Afterword: Tough Questions; Hard Problems; Incremental Progress

The papers in this issue have specific and focused targets that are essential for making incremental progress on the very difficult problem of identifying the coevolutionary interactions of cognition and culture. The purpose of this paper is to discern the shape of a few of the large problems that loom over these more narrowly focussed papers, and to explain and assess the ways these papers contribute to their solution. The background problems described are (a) the character of the selective interactions between the evolution of culture and of cognition; (b) the special features of cumulative cultural evolution; and (c) the place of language in an account of cognition–culture coevolution. The paper ends with some reflections on the extraordinarily difficult challenge of testing scenarios in this field.     

Differences in spatial versus temporal reaction norms for spring and autumn phenological events

For species to stay temporally tuned to their environment, they use cues such as the accumulation of degree-days. The relationships between the timing of a phenological event in a population and its environmental cue can be described by a population-level reaction norm. Variation in reaction norms along environmental gradients may either intensify the environmental effects on timing (cogradient variation) or attenuate the effects (countergradient variation). To resolve spatial and seasonal variation in species’ response, we use a unique dataset of 91 taxa and 178 phenological events observed across a network of 472 monitoring sites, spread across the nations of the former Soviet Union. We show that compared to local rates of advancement of phenological events with the advancement of temperature-related cues (i.e., variation within site over years), spatial variation in reaction norms tend to accentuate responses in spring (cogradient variation) and attenuate them in autumn (countergradient variation). As a result, among-population variation in the timing of events is greater in spring and less in autumn than if all populations followed the same reaction norm regardless of location. Despite such signs of local adaptation, overall phenotypic plasticity was not sufficient for phenological events to keep exact pace with their cues—the earlier the year, the more did the timing of the phenological event lag behind the timing of the cue. Overall, these patterns suggest that differences in the spatial versus temporal reaction norms will affect species’ response to climate change in opposite ways in spring and autumn.

