Research Article
Research Article
Conservation genetics of American crocodile, Crocodylus acutus, populations in Pacific Costa Rica
expand article infoLaurie A. Mauger, Elizabeth Velez§, Michael S. Cherkiss|, Matthew L. Brien, Frank J. Mazzotti#, James R. Spotila¤
‡ Southern Utah University, Cedar City, United States of America
§ Kelonia Conservation Society, Heredia, Costa Rica
| Wetland Aquatic Research Center, Davie, United States of America
¶ Wildlife Management International Pty, Karama, Australia
# University of Florida Fort Lauderdale Research and Education Center, Davie, United States of America
¤ Drexel University, Philadelphia, PA, United States of America
Open Access


Maintaining genetic diversity is crucial for the survival and management of threatened and endangered species. In this study, we analyzed genetic diversity and population genetic structure at neutral loci in American crocodiles, Crocodylus acutus, from several areas (Parque Nacional Marino Las Baulas, Parque Nacional Santa Rosa, Parque Nacional Palo Verde, Rio Tarcoles, and Osa Conservation Area) in Pacific Costa Rica. We genotyped 184 individuals at nine microsatellite loci to describe the genetic diversity and conservation genetics between and among populations. No population was at Hardy-Weinberg Equilibrium (HWE) over all loci tested and a small to moderate amount of inbreeding was present. Populations along the Pacific coast had an average heterozygosity of 0.572 across all loci. All populations were significantly differentiated from each other with both FST and RST measures of population differentiation with a greater degree of molecular variance (81%) found within populations. Our results suggest C. acutus populations in Pacific Costa Rica were not panmictic with moderate levels of genetic diversity. An effective management plan that maintains the connectivity between clusters is critical to the success of C. acutus in Pacific Costa Rica.


American crocodile, heterozygosity, microsatellites, population genetics, genetic structure


Threatened and endangered species face many challenges including habitat fragmentation and destruction, human population growth, and loss of genetic variability. Maintenance of genetic diversity is of increasing importance in the preservation of threatened and endangered species (Lacy 1997; Haig 1998; Reed and Frankham 2003; Reed et al. 2007). Lack of genetic diversity can lead to inbreeding depression (Frankham 1995), decreased immunity (O’Brien et al. 1985), decreased reproductive performance (O’Brien et al. 1985; Parker et al. 1991) and eventual extinction (Frankham 2005). Effective management strategies for threatened and endangered species require integration of all aspects of the species’ biology, including both demography and genetics (Lande 1988)

The American crocodile (Crocodylus acutus) is mainly a coastal species ranging from the extreme southern tip of Florida, through the Caribbean, and Central and northern South America (Mazzotti 1999; Thorbjarnarson 2010). Populations range-wide are threatened by habitat destruction and fragmentation, poaching, and past overexploitation (Ross 1998; Thorbjarnarson et al. 2006; Mazzotti et al. 2007). Current threats to C. acutus in Costa Rica include habitat loss (particularly nesting habitat), deliberate killing (Machkour-M’Rabet et al. 2009), and pollution (Rainwater et al. 2007; Rainwater et al. 2011). Crocodylus acutus is a wide ranging and ecologically plastic species (Thorbjarnarson 2010) with substantial genetic differentiation among populations (Menzies and Kushlan 1991; Rodriguez 2007; Porras et al. 2008; Thorbjarnarson 2010). Determining the status and ecology of C. acutus in Costa Rica is a priority project of the IUCN Crocodile Specialist Group Action Plan (Ross 1998). Genetic evaluations of C. acutus range-wide were further named as a moderate priority project in the 2010 Action Plan (Thorbjarnarson 2010). Therefore, it is important to describe genetic diversity and differentiation of this species throughout its range, including Costa Rica.

In the present study, we investigated the genetic structure of C. acutus populations in several areas of Pacific Costa Rica (Fig. 1) and the degree of effective migration occurring between populations. A series of estuaries provide pockets of suitable crocodile habitat along the Pacific coast which made it an optimal area for studying gene flow between potential metapopulations. Crocodiles are known to migrate long distances (Kay 2004; Campos et al. 2006; Read et al. 2007; Campbell et al. 2013). There have been accounts of C. acutus migrating over 35 km for nesting (Cherkiss et al. 2006) and movements over 388 km (Cherkiss et al. 2014) in southern Florida. The ability of crocodilians to migrate and disperse long distances increases the amount of potential gene flow between neighboring or distant populations. It is possible that crocodiles are dispersing between habitat patches in Pacific Costa Rica; thus, facilitating gene flow along the coast. We used microsatellites to test the hypothesis that C. acutus populations do not exist as a continuous, panmictic population in Pacific Costa Rica.


