气候变化领域集成服务门户

Understanding seasonal patterns of activity, or phenology, of vector species is fundamental to determining seasonality of disease risk and epidemics of vector-borne disease. Spatiotemporal variation in abiotic conditions can influence variation in phenological patterns and life history events, which can dramatically influence the ecological role and human impact of a species. For arthropod vectors of human diseases such as ticks, these phenological patterns determine human exposure risk, yet how abiotic conditions interact to determine suitable conditions for host-seeking of vector species is difficult to disentangle. Here, we use MaxEnt to model spatial patterns and differences in host-seeking phenology of the western blacklegged tick (Ixodes pacificus) in California using spatially and temporally refined adult tick occurrence data and similarly refined climate and environmental data. We empirically validate the model using phenological data from field studies conducted at sites across California's latitudinal gradient. We find adult tick host-seeking activity varies substantially throughout the year, as well as across the large latitudinal gradient in the state. Suitable conditions for host-seeking are found earlier in fall and later in the spring in northern than in southern California. These seasonal patterns are primarily associated with monthly precipitation, minimum winter temperature, and winter precipitation, with maximum monthly temperature possibly playing a more prominent role in limiting host-seeking activity earlier in the spring in southern than northern California. Synthesis and applications. Modelling the seasonal activity of the western blacklegged tick, we find both a longer window for host-feeding and more protracted risk of human exposure to this vector species in northern than southern California. We further identify key environmental factors associated with these patterns, including precipitation and temperature that are otherwise challenging to elucidate in field and laboratory studies over large spatial scales. Moreover, we illustrate how species distribution models, in combination with temporally refined species occurrence and environmental data, can be used to investigate environmental factors predictive of geographic variation in seasonality or phenology of vector species. This produces not only novel ecological insight, but key information for public health practitioners in managing vector-borne disease transmission and targeting public outreach and interventions.