Circadian rhythm of preferred temperature in fish: Behavioural thermoregulation linked to daily photocycles in zebrafish and Nile tilapia.

Ectothermic vertebrates, e.g. fish, maintain their body temperature within a specific physiological range mainly through behavioural thermoregulation. Here, we characterise the presence of daily rhythms of thermal preference in two phylogenetically distant and well-studied fish species: the zebrafish (Danio rerio), an experimental model, and the Nile tilapia (Oreochromis niloticus), an aquaculture species. We created a non-continuous temperature gradient using multichambered tanks according to the natural environmental range for each species. Each species was allowed to freely choose their preferred temperature during the 24h cycle over a long-term period. Both species displayed strikingly consistent temporal daily rhythms of thermal preference with higher temperatures being selected during the second half of the light phase and lower temperatures at the end of the dark phase, with mean acrophases at Zeitgeber Time (ZT) 5.37 h (zebrafish) and ZT 12.5 h (tilapia). Interestingly, when moved to the experimental tank, only tilapia displayed consistent preference for higher temperatures and took longer time to establish the thermal rhythms. Our findings highlight the importance of integrating both light-driven daily rhythm and thermal choice to refine our understanding of fish biology and improve the management and welfare of the diversity of fish species used in research and food production.

Extending coverage and thematic resolution of compositional land cover maps in a hierarchical Bayesian framework.

Ecological models are constrained by the availability of high-quality data at biologically appropriate resolutions and extents. Modeling a species' affinity or aversion with a particular land cover class requires data detailing that class across the full study area. Data sets with detailed legends (i.e., high thematic resolution) and/or high accuracy often sacrifice geographic extent, while large-area data sets often compromise on the number of classes and local accuracy. Consequently, ecologists must often restrict their study extent to match that of the more precise data set, or ignore potentially key land cover associations to study a larger area. We introduce a hierarchical Bayesian model to capitalize on the thematic resolution and accuracy of a regional land cover data set, and on the geographic breadth of a large area land cover data set. For the full extent (i.e., beyond the regional data set), the model predicts systematic discrepancies of the large-area data set with the regional data set, and divides an aggregated class into two more specific classes detailed by the regional data set. We illustrate the application of our model for mapping eastern white pine (Pinus strobus) forests, an important timber species that also provides habitat for an invasive shrub in the northeastern United States. We use the National Land Cover Database (NLCD), which covers the full study area but includes only generalized forest classes, and the NH GRANIT land cover data set, which maps White Pine Forest and has high accuracy, but only exists within New Hampshire. We evaluate the model at coarse (20 km2 ) and fine (2 km2 ) resolutions, with and without spatial random effects. The hierarchical model produced improved maps of compositional land cover for the full extent, reducing inaccuracy relative to NLCD while partitioning a White Pine Forest class out of the Evergreen Forest class. Accuracy was higher with spatial random effects and at the coarse resolution. All models improved upon simply partitioning Evergreen Forest in NLCD based on the predicted distribution of white pine. This flexible statistical method helps ecologists leverage localized mapping efforts to expand models of species distributions, population dynamics, and management strategies beyond the political boundaries that frequently delineate land cover data sets.

Unusually large upward shifts in cold-adapted, montane mammals as temperature warms.

The largest and tallest mountain range in the contiguous United States, the Southern Rocky Mountains, has warmed considerably in the past several decades due to anthropogenic climate change. Herein we examine how 47 mammal elevational ranges (27 rodent and 4 shrew species) have changed from their historical distributions (1886-1979) to their contemporary distributions (post 2005) along 2,400-m elevational gradients in the Front Range and San Juan Mountains of Colorado. Historical elevational ranges were based on more than 4,580 georeferenced museum specimen and publication records. Contemporary elevational ranges were based on 7,444 records from systematic sampling efforts and museum specimen records. We constructed Bayesian models to estimate the probability a species was present, but undetected, due to undersampling at each 50-m elevational bin for each time period and mountain range. These models leveraged individual-level detection probabilities, the number and patchiness of detections across 50-m bands of elevation, and a decaying likelihood of presence from last known detections. We compared 95% likelihood elevational ranges between historical and contemporary time periods to detect directional change. Responses were variable as 26 mammal ranges changed upward, 6 did not change, 11 changed downward, and 4 were extirpated locally. The average range shift was 131 m upward, while exclusively montane species shifted upward more often (75%) and displayed larger average range shifts (346 m). The best predictors of upper limit and total directional change were species with higher maximum latitude in their geographic range, montane affiliation, and the study mountain was at the southern edge of their geographic range. Thus, mammals in the Southern Rocky Mountains serve as harbingers of more changes to come, particularly for montane, cold-adapted species in the southern portion of their ranges.