus) live in the central part of Colombia, and this region is highly f r a g m e n t e d d u e t o i n c r e a s i n g l y deforestation for extensive cattle ranching and agricultural usage (Restrepo & Kjerfve, 2000). Nearly 80% of the original forest has been removed in order to create land for economic development (Link, A. et al., 2013). Additionally, A. hybridus is a long-lived primate (circa 30 years) and being sexually mature at
around 7-9 years. They give one offspring between 3-5 years, and because it is large bodied it is frequently hunted for meat supply around local communities. Some cultures believe they give additional fertility qualities that make them a desirable prey. Their grouping patterns are referred to as fission-fussion, which implies they live in large groups which often split into smaller subgroups even pairs for daily foraging, minimizing intra-group competition (Ahumada, J.A. 1989; Aureli F. & Schaffner C.M., 2007). The International Union for Conservation of Nature and Natural Resources (IUCN) currently classifies them as critically endangered (Urbani et al. 2008). This is why it is important to have an insight of what would the future hold for this species. Given these characteristics, it is important to recreate natural dynamics using computer simulations, helping us understand what awaits for them in future years. And what can we do as researchers for the survival of the species.
2. Materials & Methods:
2.1 Study Area:
The landscape modeled here corresponds to a region in the central part of Colombia, being the nearest most settlement Bocas del Carare in Santander Department. The exact location of basecamp is 6°42'53.12"N, 74° 8'8.95"W with a roughly eight kilometers radius. (See Figure 1.A.)
2.2 GENESYS Toolkit:
GENESYS is a population genetics simulation toolkit for exploring the effects of social structure and landscape features on population genetic structure. It was designed to allow users to examine the population genetic consequences of specific aspects of the social structure of nonhuman primates (e.g., overlapping generations, sex-biased
A.
B.
Figure 1.
(A.) Map of the region. Cyan line represent the known area in which 5 populations live. In purple a wider area is shown to serve as the complete landscape for the simulations. Dark orange show forest, light orange flooded areas, light green pasture lands and blue water.
(B.) Map with colour coded patches as follows: Green = Terra Firme; Purple = Floodable Forest; White = Pasture Lands; Blue = Water (i.e. Rivers or lagoons) or Out of Bounds.
dispersal patterns, reproduction largely within defined social groups, different mating systems, and variable levels of reproductive skew among breeders) and to explore landscape and social effects on primate dispersal patterns (e.g., differing levels of habitat permeability and predation risk on animal movement and mortality in different parts of the landscape, different dispersal models). GENESYS toolkit was developed by Anthony Di Fiore’s lab at the University of Texas in Austin (Di Fiore, A. & Valencia, L., 2014). This model allows the user to change how a specific individual will respond to others. This is possible through a set of user-modifiable parameters.
For this study we changed these parameters in order to match the characteristics of the genus, including a litter size of just one individual every three years, a total lifespan of 30 years, a sexually mature age of nine years, and infanticide by increasing the male mortality as newborns and decreasing it as they grow older. Although for some individuals it was easy to estimate or have their birth year and month, for others, mainly, adults it was necessary to use a random number generator [1] and a random day/month generator in order to establish their age. The latter is due to the fact that some individuals were already alive when researchers first come to this site. We used the genotypes of a population well studied in the Amazonian region of Ecuador, corresponding to another species of the same genus (A. belzebuth), hence given an over estimate of how genetically rich San Juan individuals really are. While the locus possible alleles range from a number between 90-383, and GENESYS only allows the input of a maximum of two digits, it was necessary to assign each allele to a number from one to 99. A part from the genotype, each individual had to
be identified with the input of both age and birth year in decimal notation, and f i n a l l y a n x a n d y c o o r d i n a t e s corresponding to a spatial location of each individual’s population (i.e. 290.03 , 326.08 for population 1). As the map supplied by The Nature Conservancy or TNC (See Figure 1.A.) dated from the year 2009, the simulation started on that year and ran for as long as 200 years.
2.3 Map:
Thanks to Google’s Earth satellite imagery, it was possible to characterise every region within the map supplied. Four main categories were efficiently identified using ImageJ (version 1.48): (1) Terra-Firme forest shown as green, (2) Lowland (Várzea) Forest shown as purple, (3) Pasture Land for livestock shown as white, and (4) Water/Out of Bounds represented as blue. (See Figure 1.B.)