Study area

We sampled 5 localities in Pacific Costa Rica for C. acutus (Fig. 1). Site LB (Parque Nacional Marino Las Baulas) was in the Tempisque Conservation Area (ACT); site PV (Parque Nacional Palo Verde) was in the Arenal-Tempisque Conservation Area (ACA-T); site SR (Parque Nacional Santa Rosa) was in the Guanacaste Conservation Area (ACG); site RT (Rio Tarcoles) was in Central Pacific Conservation Area (ACOPAC); sites RS (Rio Sierpe), T (Terraba Delta), PL (Pejeperro Lagoon), PTL (Pejeperrito Lagoon), RE (Rio Esquinas), RC (Rio Coto) and PB (Parrot Bay Lodge, Puerto Jimenez) were in the Osa Conservation Area (ACOSA). Crocodiles were sampled from seven areas in ACOSA; however, they have been combined as one population due to low sample numbers. Localities ranged from large river systems (PV, RT, and ACOSA), to estuaries (LB and SR) and coastal lagoons (SR and ACOSA). (See Mauger et al. 2012 for additional study location information.)

Figure 1.

Map of collection sites in Costa Rica of the American crocodile, Crocodylus acutus. Parque Nacional Marino Las Baulas (LB), Parque Nacional Palo Verde (PV), Parque Nacional Santa Rosa (SR), Rio Tarcoles (RT), Rio Sierpe (RS), Terraba Delta (T), Pejeperitto Lagoon (PTL), Pejeperro Lagoon (PL), Rio Esquinas (RE), Rio Coto (RC) and Parrot Bay Lodge (PB). Sites RS, T, PTL, PL, RE, RC, and PB were grouped together as Osa Conservation Area (ACOSA). Localities ranged from large river systems (PV, RT, and ACOSA) to estuaries (LB, SR) and coastal lagoons (SR and ACOSA).

Sample collection

We collected blood and tissue samples at the beginning of the rainy season in SR (2007) and PV (2005, 2008 and 2009), throughout the year in LB (2007 – 2009), during the rainy season in RT (2005 – 2006) and during the end of the dry season in ACOSA (2006, 2008 and 2009). We captured crocodiles mainly during spotlight surveys using the break-away snare method (Hutton et al. 1987; Hutton and Woodhouse 1989), snake tongs or by hand (see Mauger et al. 2012 for additional information on sample collection). Blood and/or tissue was collected from 184 individuals (see Table 1 for size class distribution of samples). In sites where a large number of hatchlings were captured, a random number selector was used to randomly select four to six hatchlings for genetic analysis. Tissue was collected from the caudal scutes during marking and blood was collected from the caudal vein or the dorsal sinus. Tissue samples were preserved in 95–100% ethanol. Blood was preserved on Whatman FTA Cards for DNA Preservation® (GE Life Sciences).

Size class distribution of genotyped crocodiles for each site.

Site Hatchling Juvenile Subadult Adult Total
LB 12 17 13 4 46
SR 4 8 5 0 17
ACOSA 0 44 3 1 48
PV 9 32 2 9 53
RT 0 13 0 3 19
Total 25 114 23 17 184

DNA isolation and microsatellite amplification

We isolated DNA from tissue samples using the DNeasy Blood and Tissue Kit™ (Qiagen) and purified from blood cards with two five-minute washes with FTA Purification Reagent (Whatman) and two five-minute washes with Tris-EDTA (TE; 10 mM Tris-Cl, pH 7.5, 1 mM EDTA) buffer. Each wash consisted of 50 µl of solution.

We amplified nine microsatellite DNA loci using previously characterized primers (Dever and Densmore 2001; Fitzsimmons et al. 2001) C391, Cj16, Cj18, Cj20, Cj109, Cj131, CU5-123, CUD68, and CUJ131 via polymerase chain reaction (PCR). The forward primer of each pair was labeled with a fluorescent dye (6-FAM, HEX or NED; Applied Biosystems) to allow for the detection and sizing of DNA fragments. The DNA was amplified in 25 µl reactions containing 1.25 units of EconoTaq DNA Polymerase (Lucigen), 2.5 µl 10X buffer (100 mM Tris-HCl (pH 9.0), 500 mM KCl, 1% Triton X-100, 15 mM MgCl2), 1.0 µl 25 mM MgCl2 (Cj16 and Cj20) or 0.5 µl 25 mM MgCl2 (all other primers), 1.0 µl of 10 mM dNTP’s (Qiagen), 1.0 µl each of the forward and reverse primer, approximately 100 ng template DNA and purified water to the final volume. Microsatellites were amplified according to the following parameters: initial denaturation at 94°C for 2 minutes, 33 cycles of 94°C for 1 minute, 58°C (C391, Cj18, Cj131, CU5-123, CUD68, CUF131), 59°C (Cj16, Cj20) or 62°C (Cj109) for 1 minute, and 72°C for l minute, and a final extension at 72°C for 10 minutes. Amplified loci were separated on an Applied Biosystems (ABI) 3730xl Genetic Analyzer and sized with LIZ-500 size standard by Genewiz, Inc ( Genotypes were assigned using PeakScanner 1.0 (Applied Biosystems).

