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The sea louse is considered an important ectoparasite that affects farmed salmonids around the world. Sea lice control relies heavily on pharmacological treatments in several salmon-producing countries, including Chile. Among options for drug administration, immersion treatments represent the majority of antiparasitic control strategies used in Chile. As a topical procedure, immersion treatments do not induce a long lasting effect; therefore, re-infestation from neighbouring farms may undermine their efficacy. Synchronization of treatments has been proposed as a strategy to improve immersion treatment performance, but it has not been evaluated so far. Using a repeated-measures linear mixed-effect model, we evaluated the impact of treatment synchronization of neighbouring farms (within 10km seaway distance) on the adult lice mean abundance from weeks 2 to 8 post-treatment on rainbow trout and Atlantic salmon farms in Chile, while controlling for external and internal sources of lice before the treatments, and also for environmental and fish-related variables. Results indicate that treatment synchronization was significantly associated with lower adult lice levels from weeks 5 to 7 after treatment. This relationship appeared to be linear, suggesting that higher levels of synchronization may result in lower adult sea lice levels during these weeks. These findings suggest that synchronization can improve the performance of immersion delousing treatments by keeping sea lice levels low for a longer period of time. Our results may be applicable to other regions of the world where immersion treatments are widely used.

Sea lice infestation levels on wild chum and pink salmon in the Broughton Archipelago region are known to vary spatially and temporally; however, the locations of areas associated with a high infestation level had not been investigated yet. In the present study, the multivariate spatial scan statistic based on a Poisson model was used to assess spatial clustering of elevated sea lice (Caligus clemensi and Lepeophtheirus salmonis) infestation levels on wild chum and pink salmon sampled between March and July of 2004 to 2012 in the Broughton Archipelago and Knight Inlet regions of British Columbia, Canada. Three covariates, seine type (beach and purse seining), fish size, and year effect, were used to provide adjustment within the analyses. The analyses were carried out across the five months/datasets and between two fish species to assess the consistency of the identified clusters. Sea lice stages were explored separately for the early life stages (non-motile) and the late life stages of sea lice (motile). Spatial patterns in fish migration were also explored using monthly plots showing the average number of each fish species captured per sampling site. The results revealed three clusters for non-motile C. clemensi, two clusters for non-motile L. salmonis, and one cluster for the motile stage in each of the sea lice species. In general, the location and timing of clusters detected for both fish species were similar. Early in the season, the clusters of elevated sea lice infestation levels on wild fish are detected in areas closer to the rivers, with decreasing relative risks as the season progresses. Clusters were detected further from the estuaries later in the season, accompanied by increasing relative risks. In addition, the plots for fish migration exhibit similar patterns for both fish species in that, as expected, the juveniles move from the rivers toward the open ocean as the season progresses The identification of space-time clustering of infestation on wild fish from this study can help in targeting investigations of factors associated with these infestations and thereby support the development of more effective sea lice control measures.

Chronic wasting disease (CWD) is a fatal disease of North American cervids that was first detected in a wild, hunter-shot deer in Saskatchewan along the border with Alberta in Canada in 2000. Spatially explicit models for assessing factors affecting disease detection are needed to guide surveillance and control programs. Spatio-temporal patterns in CWD prevalence can be complicated by variation in individual infection probability and sampling biases. We assessed hunter harvest data of mule deer (Odocoileus hemionus) and white-tailed deer (Odocoileus virginianus) during the early phases of an outbreak in Saskatchewan (i.e., 2002-2007) for targeting the detection of CWD by defining (1) where to look, and (2) how much effort to use. First, we accounted for known demographic heterogeneities in infection to model the probability, P(E), that a harvested deer was infected with CWD given characteristics of the harvest location. Second, in areas where infected deer were harvested we modelled the probability, P(D), of the hunter harvest re-detecting CWD within sample units of varying size (9-54 km(2)) given the demographics of harvested deer and time since first detection in the study area. Heterogeneities in host infection were consistent with those reported elsewhere: mule deer 3.7 timeswhite-tailed deer, males 1.8 timesfemales, and quadratically related to age in both sexes. P(E) increased with number of years since the first detection in our study area (2002) and proximity to known disease sources, and also varied with distance to the South Saskatchewan River and small creek drainages, terrain ruggedness, and extent of agriculture lands within a 3 km radius of the harvest. The majority (75%) of new CWD-positive deer from our sample were found within 20 km of infected deer harvested in the previous year, while approximately 10% were greater than 40 km. P(D) modelled at 18 km(2) was best supported, but for all scales, P(D) depended on the number of harvested deer and time since the first infected deer was harvested. Within an 18 km(2) sampling unit, there was an 80% probability of detecting a CWD-positive deer with 16 harvested deer five years after the initial infected harvest. Identifying where and how much to sample to detect CWD can improve targeted surveillance programs early in the outbreak of the disease when based on hunter harvest.

The decline of fisheries over recent decades and a growing human population has coincided with an increase in aquaculture production. As farmed fish densities increase, so have their rates of infectious diseases, as predicted by the theory of density-dependent disease transmission. One of the pathogen that has increased with the growth of salmon farming is sea lice. Effective management of this pathogen requires an understanding of the spatial scale of transmission. We used a two-part multi-scale model to account for the zero-inflated data observed in weekly sea lice abundance levels on rainbow trout and Atlantic salmon farms in Chile, and to assess internal (farm) and external (regional) sources of sea lice infection. We observed that the level of juvenile sea lice was higher on farms that were closer to processing plants with fish holding facilities. Further, evidence for sea lice exposure from the surrounding area was supported by a strong positive correlation between the level of juvenile sea lice on a farm and the number of gravid females on neighboring farms within 30 km two weeks prior. The relationship between external sources of sea lice from neighboring farms and juvenile sea lice on a farm was one of the strongest detected in our multivariable model. Our findings suggest that the management of sea lice should be coordinated between farms and should include all farms and processing plants with holding facilities within a relatively large geographic area. Understanding the contribution of pathogens on a farm from different sources is an important step in developing effective control strategies.

Landscape barriers influence movement patterns of animals, which in turn, affect spatio-temporal spread of infectious wildlife disease. We compare genetic data from computer simulations to those acquired from field samples to measure the effect of a landscape barrier on raccoon (Procyon lotor) movement, enabling risk assessment of raccoon rabies disease spread across the Niagara River from New York State into Ontario, an area currently uninfected by rabies. An individual-based spatially explicit model is used to simulate the expansion of a raccoon population to cross the Niagara River, for different permeabilities of the river to raccoon crossings. Since the model records individual raccoon genetics, the genetic population structure of neutral mitochondrial DNA haplotypes are characterised in the expanding population, every 25 years, using a genetic distance measure, phi ST, Mantel tests and a gene diversity measure. The river barrier effect is assessed by comparing genetic measures computed from model outputs to those calculated from 166 raccoons recently sampled from the same landscape. The "best fit" between modelled scenarios and field data indicate the river prevents 50% of attempts to cross the river. Founder effects dominated the colonizing genetic population structure, and, as the river barrier effect increased, its genetic diversity decreased. Using gene flow to calibrate the effect of the river as a barrier to movement provides an estimate of the effect of a river in reducing the likelihood of cross-river infection. Including individual genetic markers in simulation modelling benefits investigations of disease spread and control.