2.4 Scenarios:
For this study three different scenarios were tested. These were: (A) Actual Conditions [AC], (B) Degraded H a b i t a t [D H] , a n d ( C ) M a t r i x Enhancement [ME]. Each scenario varied from the other in both permeability (p) and mortality (m) each pixel depending of its color [(AC (1) p=1 m=0.08, (2) p=0.68 m=0.15, (3) p=0.1 m=0.64, (4) p=0.05 m=0.92) (DH (1) p=1 m=0.08, (2) p=0.48 m=0.5, (3) p=0.1 m=0.84, (4) p=0.05 m=0.92) (ME (1) p=1 m=0.08, (2) p=1 m=0.08, (3) p=0.68 m=0.15, (4) p=0.05 m=0.92)]
2.5 Genetic Analysis:
In regards to genetic analysis the first step was to use GeneAlex (version 6.501) to test whether or not Hardy-Weinberg equilibrium was significant (Peakall, R., & Smouse, P. E., 2012). Given a non-significant result it was possible to continue with the other
analysis. We used GenoDive (version 2.0b27) for computing the total observed heterozygosity denoted as Ho. And once again using GeneAlex we computed the Wright’s fixation index or FST (Wright, 1978), which allows identifying gene flow across populations (Meirmans, P. G., & Van Tienderen, P. H., 2004). Finally, an inbreeding coefficient was computed (Fsi) this again with GenoDive (Meirmans, P. G., & Van Tienderen, P. H., 2004).
3. RESULTS:
3.1 Demography
In regard to population’s viability we see a clear difference between ME and DH as one should expect (See Figure 2.). A clear decline in populations around 19 years for DH and ME is expected due to
the fact that almost every adult (greater than 11 years) is expected to die at this time. What is remarkable is that AC scenario tends to maintain a balanced number of individuals for more than twice as much as the other two scenarios, but then quickly drops. In overall ME is able to maintain individuals twice as much as the DH scenario does.
3.2 Gene Flow
Every test for Hardy-Weinberg equilibrium showed us non-significant results, which allowed us to proceed to test all other variables. Wright’s fixation index or FST serves as an indicator for gene flow, which ultimately this means how different populations are from one another. There’s a tendency to increase FST as time passes. Although ME shows a smaller slope than the other scenarios, Figure 2.
Demography for all scenarios. The Y axis represent the number of total individuals, and the X axis the year in which the simulation is. In DH all populations deplete after 105 years, for AC is 130 years and for ME 200 years
meaning more connectivity within patches.Wright (1978) suggested an outline for the interpretation of FST values, which theoretically ranges from 0 to 1, but results never get much higher, with significance intervals being, (1) from 0.0 to 0.05 indicates little genetic differentiation, (2) from 0.05 to 0.15 may be considered as a moderate genetic differentiation, (3) 0.15-0.25 indicates great genetic differentiation, and finally (4) from 0.25 and above indicates a great genetic differentiation. Although all scenarios present a highly significant differentiation between populations both
AC and DH show higher values of FST, which is in inverse proportion to gene flow, while ME values are predominately lower (See Figure 3.). FST is a relative, rather than an absolute predictor of differentiation (e.g. when population’s level of variation varies dramatically among loci (Hedrick, 1999))
It is necessary to estimate the heterozygosity for support of genetic diversity (See Figure 4.) Values range from 0 to 1, being 0 completely h o m o z y g o t e a n d 1 c o m p l e t e l y heterozygote.
Figure 3.
Wright’s fixation index over time. In 1978 Wright suggested theoretical values ranging from 0 to 1, although never reaching 1. He suggested that below 0.05 there were no significant differences between populations, from here until 0.15 there was a moderate differentiation with a 95% confidence. From 0.15 up to 0.25 there is a great differentiation with a 99% confidence. Finally above 0.25 implies great genetic differentiation. Since this value compares different populations, as soon as the simulation consist of only one population it stops throwing values.
*0.05 **0.01
A
B
C
Figure 4.
Heterozygosity across time for the three scenarios Actual Conditions (AC), Degraded Habitat (DH), and Matrix Enhancement (ME) being A,B and C respectively. Dashed vertical lines represent a population has been lost.
C
Figure 5.
Inbreeding coefficient (Fsi) across time for all possible scenarios: Actual Conditions (AC), Degraded Habitat (DH), and Matrix Enhancement (ME) being A, B, and C respectively.
B
A
It is remarkable that in every scenario heterozygosis decreases drastically over time, and is consistent with the values acquired with FST. Also heterozygosity is strongly related with the number of populations present at any given time during the simulation. This is because the fewer the individuals, there are less chances of finding new aleles for the new generations.
3.3 Inbreeding Coefficient:
As stated before Wright’s fixation index allows evidencing gene flow across populations. A crucial parameter affecting kinship is female migration to avoid inbreeding (Clutton-Brock & Janson, 2012). Hence it also relates to how are individuals migrating from groups. A. hybridus is a patrilocal species meaning that males stay within the groups and females are the individuals who disperse to other groups. When a value of Fis is negative indicates there is inbreeding among individuals also known as endogamy. For all scenarios it is clear that the degree of inbreeding increases over time mainly caused by low migration opportunities because connections among forest patches are scarce. Higher values were achieved in the AC scenario, something we didn’t expect. What we actually thought was that the lower value would be obtain from the
ME scenario and that was exactly what we obtained (-‐0.412); for AC (-‐0.554) and for ME (-‐0.425).