Genetic diversity

Data files were converted to formats supported by various genetic programs in CREATE 1.0 (Coombs et al. 2007). Probability of Identity (PI) was estimated in GENALEX 6 (Peakall and Smouse 2006) to determine the minimum number of microsatellites needed to identify individuals. Allelic richness (AR) and the number of private alleles (APriv) were estimated in HP-RARE (Kalinowski 2005). We estimated numbers of alleles, allele frequencies and gene diversities in FSTAT (Goudet 1995).

Observed versus expected number of heterozygotes were estimated in Genepop on the Web (Raymond and Rousset 1995; Rousset 2008). Departure from Hardy-Weinberg Equilibrium (HWE) and linkage disequilibrium (LD) were estimated in Genepop on the Web (Raymond and Rousset 1995; Rousset 2008). Departure from HWE was tested using an exact test (Guo and Thompson 1992) and a chi-square goodness of fit test with a dememorization number of 10,000, and 1,000 batches of 10,000 iterations each. Linkage disequilibrium was tested to determine if small effective population sizes within the different localities caused nonrandom association of alleles at different loci. Linkage disequilibrium was tested for all pairs of loci used by the log likelihood ratio statistic under the same parameters as HWE. All p-values were adjusted to allow for multiple comparisons. Weir and Cockerham’s (1984) inbreeding coefficient, FIS, was estimated for each population in FSTAT (Goudet 1995) with and without randomly selected hatchlings. Homozygote excess at each locus in each locality was estimated by MICROCHECKER (Van Oosterhout et al. 2004). MICROCHECKER was also used to identify if null alleles were present at each locus in each locality. Null alleles were suggested for loci with a general excess of homozygotes for most allele size classes.

We identified whether rare alleles had been lost due to previous genetic bottlenecks under the infinite alleles (IAM) and stepwise mutation (SMM) models in BOTTLENECK version 1.2.02 (Piry et al 1999). Both the standardized differences and Wilcoxon tests were run.

Population genetic structure

We used an analysis of molecular variance (AMOVA) to estimate the percentage of variance within and among populations with GENALEX 6 (Peakall and Smouse 2006). Population differentiation was estimated for all population pairs using several methods. We estimated FST and RST for each population pair in FSTAT (Goudet 1995) and Arlequin ver. 3.11 (Excoffier et al. 2005) respectively. All p-values were adjusted to allow for multiple comparisons.

We used Mantel’s test to determine the relationship between geographic and genetic distance. Isolation by Distance Web Service (IBDWS; Jensen et al. 2005) was used to test for the presence of isolation by distance (IBD) between population pairs. Sites RT and T were excluded from the analysis because GPS coordinates of crocodile captures were not available. Each Mantel test was performed with 30,000 randomizations. Distances between populations were estimated using an oceanic/coastline route. Rousset’s genetic distance (F/(1-F)) was calculated using genetic differentiation (FST).


Genetic diversity

The nine microsatellites chosen for this study had an average probability of identity (PI) of 4.96-6 across all five Costa Rican populations (SR=5.4-6; LB=7.9-6; ACOSA=8.7-8; PV=1.5-7; RT=1.1-5). This indicated that there was a low probability that two individuals chosen at random would have the same genotype. These microsatellites were sufficient for this study.

We identified 88 alleles in five C. acutus populations sampled in Pacific Costa Rica across all nine microsatellite loci. Average AR and APriv over all loci were estimated using a corrected sample size of 34 alleles. The AR ranged between 4.22 and 5.64 and APriv ranged between 0.27 and 1.36 in the sampled locations (Table 2). Allele frequencies for each microsatellite locus ranged from 0 (in localities where the allele was not genotyped) to 0.882 (Appendix 1: Allele Frequencies). Allele frequencies were also calculated with the hatchlings removed. There were no substantial differences in allele frequencies when hatchlings were removed. We tested for genetic bottlenecks under the IAM and SMM models for all samples combined and each site separately. Bottlenecks were not detected under IAM (p>0.05), but were detected in all populations under SMM (p<0.009) with the standardized differences test.

Genetic variability estimates for Crocodylus acutus populations in Pacific Costa Rica.

