Simulation of climate‐tick‐host‐landscape interactions: Effects of shifts in the seasonality of host population fluctuations on tick densities

Tick vector systems are comprised of complex climate‐tick‐host‐landscape interactions that are difficult to identify and estimate from empirical observations alone. We developed a spatially‐explicit, individual‐based model, parameterized to represent ecological conditions typical of the south‐central United States, to examine effects of shifts in the seasonal occurrence of fluctuations of host densities on tick densities. Simulated shifts in the seasonal occurrence of periods of high and low host densities affected both the magnitude of unfed tick densities and the seasonality of tick development. When shifting the seasonal densities of all size classes of hosts (small, medium, and large) synchronously, densities of nymphs were affected more by smaller shifts away from the baseline host seasonality than were densities of larval and adult life stages. When shifting the seasonal densities of only a single size‐class of hosts while holding other size classes at their baseline levels, densities of larval, nymph, and adult life stages responded differently. Shifting seasonal densities of any single host‐class earlier resulted in a greater increase in adult tick density than when seasonal densities of all host classes were shifted earlier simultaneously. The mean densities of tick life stages associated with shifts in host densities resulted from system‐level interactions of host availability with tick phenology. For example, shifting the seasonality of all hosts ten weeks earlier resulted in an approximately 30% increase in the relative degree of temporal co‐occurrence of actively host‐seeking ticks and hosts compared to baseline, whereas shifting the seasonality of all hosts ten weeks later resulted in an approximately 70% decrease compared to baseline. Differences among scenarios in the overall presence of active host‐seeking ticks in the system were due primarily to the degree of co‐occurrence of periods of high densities of unfed ticks and periods of high densities of hosts.     

Seasonal variation of Sargassum ilicifolium (Phaeophyceae) growth on equatorial coral reefs

Temporal and spatial variations in Sargassum ilicifolium thallus density and length were investigated on equatorial coral reefs in Singapore from November 2011 to October 2012. Thalli density varied little throughout the year, however, we found strong seasonal patterns in thallus length and identified temperature as the significant driver. Sargassum ilicifolium reached maximum length in December (110.39 ± 2.37 cm) during periods of cooler water temperatures, and minimum length in May (9.88 ± 0.48 cm) during periods of warmer water temperatures. Significant spatial variation was also observed for both thallus density and length of S. ilicifolium among reefs. Within reefs, densities of S. ilicifolium were higher on reef flats (20.40 ± 0.40 individuals · 0.25 m^?2) compared to upper reef slopes (5.66 ± 0.23 individuals · 0.25 m^?2). Our findings highlight that marked seasonality in the growth of canopy‐forming macroalgae can occur within equatorial reef systems where temperature ranges are restricted (<3°C).     

Double‐wavelet transform for multisubject task‐induced functional magnetic resonance imaging data

The goal of this article is to model multisubject task‐induced functional magnetic resonance imaging (fMRI) response among predefined regions of interest (ROIs) of the human brain. Conventional approaches to fMRI analysis only take into account temporal correlations, but do not rigorously model the underlying spatial correlation due to the complexity of estimating and inverting the high dimensional spatio‐temporal covariance matrix. Other spatio‐temporal model approaches estimate the covariance matrix with the assumption of stationary time series, which is not always feasible. To address these limitations, we propose a double‐wavelet approach for modeling the spatio‐temporal brain process. Working with wavelet coefficients simplifies temporal and spatial covariance structure because under regularity conditions, wavelet coefficients are approximately uncorrelated. Different wavelet functions were used to capture different correlation structures in the spatio‐temporal model. The main advantages of the wavelet approach are that it is scalable and that it deals with nonstationarity in brain signals. Simulation studies showed that our method could reduce false‐positive and false‐negative rates by taking into account spatial and temporal correlations simultaneously. We also applied our method to fMRI data to study activation in prespecified ROIs in the prefontal cortex. Data analysis showed that the result using the double‐wavelet approach was more consistent than the conventional approach when sample size decreased.     

Inter‐annual monitoring improves diversity estimation of tropical butterfly assemblages

Monitoring programs for diverse tropical butterfly assemblages are scarce, and temporal diversity patterns in these assemblages are poorly understood. We adopted an additive partitioning approach to determine how temporal butterfly species richness was structured at the levels of days, months, and years in five tropical/subtropical sites across three continents covering up to 9 years of monitoring. We found that observed butterfly richness was not uniformly distributed across temporal extents. Butterfly species composition differed across months and years, potentially accounting for the fact that temporal butterfly species richness contributed a high proportion to total species richness. We further examined how species richness of common and uncommon species (> and <0.5% of total abundance, respectively) were structured across temporal extents. The results showed that the common species relative contribution to total species richness was higher at lower‐temporal levels, whereas uncommon species contributed more at higher‐temporal resolutions. This suggests that long‐term sampling will be more effective in capturing patterns of rare species and the total species pool while lower‐temporal level sampling (e.g., daily or weekly) may be more useful in examining common species demographic patterns. We therefore encourage careful consideration of temporal replication at different extents in developing butterfly monitoring schemes. Long‐term monitoring is essential for improvement in the resolution of species estimation and diversity patterns for tropical ecosystems. Abstract in Chinese is available with online material.     

How does temporal variation in habitat connectivity influence metapopulation dynamics?

Metapopulation persistence depends on connectivity between habitat patches. While emphasis has been placed on the spatial dynamics of connectivity, much less has been placed on its short‐term temporal dynamics. In many terrestrial and aquatic ecosystems, however, transient (short‐term) changes in connectivity occur as habitat patches are connected and disconnected due, for example, to climatic or hydrological variability. We evaluated the implications of transient connectivity using a network‐based metapopulation model and a series of scenarios representing temporal changes in connectivity. The transient loss of connectivity can influence metapopulation persistence, and more strongly autocorrelated temporal dynamics affect metapopulation persistence more severely. Given that many ecosystems experience short‐term and temporary loss of habitat connectivity, it is important that these dynamics are adequately represented in metapopulation models; failing to do so may yield overly optimistic‐estimates of metapopulation persistence in fragmented landscapes.     

Structured Odorant Response Patterns across a Complete Olfactory Receptor Neuron Population

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