Analyze Diet
PloS one2012; 7(10); e47858; doi: 10.1371/journal.pone.0047858

Explaining spatial heterogeneity in population dynamics and genetics from spatial variation in resources for a large herbivore.

Abstract: Fine-scale spatial variation in genetic relatedness and inbreeding occur across continuous distributions of several populations of vertebrates; however, the basis of observed variation is often left untested. Here we test the hypothesis that prior observations of spatial patterns in genetics for an island population of feral horses (Sable Island, Canada) were the result of spatial variation in population dynamics, itself based in spatial heterogeneity in underlying habitat quality. In order to assess how genetic and population structuring related to habitat, we used hierarchical cluster analysis of water sources and an indicator analysis of the availability of important forage species to identify a longitudinal gradient in habitat quality along the length of Sable Island. We quantify a west-east gradient in access to fresh water and availability of two important food species to horses: sandwort, Honckenya peploides, and beach pea, Lathyrus japonicas. Accordingly, the population clusters into three groups that occupy different island segments (west, central, and east) that vary markedly in their local dynamics. Density, body condition, and survival and reproduction of adult females were highest in the west, followed by central and east areas. These results mirror a previous analysis of genetics, which showed that inbreeding levels are highest in the west (with outbreeding in the east), and that there are significant differences in fixation indices among groups of horses along the length of Sable Island. Our results suggest that inbreeding depression is not an important limiting factor to the horse population. We conclude that where habitat gradients exist, we can anticipate fine-scale heterogeneity in population dynamics and hence genetics.
Publication Date: 2012-10-31 PubMed ID: 23118900PubMed Central: PMC3485331DOI: 10.1371/journal.pone.0047858Google Scholar: Lookup
The Equine Research Bank provides access to a large database of publicly available scientific literature. Inclusion in the Research Bank does not imply endorsement of study methods or findings by Mad Barn.
  • Journal Article
  • Research Support
  • Non-U.S. Gov't

Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

The research explores the spatial variation in population dynamics and genetic relatedness in a population of feral horses on Sable Island, Canada. The study links these variations to differences in habitat quality along different island segments.

Research Overview

  • The researchers aimed to understand the effect of spatial variation in resources on population dynamics and genetics in large herbivores.
  • They used the population of feral horses in Sable Island, Canada as a case study.
  • The hypothesis proposed that the spatial patterns in genetics observed for these horses resulted from spatial variation in population dynamics, which in turn were influenced by the spatial heterogeneity in underlying habitat quality.

Methodology

  • They conducted a hierarchical cluster analysis of water sources as well as an indicator analysis of the availability of important forage species to identify longitudinal gradient in habitat quality along the length of Sable Island.
  • They identified the availability of two important food species for horses: the sandwort (Honckenya peploides), and the beach pea (Lathyrus japonicas).
  • Based on the habitat quality, the horse population was divided into three groups occupying different island segments (west, central and east) that varied markedly in their local dynamics.

Findings

  • The researchers noticed that the horse density, body condition, survival and reproduction of adult females were highest in the west of the island, followed by the central and eastern areas.
  • They correlated these findings with a previous analysis of genetics, which showed that inbreeding levels were highest in the west with outbreeding observed in the east, and there were significant differences in fixation indices among groups of horses along the length of Sable Island.
  • Interestingly, the results suggested that inbreeding depression was not a significant limiting factor to the horse population.

Conclusion

  • The research concluded that in environments where habitat gradients exist, variations in population dynamics and genetics at micro levels can be anticipated.
  • These findings could have applications in understanding other large herbivore populations and their genetic and population dynamics based on resource availability and habitat quality.