Sample Site Code N AR APriv
Area of Conservation Tempisque ACT
Las Baulas National Park LB 46 4.31 0.37
Palo Verde National Park PV 54 5.19 0.58
Area of Conservation Guanacaste ACG
Santa Rosa National Park SR 17 4.22 0.3
Central Pacific Conservation Area ACOPAC
Rio Tarcoles RT 17 4.22 0.27
Osa Conservation Area ACOSA 49 5.64 1.36

No population was in Hardy-Weinberg Equilibrium (HWE) over all nine microsatellite loci tested (Table 3). Site LB was not in HWE at loci Cj16 (p<0.001), Cj109 (p=0.03) and Cj131 (p=0.01); site SR was not in HWE equilibrium at locus C391 (p=0.005); site ACOSA was not in HWE at loci C391 (p=0.03), Cj18 (p=0.02), Cj20 (p=0.04), Cj109 (p=0.002), CU5-123 (p<0.001) and CUD68 (p<0.001); site PV was not in HWE at loci Cj18 (p=0.001), Cj109 (p=0.03), Cj131 (p=0.01), CU5-123 (p<0.001), CUD68 (p<0.001) and CUJ131 (p=0.02); and site RT was not in HWE at loci Cj18 (p=0.03), Cj20 (p=0.02) and CUD68 (p=0.0026). The Wilcoxon test identified heterozygote deficiencies in all localities for at least one microsatellite loci (p=0.002-0.02; Table 3). Null alleles were suggested for at least one locus in each locality by general excess of homozygote for most allele size classes (Table 3). Average FIS values ranged from 0.096 to 0.179 for each site. Inbreeding levels were 0.179 and 0.103 in ACOSA and RT, respectively. Inbreeding coefficients were calculated with hatchlings included and removed in the remaining sites. Site PV had a lower FIS when hatchlings were included (0.127 and 0.142 with and without hatchlings, respectively). Site SR had higher FIS when hatchlings were included (0.179 and 0.166 with and without hatchlings, respectively). There was no difference in FIS (0.096) in site LB.

Expected (HE) and observed heterozygosities (HO) for microsatellite loci in Crocodylus acutus populations.

(N = 46)
(N = 17)
(N = 54)
(N = 17)
(N = 49)
C391 0.46 0.52 0.65# 0.35*+ 0.59 0.57 0.36 0.35 0.57# 0.53*
Cj16 0.48 0.28*+# 0.51 0.53 0.62 0.59 0.39 0.47 0.64 0.65
Cj20 0.41 0.61 0.56 0.35 0.67 0.59 0.75# 0.41* 0.66# 0.63+
Cj131 0.49 0.48 0.22 0.24 0.37# 0.43 0.39 0.35 0.45 0.47*
Cj18 0.45 0.41 0.47 0.53 0.76# 0.70* 0.73# 0.41*+ 0.89# 0.80*+
Cj109 0.64 0.59*# 0.78 0.65 0.78# 0.67* 0.62 0.76+ 0.61# 0.39*
CU5-123 0.40 0.37* 0.06 0.06 0.62# 0.33*+ 0.49 0.53+ 0.45# 0.29*
CUD68 0.70 0.54*+ 0.68 0.65 0.73# 0.46+ 0.65# 0.35*+ 0.76# 0.39*+
CUJ131 0.68 0.67# 0.63 0.41* 0.57# 0.61 0.56 0.76 0.54 0.51

Linkage disequilibrium (LD) tests were performed to investigate the distribution of the nine microsatellite loci for C. acutus populations on the Pacific coast of Costa Rica. Pairwise comparisons were performed for each population. LD did not play a strong role in the nine microsatellites tested (p=0.001-0.99). All p values were adjusted for multiple tests.

Population genetic structure

An analysis of molecular variance estimated that 19% of the variation occurred between populations, while 81% of molecular variance occurred within individual populations. This suggested that individual populations were genetically diverse. Population differentiation was measured between all population pairs using FST and RST. All population pairs were significantly differentiated (p=0.05) using both measures of population differentiation (Table 4). We observed the least amount of differentiation between ACOSA with LB and SR and the highest level of differentiation between LB with SR and RT.

Population differentiation between all Crocodylus acutus population pairs using RST (above 0 line) and FST (below 0 line).

LB 0.66+ 0.04+ 0.23+ 0.65+
SR 0.19+ 0.04* 0.18+ 0.11*
ACOSA 0.15+ 0.1+ 0.10+ 0.06*
PV 0.1+ 0.08+ 0.07+ 0.24+
RT 0.24+ 0.14+ 0.10+ 0.10+

Isolation by distance (IBD) was estimated between each sampled crocodile in all localities in Pacific Costa Rica. No IBD was observed (p=0.92; Fig. 2). Isolation between populations did not restrict gene flow.

Figure 2.

Mantel test for isolation by distance between individual crocodiles in all localities. No isolation by distance was observed (p=0.92). Rousset’s distance (F/(1-F)) was used for genetic distance. Geographic distance was the log of a straight line distance (km) between populations.


