Using fine‐scale spatial genetics of Norway rats to improve control efforts and reduce leptospirosis risk in urban slum environments

Abstract

The Norway rat (Rattus norvegicus) is a key pest species globally and responsible for seasonal outbreaks of the zoonotic bacterial disease leptospirosis in the tropics. The city of Salvador, Brazil, has seen recent and dramatic increases in human population residing in slums, where conditions foster high rat density and increasing leptospirosis infection rates. Intervention campaigns have been used to drastically reduce rat numbers. In planning these interventions, it is important to define the eradication units ‐ the spatial scale at which rats constitute continuous populations and from where rats are likely recolonizing, post‐intervention. To provide this information, we applied spatial genetic analyses to 706 rats collected across Salvador and genotyped at 16 microsatellite loci. We performed spatially explicit analyses and estimated migration levels to identify distinct genetic units and landscape features associated with genetic divergence at different spatial scales, ranging from valleys within a slum community to city‐wide analyses. Clear genetic breaks exist between rats not only across Salvador but also between valleys of slums separated by <100 m—well within the dispersal capacity of rats. The genetic data indicate that valleys may be considered separate units and identified high‐traffic roads as strong impediments to rat movement. Migration data suggest that most (71–90%) movement is contained within valleys, with no clear source population contributing to migrant rats. We use these data to recommend eradication units and discuss the importance of carrying out individual‐based analyses at different spatial scales in urban landscapes.

Interplay between historical and current features of the cityscape in shaping the genetic structure of the house mouse (Mus musculus domesticus) in Dakar (Senegal, West Africa)

“…Beyond this general trend, spatially explicit analyses have also revealed sharp genetic discontinuities at relatively small spatial scales (Combs et al, 2018b;Richardson et al, 2017). Such genetic breaks have been attributed to physical barriers to dispersal (waterway in Baltimore, USA -Gardner-Santana et al, 2009; roads and topography in Pau de Lima, Salvador, Brazil -Kajdacsi et al, 2013;Richardson et al, 2017) or spatial heterogeneity in habitat quality that impacts local population size (resource availability varying with socioeconomic features of urban infrastructures, Manhattan, USA -Combs et al, 2018b).…”

“…Invasive rats and mice are archetypal commensals and a major concern for public health, food security and biodiversity all around the world (Angel et al, 2009;Meerburg et al, 2009;Parsons et al, 2017;Singleton et al, 2003Singleton et al, , 2015. Rodent control actions are costly, and classical control protocols, relying mostly on poisoning, have proven to be quite inefficient in reducing urban rodent densities in the long term (Parsons et al, 2017;Richardson et al, 2017). It is now recognized that the development of more efficient management strategies requires a better understanding of urban rodent evolutionary ecology (Byers et al, 2019;Combs et al, 2018a;Feng and Himsworth, 2014;Makundi and Massawe, 2011;Parsons et al, 2017;Singleton et al, 1999) and notably of variations in gene flow (Combs et al, 2018a,b;Gardner-Santana et al, 2009;Johnson and Munshi-South, 2017;Kajdacsi et al, 2013;Parsons et al, 2017;Richardson et al, 2017).…”

“…Rodent control actions are costly, and classical control protocols, relying mostly on poisoning, have proven to be quite inefficient in reducing urban rodent densities in the long term (Parsons et al, 2017;Richardson et al, 2017). It is now recognized that the development of more efficient management strategies requires a better understanding of urban rodent evolutionary ecology (Byers et al, 2019;Combs et al, 2018a;Feng and Himsworth, 2014;Makundi and Massawe, 2011;Parsons et al, 2017;Singleton et al, 1999) and notably of variations in gene flow (Combs et al, 2018a,b;Gardner-Santana et al, 2009;Johnson and Munshi-South, 2017;Kajdacsi et al, 2013;Parsons et al, 2017;Richardson et al, 2017). For instance, identifying genetic clusters can allow defining meaningful spatial units for control actions (Combs et al, 2018a;Kajdacsi et al, 2013;Richardson et al, 2017).…”

“…It is now recognized that the development of more efficient management strategies requires a better understanding of urban rodent evolutionary ecology (Byers et al, 2019;Combs et al, 2018a;Feng and Himsworth, 2014;Makundi and Massawe, 2011;Parsons et al, 2017;Singleton et al, 1999) and notably of variations in gene flow (Combs et al, 2018a,b;Gardner-Santana et al, 2009;Johnson and Munshi-South, 2017;Kajdacsi et al, 2013;Parsons et al, 2017;Richardson et al, 2017). For instance, identifying genetic clusters can allow defining meaningful spatial units for control actions (Combs et al, 2018a;Kajdacsi et al, 2013;Richardson et al, 2017). As anticoagulant resistance can rapidly reach high frequency within local rodent populations (Berny et al, 2018), knowledge on spatial patterns of gene flow can also help in predicting the risk at which resistance alleles can spread across a cityscape (Haniza et al, 2015).…”

A spatial genomic approach identifies time lags and historical barriers to gene flow in a rapidly fragmenting Appalachian landscape

“…These methods use spatial autocorrelation to tease apart patterns of inter-vs. intrapopulation genetic variation, improving the identification of population structure at fine geographical scales (Galpern, Manseau, & Wilson, 2012). When coupled with genomic data, spatially informed analyses may also allow for the detection of weak spatial structure related to recent habitat fragmentation or incomplete barriers to migration (Combs, Byers, et al, 2018;Combs, Puckett, Richardson, Mims, & Munshi-South, 2018;Combs, Puckett, et al, 2018;Richardson et al, 2016Richardson et al, , 2017. However, alongside these methodological improvements, work remains to understand the amount of individual-level genomic data necessary to assess spatial genetic patterns (e.g., McCartney-Melstad et al, 2018), and the effects of genomic data quality on the resolution of recently evolved population structure.…”