Cite This Article

APA
Contasti AL, Tissier EJ, Johnstone JF, McLoughlin PD. (2012). Explaining spatial heterogeneity in population dynamics and genetics from spatial variation in resources for a large herbivore. PLoS One, 7(10), e47858. https://doi.org/10.1371/journal.pone.0047858

Publication

ISSN: 1932-6203
NlmUniqueID: 101285081
Country: United States
Language: English
Volume: 7
Issue: 10
Pages: e47858
PII: e47858

Researcher Affiliations

Contasti, Adrienne L
  • Department of Biology, University of Saskatchewan, Saskatoon, Saskatchewan, Canada.
Tissier, Emily J
    Johnstone, Jill F
      McLoughlin, Philip D

        MeSH Terms

        • Animals
        • Canada
        • Ecosystem
        • Female
        • Genetic Variation
        • Herbivory / genetics
        • Herbivory / physiology
        • Horses / genetics
        • Horses / physiology
        • Inbreeding
        • Microsatellite Repeats
        • Population Density
        • Population Dynamics
        • Water

        Conflict of Interest Statement

        The authors have declared that no competing interests exist.

        References

        This article includes 52 references
        1. Coulson T, Albon S, Guinness F, Pemberton J, Clutton-Brock T. Population substructure, local density, and calf winter survival in red deer (Cervus elaphus). Ecology 78: 852–863.
        2. Coltman DW, Pilkington JG, Pemberton JM. Fine-scale genetic structure in a free-living ungulate population.. Mol Ecol 2003 Mar;12(3):733-42.
        3. Nussey DH, Coltman DW, Coulson T, Kruuk LE, Donald A, Morris SJ, Clutton-Brock TH, Pemberton J. Rapidly declining fine-scale spatial genetic structure in female red deer.. Mol Ecol 2005 Oct;14(11):3395-405.
        4. Brazeau DA, Sammarco PW, Atchison AD. Micro-scale genetic heterogeneity and structure in coral recruitment: fine-scale patchiness. Aquat Biol 12: 55–67.
        5. Cullingham CI, Merrill EH, Pybus MJ, Bollinger TK, Wilson GA, Coltman DW. Broad and fine-scale genetic analysis of white-tailed deer populations: estimating the relative risk of chronic wasting disease spread.. Evol Appl 2011 Jan;4(1):116-31.
        6. Stopher KV, Walling CA, Morris A, Guinness FE, Clutton-Brock TH, Pemberton JM, Nussey DH. Shared spatial effects on quantitative genetic parameters: accounting for spatial autocorrelation and home range overlap reduces estimates of heritability in wild red deer.. Evolution 2012 Aug;66(8):2411-26.
        7. Chesser RK. Relativity of behavioral interactions in socially structured populations. J Mammal 79: 713–724.
        8. Veran S, Beissinger SR. Demographic origins of skewed operational and adult sex ratios: perturbation analyses of two-sex models.. Ecol Lett 2009 Feb;12(2):129-43.
        9. Fitze PS, Le Galliard JF. Inconsistency between different measures of sexual selection.. Am Nat 2011 Aug;178(2):256-68.
          pubmed: 21750388doi: 10.1086/660826google scholar: lookup
        10. Chesser RK. Gene diversity and female philopatry.. Genetics 1991 Feb;127(2):437-47.
          pmc: PMC1204371pubmed: 2004714doi: 10.1093/genetics/127.2.437google scholar: lookup
        11. Thouless CR, Guinness FE. Conflict between red deer hinds: the winner always wins. Anim Behav 34: 1166–1171.
        12. Fortin D, Morris DW, McLoughlin PD. Adaptive habitat selection and the evolution of specialists in heterogeneous environments. Isr J Ecol Evol 54: 311–328.
        13. Lucas ZL, McLoughlin PD, Coltman DW, Barber C. Multiscale analysis reveals restricted gene flow and a linear gradient in heterozygosity for an island population of feral horses. Can J Zool 87: 1–7.
        14. Wright S. The interpretation of population structure by F-statistics with special regard to systems of mating. Evolution 19: 395–420.
        15. Weir BS, Cockerham CC. ESTIMATING F-STATISTICS FOR THE ANALYSIS OF POPULATION STRUCTURE.. Evolution 1984 Nov;38(6):1358-1370.
        16. Contasti AL. Structure in vital rates, internal source-sink dynamics, and their influence on current population expansion for the feral horses (Equus ferus caballus) of Sable Island, Nova Scotia. .
        17. Environment Canada. National climate data and information archive. .
        18. Catling PM, Freedman B, Lucas Z. The vegetation and phytogeography of Sable Island, Nova Scotia. PNSIS 34: 181–247.
        19. Stalter R, Lamont E. The historical and extant flora of Sable Island, Nova Scotia, Canada. J Torrey Bot Soc 133: 362–374.
        20. Tissier EJ. Vegetation associations along disturbance gradients on the sand dunes of Sable Island, Nova Scotia. .
        21. Freedman B, Catling PM, Lucas ZL. Effects of feral horses on vegetation of Sable Island, Nova Scotia. Can Field-Nat 125: 200–212.