The nine microsatellites chosen in this study provided data on the genetic structure of C. acutus populations along the Pacific coast of Costa Rica. Average heterozygosity of crocodiles along the Pacific coast of Costa Rica was slightly higher than or comparable to that in other crocodilian populations (Glenn et al. 1998; Davis et al. 2001; Dever et al. 2002; Ryberg et al. 2002; Verdade et al 2002; de Thoisy et al. 2006; Rodriguez et al. 2008). However, in this study, several individual loci did have lower heterozygosity values, possibly due to inbreeding observed in all populations. Crocodiles within site SR had higher FIS values than other sites. This may be because we observed and captured few individuals that had exceeded minimum breeding size. This site also represented the smallest crocodile habitat with lower encounter rates (Mauger et al. 2012). Fewer breeding crocodiles and lower encounter rates could explain the higher inbreeding levels since presumably the gene pool is limited to fewer individuals. In all surveyed localities, size class distributions estimated during the study showed a higher percentage of juveniles (0.5 – 1.25 m) than adults (>2.25 m) (Mauger et al. 2012). The higher percentage of juveniles in these localities could indicate population recovering from past bottlenecks (Ouboter and Nanhoe 1989) and explain the higher inbreeding levels observed in this study.

We identified previous genetic bottlenecks in all sampled localities. Past bottlenecks were confirmed under the SMM model by BOTTLENECK (Piry et al. 1999). Based on these analyses, we concluded that all populations underwent a previous reduction in population size. Population bottlenecks lead to the rapid loss of rare alleles and results in the loss of the total number of alleles at a faster rate than a loss in overall heterozygosity (Ortego et al. 2010). The low number of private alleles observed in this study (Table 2) could be an artifact of past genetic bottlenecks. Null alleles were also detected for at least one locus in each locality (Table 3). The absence of these alleles in the analysis could help explain the loss of genetic variation observed in these localities. Crocodylus acutus populations declined range-wide through the mid-20th century as a result of hunting and illegal poaching (Ross 1998; Thorbjarnarson et al. 2006; Thorbjarnarson 2010). These human activities have not been documented in Costa Rica; however, it is possible that populations here experienced similar pressures and population declines. We observed some poaching and killing of large individuals in LB during the study and have received reports of similar activities at other sites in Pacific Costa Rica (personal observation and communication with local people). Anecdotal data suggests that crocodile numbers are also increasing in LB (F. Paladino, personal communication) and PV (Bolaños-Montero 2012). Recent introduction of tilapia into the Tempisque River Basin (site PV) has provided a continuous food source for crocodiles (Sandlund et al. 2010), potentially contributing to the recent population growth. As a result, crocodile numbers in this region have increased precipitously in recent years, causing adverse interactions between human and crocodile populations (Bolaños-Montero 2012). Additional factors are most likely at play, which are contributing to population growth.

No population was in Hardy-Weinberg Equilibrium for all microsatellite loci. This could be due to the heterozygote deficiency observed at loci that were not in HWE, to inbreeding documented in several populations, or to effective migration. Our results suggest that crocodile populations in Pacific Costa Rica were differentiated from each other, as supported by studies on C. acutus and other crocodilian species (Farias et al. 2004; Porras et al. 2008; Machkour-M’Rabet et al. 2009; Thorbjarnarson 2010). All population pairs exhibited significant genetic differentiation, with all but two population pairs (LB and ACOSA, and SR and ACOSA) showing moderate differentiation from each other (RST<0.05; Table 4). These values supported the hypothesis that migration occurred between populations; however, some population pairs were more differentiated from each other and thus had lower historical migration rates between patchily distributed habitats in Costa Rica. The level of subdivision observed suggested that crocodiles in Pacific Costa Rica were not panmictic; however, genetic connections did exist. Additionally, the majority of molecular variance was observed within populations. This could explain why moderate differentiation was observed between most population pairs. However, the highly mobile nature of crocodiles (Kay 2004; Campos et al. 2006; Read et al. 2007; Campbell et al. 2013) could be facilitating gene flow between populations along the Pacific coast. A recent study on the spatial ecology of C. acutus in Panama, suggests that males have a larger home ranges, but females have larger average movement distances (Balaguera-Reina et al. 2016). Balaguera-Reina et al. (2016) also noted dispersal differences between age classes and dry and wet seasons. Dispersal differences between age classes, i.e. subadults dispersing to find mating territories, could also explain the departure from Hardy-Weinberg Equilibrium and moderate differentiation levels. GPS-based tracking studies of Costa Rican C. acutus would contribute important information on contemporary crocodile dispersal abilities and maximum home ranges in patchily distributed habitats.


The data presented here supported moderate differentiation and an absence of isolation by distance in Pacific Costa Rica. Our results suggested the loss of genetic variation through a lack of connectivity between some localities and previous population bottlenecks. The moderate heterozygosity values and genetic differentiation described here emphasized the need to protect all potential crocodile habitat, to write management plans across conservation areas and national parks in Costa Rica, and the need for conservation and management units to extend over the entire span of a species’ range.