4. CONCLUSIONS:
For every scenario it is clear that in the near future, populations seem to survive successfully. This changes radically after twenty to forty years when populations quickly decrease. Even under the scenario of matrix enhancement (ME) 180 years from the simulation’s present the population is represented by only one individual that ultimately dies as soon as year 200 is achieved. Our results
clearly show how heterozygocity decreases over time and this has f u r t h e r i m p l i c a t i o n s . W h e n a population becomes homozygotic it is more vulnerable to diseases that can spread rapidly among all individuals. On the other hand, low genetic diversity causes inviability of a given population. Because of few space to move around and Finally, although this m o d e l i m p l i e s u s i n g s e v e r a l assumptions we suggest if the real scenario turns to be as our results to take immediate actions in order to guarantee the survival of the species.
5. AKNOWLEDGEMENTS:
We would like to thank Proyecto primates’ field assistants for collection of fecal samples and processing them in order to reveal the genotypes.
6. REFERENCES:
-‐ Ahumada, J. A. (1989). Behavior and social structure of the free-‐ranging spider monkeys (Ateles belzebuth) in La Macarena. Field Studies of New World
Monkeys, La Macarena, Colombia, 2, 7–
31.
- Aureli, F. and Schaffner, C. M. (2007). Aggression and conflict management at fusion in spider monkeys. Biol. Lett. 3, 147–149.
- Fiore, A. Di, & Valencia, L. M. (2014). The Interplay of Landscape Features and Social System on the Genetic Structure of a Primate Population: An Agent-Based Simulation Study Using “Tamarins,” 2 2 6 – 2 5 7 . d o i : 1 0 . 1 0 0 7 / s10764-013-9726-4
- Clutton-Brock, T., & Janson, C. (2012). Primate socioecology at the crossroads: past, present, and future. Evolutionary Anthropology, 21(4), 136–50. doi: 10.1002/evan.21316
- Epperson, B. K., McRae, B. H., Scribner, K., Cushman, S. A., Rosenberg, M. S., Fortin, M. J., et al. (2010). Utility of computer simulations in landscape genetics. Molecular Ecology, 19, 3549– 3564.
- Hedrick PW (1999) Perspective: Highly variable loci and their interpretation in evolution and conservation. Evolution, 53: 313-318.
- Gilbert, G. N. (2008). Agent-based models. Los Angeles: SAGE.
- Hedrick PW (1999) Perspective: Highly variable loci and their interpretation in evolution and conservation. Evolution, 53: 313-318.
- Landguth, E. L., Cushman, S. A., Murphy, M. A., & Luikart, G. (2010). Relationships between migration rates and landscape resistance assessed using individual-based simulations. Molecular Ecology Resources, 10, 854–862.
- Link, A., Rimbach, R., Heistermann, M., Gomez-Posada, C., Galvis, N., Heymann, E.W. (2013). Effects of logging, hunting and forest fragment size on physiological stress levels in two sympatric ateline primates in Colombia. Conservation Physiology 1: cot031 doi: 10.1093/conphys/cot031
- Meirmans, P. G., & Van Tienderen, P. H. (2004). GENOTYPE and GENODIVE: Two programs for the analysis of genetic diversity of asexual organisms. Molecular
Ecology Notes, 4(4), 792–794. doi: 10.1111/j.1471-8286.2004.00770.x
- Peakall, R., & Smouse, P. E. (2012). GenALEx 6.5: Genetic analysis in Excel. Population genetic software for teaching and research-an update. Bioinformatics, 28(19), 2537–2539. doi:10.1093/ bioinformatics/bts460
- Railsback, S. F., & Grimm, V. (2012). Agent-based and individual-based modeling: A practical introduction. Princeton, NJ: Princeton University Press.
- Restrepo, J. D., & Kjerfve, B. (2000). Water Discharge and Sediment Load from the Western Slopes of the Colombian Andes with Focus on Rio San Juan. The Journal of Geology, 108, 17–33. doi: 10.1086/314390
- Stevenson, P. R., & Link, A. (2010). Fruit Preferences of Ateles belzebuth in Tinigua Park, Northwestern Amazonia. International Journal of Primatology, 3 1 ( 3 ) , 3 9 3 – 4 0 7 . d o i : 1 0 . 1 0 0 7 / s10764-010-9392-8
- Urbani, B., Morales, A. L., Link, A. & Stevenson, P. (2008). Ateles hybridus. The IUCN Red List of Threatened S p e c i e s . V e r s i o n 2 0 1 5 . 2 . <www.iucnredlist.org>.
- Wright, S. (1978) Evolution and the Genetics of Populations. Vol. 4. Variability Within and Among Natural Populations. Univ. of Chicago Press, Chicago.