        22. Welsh DA. Population, behavioural, and grazing ecology of the horses of Sable Island, Nova Scotia. .
        23. Christie BJ. The Horses of Sable Island. .
        24. Plante Y, Vega-Pla JL, Lucas Z, Colling D, de March B, Buchanan F. Genetic diversity in a feral horse population from Sable Island, Canada.. J Hered 2007 Sep-Oct;98(6):594-602.
          pubmed: 17855732doi: 10.1093/jhered/esm064google scholar: lookup
        25. Prystupa JM, Juras R, Cothran EG, Buchanan FC, Plante Y. Genetic diversity and admixture among Canadian, Mountain and Moorland and Nordic pony populations.. Animal 2012 Jan;6(1):19-30.
          pubmed: 22436150doi: 10.1017/s1751731111001212google scholar: lookup
        26. Prystupa JM, Hind P, Cothran EG, Plante Y. Maternal lineages in native Canadian equine populations and their relationship to the Nordic and Mountain and Moorland pony breeds.. J Hered 2012 May-Jun;103(3):380-90.
          pubmed: 22504109doi: 10.1093/jhered/ess003google scholar: lookup
        27. Carroll CL, Huntington PJ. Body condition scoring and weight estimation of horses.. Equine Vet J 1988 Jan;20(1):41-5.
        28. Rubenstein DI. Behavioural ecology of island feral horses. Equine Vet J 13: 27–34.
        29. Berger J. Wild Horses of the Great Basin. .
        30. Ritter RC, Bednekoff PA. Dry season water, female movements and male territoriality in springbok: preliminary evidence of waterhole-directed sexual selection. Afr J Ecol 33: 395–404.
        31. Chamaillé-Jammes S, Valeix M, Fritz H. Managing heterogeneity in elephant distribution: interactions between elephant population density and surface-water availability. J Appl Ecol 44: 625–633.
        32. McCune B, Grace JB. Analysis of Ecological Communities. .
        33. Kenkel NC. On selecting an appropriate multivatriate analysis. Can J Plant Sci 86: 663–667.
        34. Mauritzen M, Derocher AE, Wiig Ø, Belikov SE, Boltunov AN. Using satellite telemetry to define spatial population structure in polar bears in the Norwegian and western Russian Arctic. J Appl Ecol 39: 79–90.
        35. van Sickle J. Using mean similarity dendrograms to evaluate classifications. J Agric Biol Environ Stat 2: 370–388.
        36. Dufrêne M, Legendre P. Species assemblages and indicator species: The need for a flexible asymmetrical approach. Ecol Monogr 67: 345–366.
        37. nRoberts DW. labdsv: Ordination and multivariate analysis for ecology. .
        38. R Development Core Team. R: A language and environment for statistical computing. .
        39. Caswell H. Matrix population models construction, analysis, and interpretation. .
        40. Morris WF, Doak DF. Quantitative Conservation Biology theory and practice of population viability analysis. .
        41. Venables WN, Ripley BD. Modern Applied Statistics with S. Fourth Edition. .
        42. Virgl JA, Messier F. Assessment of source-sink theory for predicting demographic rates among habitats that exhibit temporal changes in quality. Can J Zool 78: 1483–1493.
        43. Siegel S, Castellan NJ Jr. Nonparametric statistics for the behavioural sciences. .
        44. Gadagkar R, Joshi NV. Quantitative ethology of social wasps: time-activity budgets and caste differentiation in Ropalidia marginata (Lep.) (Hymenoptera: vespidae). Anim Behav 31: 26–31.
        45. Wittemyer G, Douglas-Hamilton I, Getz WM. The socioecology of elephants: analysis of the processes creating multitiered social structures. Anim Behav 69: 1357–1371.
        46. Weise MJ, Harvey JT, Costa DP. The role of body size in individual-based foraging strategies of a top marine predator.. Ecology 2010 Apr;91(4):1004-15.
          pubmed: 20462115doi: 10.1890/08-1554.1google scholar: lookup
        47. Edwards MA, Nagy JA, Derocher AE. Using subpopulation structure for barren-ground grizzly bear management. Ursus 19: 91–104.
        48. Fretwell DS, Lucas HL. On territorial behavior and other factors influencing habitat distribution in birds. Acta Biotheor 19: 16–32.
        49. Pulliam HR. Sources, sinks, and population regulation. Am Nat 132: 652–661.
        50. Clutton-Brock TH. Female transfer and inbreeding avoidance in social mammals.. Nature 1989 Jan 5;337(6202):70-2.
          pubmed: 2909891doi: 10.1038/337070a0google scholar: lookup
        51. Monard A-M, Duncan P, Boy V. The proximate mechanisms of natal dispersal in female horses. Behaviour 133: 1095–1124.
        52. Cameron EZ, Linklater WL. Extreme sex ratio variation in relation to change in condition around conception.. Biol Lett 2007 Aug 22;3(4):395-7.
          pmc: PMC2390657pubmed: 17439844doi: 10.1098/rsbl.2007.0089google scholar: lookup