We would like to thank everyone that helped us in the field, especially Bernal Cortes, Luis Fernando Lopez Lara, Juan Jose Victor Villalobos, Guillermo Briceo, Ademar Rosales, Fabricia Alvarez, Issac Ehresman, Gareth Blakemore, Mike Boston and Jim Tamarack. We would also like to thank the Ministerio del Ambiente, Energa y Telecomunications (MINAET) and the Sistema Nacional de Áreas de Conservación (SINAC), especially Rotney Piedra, Jose Quiroz and Roger Blanco for research permits. Luz Barrantes Bahder and Juan Rafael Boloños Montero provided samples from Rio Tarcoles and Tempísque. This work was largely supported by the Betz Chair of Environmental Sciences at Drexel University, and the Sophie Danforth Conservation Biology Fund from Roger Williams Park Zoo in Rhode Island. Research in the Osa Peninsula was also partially funded by the El Tigre Fund. The field research in Las Baulas National Park was partially supported by the Leatherback Trust. The Leatherback Trust provided vehicles in Guanacaste, lodging at the Goldring Gund Marine Biology Station at Playa Grande, and a boat for many of the crocodile surveys conducted to collect genetic material. Any use of trade, product or firm names is for descriptive purposed only and does not imply endorsement by the U.S. Government.


