No abstract

Rates of temperature change and thermal acclimation can alter measures of temperature tolerance. Using new experimental data on springtails and data from the literature, we show that these factors interact and have consequences for estimates of organismal vulnerability to climate change at global scales.

Ecologists often use indices or proxies to communicate complex ecological entities. Indices commonly known as thermal safety margin, habitat thermal quality and hours of restriction describe species’ vulnerability to climate change by comparing organisms’ thermal limits or preferences to available habitat temperatures. Ready access to temperature data, from global gridded datasets or limited in situ measurements, has made these indices popular for vulnerability assessments across taxonomic groups and regions. However, such coarse descriptions of thermal landscape mask the spatio‐temporal heterogeneity that organisms experience, compromising the value of these indices. Full understanding of how scale affects index estimates is lacking, leaving ecologists and conservation managers with little guidance for applying or interpreting indices. Here, we show that incomplete temperature sampling, in space or time, provides erroneous assessments of vulnerability. Gradually sub‐sampling a long‐term, fine‐scale dataset of operative environmental temperature altered the index estimates for a lizard. Uncertainty associated with the selection of data increased with coarser scales, often leading to contrasting interpretations about the species’ vulnerability to climate change when different data subsets were used. Compressing the environmental temperature data into central or extreme tendencies, as traditionally done to compute these indices, further masked the thermal variation that animals exploit to buffer warming. We suggest the use of improved index formulations that better describe temperature availability at scales that are appropriate to the study organism.

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Marginal marine deposits of the John Henry Member, Upper Cretaceous Straight Cliffs Formation, were deposited within a moderately high accommodation and high sediment supply setting that facilitated preservation of both transgressive and regressive marginal marine deposits. Complete transgressive–regressive cycles, comprising barrier island lagoonal transgressive deposits interfingered with regressive shoreface facies, are distinguished based on their internal facies architecture and bounding surfaces. Two main types of boundaries occur between the transgressive and regressive portions of each cycle: (i) surfaces that record the maximum regression and onset of transgression (bounding surface A); and (ii) surfaces that place deeper facies on top of shallower facies (bounding surface B). The base of a transgressive facies (bounding surface A) is defined by a process change from wave‐dominated to tide‐dominated facies, or a coaly/shelly interval indicating a shift from a regressive to a transgressive regime. The surface recording such a process change can be erosional or non‐erosive and conformable. A shift to deeper facies occurs at the base of regressive shoreface deposits along both flooding surfaces and wave ravinement surfaces (bounding surface B). These two main bounding surfaces and their subtypes generate three distinct transgressive–regressive cycle architectures: (i) tabular, shoaling‐upward marine parasequences that are bounded by flooding surfaces; (ii) transgressive and regressive unit wedges that thin basinward and landward, respectively; and (iii) tabular, transgressive lagoonal shales with intervening regressive coaly intervals. The preservation of transgressive facies under moderately high accommodation and sediment supply conditions greatly affects stratigraphic architecture of transgressive–regressive cycles. Acknowledging variation in transgressive–regressive cycles, and recognizing transgressive successions that correlate to flooding surfaces basinward, are both critical to achieving an accurate sequence stratigraphic interpretation of high‐frequency cycles.