        Citations

        This article has been cited 7 times.
        1. Manning JA, McLoughlin PD. Environmental and demographic drivers of male mating success vary across sequential reproductive episodes in a polygynous breeder. Ecol Evol 2019 May;9(9):5106-5117.
          doi: 10.1002/ece3.5066pubmed: 31110665google scholar: lookup
        2. Gold S, Regan CE, McLoughlin PD, Gilleard JS, Wilson AJ, Poissant J. Quantitative genetics of gastrointestinal strongyle burden and associated body condition in feral horses. Int J Parasitol Parasites Wildl 2019 Aug;9:104-111.
          doi: 10.1016/j.ijppaw.2019.03.010pubmed: 31011533google scholar: lookup
        3. Debeffe L, Poissant J, McLoughlin PD. Individual quality and age but not environmental or social conditions modulate costs of reproduction in a capital breeder. Ecol Evol 2017 Aug;7(15):5580-5591.
          doi: 10.1002/ece3.3082pubmed: 28811876google scholar: lookup
        4. Richard E, Simpson SE, Medill SA, McLoughlin PD. Interacting effects of age, density, and weather on survival and current reproduction for a large mammal. Ecol Evol 2014 Oct;4(19):3851-60.
          doi: 10.1002/ece3.1250pubmed: 25614799google scholar: lookup
        5. Marjamäki PH, Contasti AL, Coulson TN, McLoughlin PD. Local density and group size interacts with age and sex to determine direction and rate of social dispersal in a polygynous mammal. Ecol Evol 2013 Sep;3(9):3073-82.
          doi: 10.1002/ece3.694pubmed: 24101995google scholar: lookup
        6. Ahn S, Redman EM, Gavriliuc S, Bellaw J, Gilleard JS, McLoughlin PD, Poissant J. Mixed strongyle parasite infections vary across host age and space in a population of feral horses. Parasitology 2024 Oct;151(12):1299-1316.
          doi: 10.1017/S0031182024001185pubmed: 39663810google scholar: lookup
        7. Stothart MR, McLoughlin PD, Medill SA, Greuel RJ, Wilson AJ, Poissant J. Methanogenic patterns in the gut microbiome are associated with survival in a population of feral horses. Nat Commun 2024 Jul 22;15(1):6012.
          doi: 10.1038/s41467-024-49963-xpubmed: 39039075google scholar: lookup