  • Balaguera-Reina S, Venegas-Anaya M, Sánchez A, Arbelaez I, Lessios HA, Densmore LD (2016) Spatial ecology of the American crocodile in a tropical Pacific island in Central America. PLoS ONE 11.
  • Bolaños-Montero JR (2012) Survey of American crocodiles in Tempisque Great Wetlands, Costa Rica. Crocodile Specialist Group Newsletter 31: 5–7.
  • Campbell HA, Dwyer RG, Irwin TR, Franklin CE (2013) Home range utilization and long-range movement of estuarine crocodiles during the breeding and nesting season. PLoS ONE 8:
  • Campos Z, Coutinho M, Mourão G, Bayliss P, Magnusson WE (2006) Long distance movements by Caiman crocodilus yacare: implications for management of the species in the Brazilian Pantanal. Journal of Herpetology 16: 123–132.
  • Cherkiss MS, Parry M, Mazzotti FJ (2006) Crocodylus acutus (American crocodile) migration. Herpetological Review 38: 72–73.
  • Cherkiss MS, Mazzotti FJ, Hord L, Aldecoa M (2014) Remarkable movements of an American crocodile (Crocodylus acutus) in Florida. Southeastern Naturalist 13: N52–N56.
  • Davis LM, Glenn TC, Elsey RM, Dessauer HC, and Sawyer RH (2001) Multiple paternity and mating patterns in the American alligator, Alligator mississippiensis. Molecular Ecology 10: 1011–1024.
  • de Thoisy BT, Hrbek T, Farias IP, Vasconcelos WR, Lavergne A (2006) Genetic structure, population dynamics, and conservation of Black caiman (Melanocuchus niger). Biological Conservation 133: 474–482.
  • Dever JA, Densmore LD (2001) Microsatellite’s in Morelet’s crocodile (Crocodylus moreletti) and their utility in addressing crocodilian population genetics questions. Journal of Herpetology 35: 541–544.
  • Excoffier L, Laval G, Schneider S (2005) Arlequin ver 3.0: An integrated software package for population genetics data analysis. Evolutionary Bioinformatics Online 1: 47–50.
  • Farias IP, da Silveira R, de Thoisy B, Monjelo LA, Thorbjarnarson J, Hrbek T (2004) Genetic diversity and population structure of Amazonian crocodilians. Animal Conservation 7: 265–272.
  • Fitzsimmons NN, Tanksley S, Forstner MRJ, Louis EE, Daglish R, Gratten J, Davis S (2001) Microsatellite markers for Crocodylus: new genetic tools for population genetics, mating system studies and forensics. In: Grigg GC, Seebacher F, Franklin CE (Eds) Crocodilian Biology and Evolution. Surrey Beaty & Sons (Australia), 51–57.
  • Glenn TC, Dessauer HC, Braun MJ (1998) Characterization of microsatellite DNA loci in American alligators. Copeia 1998: 591–601.
  • Goudet J (1995) FSTAT (version 1.2): A computer program to calculate F-statistics. Journal of Heredity 86: 485–486.
  • Guo S, Thompson E (1992) A monte-carlo method for combined segregation and linkage analysis. American Journal of Human Genetics 51: 1111–1126.
  • Hutton JM, Loveride JP, Blake DK (1987) Capture methods for the Nile crocodile in Zimbabwe. In: Webb GJW, Manolis SC, Whitehead PJ (Eds) Wildlife management: Crocodiles and alligators. Surrey Beatty & Sons (Australia), 243–247.
  • Hutton JM, Woodhouse ME (1989) Mark-recapture to assess factors affecting the proportion of a Nile crocodile population seen during spotlight counts at Ngezi, Zimbabwe, and the use of spotlight counts to monitor crocodile abundance. Journal of Applied Ecology 26: 381–395.
  • Kay WR (2004) Movements and home ranges of radio-tracked Crocodylus porosus in the Cambridge Gulf region of Western Australia. Wildlife Research 31: 495–508.
  • Lacy RC (1997) Importance of genetic variation to the viability of mammalian populations. Journal of Mammalogy 78: 320–335.
  • Machkour-M’Rabet S, Hénaut Y, Charruau P, Gevrey M, Winterton P, Legal L (2009) Between introgression events and fragmentation, islands are the last refuge for the American crocodile in Caribbean Mexico. Marine Biology 156: 1321–1333.
  • Mauger LA, Velez E, Cherkiss MS, Brien ML, Boston M, Mazzotti FJ, Spotila JR (2012) Population assessment of the American crocodile, Crocodylus acutus (Crocodilia: Crocodylidae) on the Pacific coast of Costa Rica. Revista de Biología Tropical Trop 60: 1889–1901.
  • Menzies RA, Kushlan JA (1991) Genetic variation in populations of the American Crocodile. Journal of Herpetology 25: 357–361.
  • O’Brien SJ, Roelke ME, Marker L, Newman A, Winkler CA, Meltzer D, Colly L, Evermann JF, Bush M, Wildt DE (1985) Genetic basis for species vulnerability in the cheetah. Science 227: 1428–1434.
  • Ouboter PE, Nanhoe MR (1989) Notes on the dynamics of a population of Caiman crocodilus in Northern Suriname and its implications for management. Biological Conservation 48: 243–264.
  • Piry S, Luikart G, Cornuet JM (1999) BOTTLENECK: A computer program for detecting recent reductions in the effective population size using allele frequency data. Journal of Heredity 90: 502–503.
  • Porras LPM, Boloños Montero JR, Barr BR (2008) Genetic variation and gene flow among populations of Crocodylus acutus (Crocodylia: Crocodylidae) in three rivers of Central Pacific, Costa Rica. Revista de Biología Tropical 56: 1471–1480.
  • Rainwater TR, Millichamp NJ, Barrentes LDB, Barr BR, Montero JRB, Platt SG, Abel MT, Cobb GP, Anderson TA (2011) Occular disease in American crocodiles (Crocodylus acutus) in Costa Rica. Journal of Wildlife Diseases 47: 415–426.
  • Rainwater TR, Wu TH, Finger AG, Cañas JE, Yu L, Reynolds KD, Coimbatore G, Barr B, Platt SG, Cobb GP, Anderson TA, McMurry ST (2007) Metals and organochlorine pesticides in caudal scutes of crocodiles from Belize and Costa Rica. Science of the Total Environment 373: 146–156.
  • Raymond M, Rousset F (1995) GENEPOP (version 1.2): population genetics software for exact tests and exumenicism. Journal of Heredity 86: 248–249.
  • Read MA, Grigg GC, Irwin SR, Shanahan D, Franklin CE (2007) Satellite tracking reveals long distance coastal travel and homing by translocated estuarine crocodiles, Crocodylus porosus. PLoS ONE 9.
  • Rodriguez D (2007) Crocodilian evolution, systematics and population genetics: recovery and ecological interactions of the American crocodile (Crocodylus acutus). PHD Thesis. Texas Tech University.
  • Rodriguez D, Cedeño-Vazquez JR, Forstner MR, Densmore LD (2008) Hybridization between Crocodylus acutus and Crocodylus moreletti in the Yucatan Peninsula: II Evidence from microsatellites. Journal of Experimental Zoology Part B 309A: 674–686.
  • Ross JP (1998) Crocodiles: an action plan for their conservation, IUCN/SSG Crocodile Specialist Group Publication. Oxford Press (Oxford).
  • Ryberg WA, Fitzgerald LA, Honeycutt RL, Cathey JC (2002) Genetic relationships of American alligator populations distributed across different ecological and geographic scales. Journal of Experimental Zoology Part B 294: 325–333.
  • Sandlund OT, Daverdin RH, Choudhury A, Brooks DR, Diserud OH (2010) A survey of freshwater fishes and their macroparasites in the Guanacaste Conservation Area (ACG), Costa Rica. NINA Report 635, 45 pp.
  • Thorbjarnarson JB, Mazzotti FJ, Sanderson E, Buitrago F, Lazcano M, Minkowski K, Muniz M, Ponce P, Sigler L, Soberon R, Trelancia AM, Velasco A (2006) Regional habitat conservation priorities for the American crocodile. Biological Conservation 128: 25–36.
  • Thorbjarnarson JB (2010) American Crocodile (Crocodylus acutus). In: Manolis SC, Stevenson C (Eds) Crocodiles. Status Survey and Conservation Action Plan, 3rd ed. Crocodile Specialist Group (Darwin), 46–53.
  • Van Oosterhout C, Hutchinson WF, Wills DPM, Shipley P (2004) MICRO-CHECKER: software for identifying and correcting genotyping errors in microsatellite data. Molecular Ecology Resources 4: 535–538.
  • Verdade LM, Zucoloto RB, Coutinho LL (2002) Microgeographic variation in Caiman latirostris. Journal of Experimental Zoology Part B 294: 387–396.

Appendix 1

Allele Frequencies.Download as CSV 

Allele frequencies for each locus in each Crocodylus acutus population studied in Pacific Costa Rica. The number in parenthesis indicates the sample size in that location.

Locus Allele LB (N=46) SR (N=17) PV (N=54) RT (N=17) ACOSA (N=49)
C391 139 0 0 0.009 0 0
141 0.022 0 0.093 0.088 0.02
143 0.011 0 0 0 0
147 0.022 0.029 0 0 0
149 0.065 0.059 0.028 0 0.041
151 0.033 0.206 0.037 0 0.02
153 0.728 0.559 0.611 0.794 0.602
155 0.022 0.029 0 0 0.031
157 0 0.029 0.167 0.088 0.265
159 0 0 0.009 0 0
161 0 0.088 0.046 0.029 0.01
163 0.098 0 0 0 0.01
Cj16 151 0 0 0 0 0.031
153 0 0.676 0.287 0.765 0.551
155 0 0 0.009 0 0.041
157 0 0 0 0 0.01
173 0 0 0.009 0.029 0
175 0.13 0.029 0.093 0.176 0.02
183 0 0 0.019 0 0.071
185 0.696 0.147 0.537 0 0.224
187 0.163 0.147 0.046 0.029 0.051
189 0.011 0 0 0 0
Cj18 195 0 0 0 0 0.02
197 0.011 0 0 0 0
199 0.054 0 0.157 0.088 0.071
201 0.739 0.029 0.231 0.147 0.133
203 0 0 0 0.176 0.041
205 0 0 0 0 0.01
215 0.043 0 0 0 0.163
217 0.054 0.059 0.046 0.029 0.112
219 0.011 0.206 0.111 0.088 0.01
221 0.054 0 0.019 0 0.041
223 0.033 0.706 0.389 0.471 0.204
225 0 0 0.037 0 0.031
227 0 0 0 0 0.092
229 0 0 0.009 0 0.071
Cj20 168 0.011 0 0 0 0.031
170 0.022 0.059 0.019 0.029 0.02
172 0.054 0.265 0.296 0.206 0.51
174 0.75 0.618 0.491 0.206 0.143
176 0 0.029 0.037 0.118 0
178 0.163 0.029 0.074 0.412 0.245
186 0 0 0.046 0 0
196 0 0 0 0 0.051
200 0 0 0 0.029 0
206 0 0 0.019 0 0
212 0 0 0.019 0 0
Cj109 364 0 0 0.065 0.029 0
366 0.054 0.176 0.343 0.412 0.214
368 0.511 0.324 0.194 0.059 0.551
370 0.141 0.235 0.231 0.471 0.204
372 0 0.029 0.037 0.029 0.01
374 0.293 0.235 0.12 0 0.01
376 0 0 0.009 0 0
378 0 0 0 0 0.01
Cj131 209 0 0.029 0 0 0
211 0.087 0 0.019 0.059 0.061
213 0.141 0.059 0.194 0.176 0.143
215 0.696 0.882 0.769 0.765 0.724
217 0.065 0.029 0 0 0.051
219 0.011 0 0.019 0 0
231 0 0 0 0 0.02
CU5-123 218 0 0 0.01 0 0.013
220 0 0 0.029 0 0
222 0.011 0 0 0 0
224 0.189 0 0.279 0 0.079
226 0.744 0.971 0.519 0.618 0.605
228 0.033 0.029 0.106 0 0.026
230 0.022 0 0.01 0 0
232 0 0 0 0 0.066
234 0 0 0.048 0.382 0.211
220 0 0 0.029 0 0
222 0.011 0 0 0 0
224 0.189 0 0.279 0 0.079
226 0.744 0.971 0.519 0.618 0.605
228 0.033 0.029 0.106 0 0.026
230 0.022 0 0.01 0 0
232 0 0 0 0 0.066
234 0 0 0.048 0.382 0.211
CUD68 105 0 0 0 0 0.031
121 0 0 0 0 0.02
123 0.054 0 0.009 0 0.051
125 0.261 0.294 0.389 0.029 0.173
127 0.348 0 0.139 0.147 0.367
129 0.337 0.412 0.231 0.353 0.265
131 0 0 0.231 0.471 0.092
133 0 0.294 0 0 0
CUJ131 141 0.011 0 0 0 0
155 0 0 0.019 0.059 0
171 0.272 0.088 0.009 0.265 0
175 0 0 0.009 0 0.01
179 0.011 0.059 0.009 0 0.02
181 0.337 0.5 0.389 0.059 0.582
183 0 0 0.028 0 0.031
185 0.37 0.353 0.528 0.618 0.357
187 0 0 0.009 0 0