To stay tuned to their environment, species need to respond to both short- and long-term variation in climatic conditions. In temperate regions, favorable abiotic conditions, key resources, and major enemies may all occur early in a warm year, whereas they may occur late in a cold year. Coinciding with such factors may thus come with pronounced effects on individual fitness and population-level performance (1–4). As phenological traits also show substantial variability within and among populations, they can be subject to selection in nature (5–7), potentially resulting in patterns of local adaptation (8–10).At present, the rapid rate of global change is causing shifts in species phenology across the globe (11–13). Of acute interest is the extent to which different events are shifting in unison or not, sometimes creating seasonal mismatches and functionally disruptive asynchrony (3, 14–16). If much of the temporal and spatial variation in seasonal timing is a product of phenotypic plasticity, then changes can be instant, and sustained synchrony among interaction partners will depend on the extent to which different species react similarly to short-term variation in climatic conditions. If geographic variation in phenology reflects local adaptive evolutionary differentiation, then, in the short term, as climate changes, phenological interactions may be disrupted due to the lag as adaptation tries to catch up (17–19). By assuming that space can substitute time, it is possible to make inference about the role that adaptation to climate may play. How well species stay in synchrony will then depend on the extent to which local selective forces act similarly or differently on different species and events.Local adaptation in phenology may take two forms. 1) The magnitude of phenological change might vary along environmental gradients in ways that intensify the environmental effects on phenological traits, a process known as cogradient variation (Fig. 1B). In such a case, the covariance between the genetic influences on phenological traits and the environmental influences is positive. Under this scenario, the effect of environmental variation over space and time will be larger than if all populations were to follow the same reaction norm regardless of location. 2) Genotypes might counteract environmental effects, thereby diminishing the change in mean trait expression across the environmental gradient. In such a case, the effect of environmental variation over space and time will be smaller than if all populations were to follow the same reaction norm regardless of location. This latter scenario, termed countergradient variation, occurs when genetic and environmental influences on phenotypic traits oppose one another (Fig. 1C) (20, 21).Open in a separate windowFig. 1.Schematic illustration showing slopes of phenology on temperature. Adapted with permission from ref. 30. A corresponds to phenological plasticity with respect to temperature and no local adaptation. B reveals phenological plasticity with respect to temperature plus cogradient local adaptation. C reveals phenological plasticity with respect to temperature plus countergradient local adaptation. For each scenario, we have included two examples of events showing this type of pattern in our data. For the exact climatic cues related to these biotic events, see SI Appendix, Table S1. In each plot, the red lines correspond to the within-population reaction norms through time (i.e., temporal slopes within locations), and the blue line corresponds to the between-population reaction norm (i.e., spatial slopes). If all populations respond alike, then the same reaction norm will apply across all locations, and individuals will respond in the same way to the cue no matter where they were, and no matter whether we examine responses within or between locations. If this was the case, then the reaction norm would be the same within (red lines) and between locations, and the blue and the red slopes would be parallel (i.e., their slopes identical). This scenario is depicted in A. What we use as our estimate of local adaptation is the difference between the two, i.e., whether the slope of reaction norms within populations differs from that across populations. If the temporal slopes are estimated at a relatively short time scale (as compared to the generation length of the focal organisms), then we can assume that within-location variation in the timing of the event reflects phenotypic responses alone, not evolutionary change over time. This component is then, per definition, due to phenotypic plasticity as such, i.e., to how individuals of a constant genetic makeup respond to annual variation in their environment. By comparison, the spatial slope (i.e., the blue line) is a sum of two parts: first, it reflects the mean of how individuals of a constant genetic makeup respond to annual variation in their environment, i.e., the temporal reaction norm defined above. These means are shown by the red dots in A–C. However, second, if populations differentiate across sites, then we will see variation in their response to long-term conditions, with an added element in the spatial slope reflecting mean plasticity plus local adaptation. Therefore, if the spatial slope differs from the temporal slope, this reveals local adaptation (see Materials and Methods for further details). Such local adaptation in phenological response may take two forms. 1) The magnitude of phenological change might vary along environmental gradients in ways that intensify the environmental effects on phenological traits, a process known as cogradient variation (Fig. 1B). In such a case, the covariance between the genetic influences on phenological traits and the environmental influences is positive. Under this scenario, variation in the environmental cue over space and time will cause larger variation in phenological timing than if all populations were to follow the same reaction norm regardless of location. 2) Genotypes might counteract environmental effects, thereby diminishing the change in mean trait expression across the environmental gradient. In such a case, the effect of variation in the environmental cue over space and time will be smaller than if all populations were to follow the same reaction norm regardless of location. This latter scenario, termed countergradient variation, occurs when genetic and environmental influences on phenotypic traits oppose one another (C).For phenology, the overall prevalence of co- versus countergradient patterns is crucial, as it will dictate the extent to which local adaptation will either accentuate or attenuate phenological responses to temporal shifts in climate (10). Across environmental gradients in space, the relative prevalence of counter- versus cogradient variation in spring versus autumn will critically modify how climatic variation affects the length of the activity period of the entire ecological community. Overall, geographic variation in the activity period will be maximized when events in autumn and spring differ in terms of whether they adhere to patterns of co- or countergradient variation.Although the study of individual species and local species communities has revealed fine-tuning of species to local conditions (22), and a wealth of studies report shifts in phenology worldwide (23), we still lack a general understanding of how the two tie together: how strong is local adaptation in the timing of events, and how do they vary across the season? Here, a major hurdle to progress has been a skew in the focus of past studies: our current understanding of climatic effects on phenology has been colored by springtime events (24–26), whereas events with a mean occurrence later in the season have been disproportionately neglected (27). To achieve satisfactory insight into how climate and its change affect the timing of biological activity across the season, we should thus ask how strongly phenology is influenced by climatic variation, what part of this response reflects phenotypic plasticity and what part evolutionary differentiation, and how the relative imprint of the two varies across the season. Addressing these pertinent questions is logistically challenging (e.g., ref. 28). Therefore, few studies have tackled them outside of the laboratory (29).Phillimore and coworkers (10, 30) proposed an elegant technique for identifying the relative roles of plasticity and local adaptation in generating spatiotemporal patterns of phenological variation. The rationale is to use a space versus time comparison (10, 30) (but see ref. 31 for criticism), drawing on the realization that at any one site, local conditions will vary between years. To be active at the right time, species will thus need to respond to temporal variation in climatic conditions. Let us assume that a focal species times some aspect of its annual activity (a species-specific “phenological event”) by reacting to a single environmental cue (e.g., the crossing of a given temperature sum). Now, if there were no differentiation between populations and all populations followed the same reaction norm, then with variation in the relative timing of the cue over time, all populations would react in the same way to the same cue regardless of spatial location (Fig. 1A). At the level of population means across space (blue line in Fig. 1A), we would then see a relationship between phenological event and cue timing identical to year-to-year variation within locations (red lines in Fig. 1A). However, if populations differentiate across sites, then we will see an added component in the spatial slope, reflecting the contribution of local adaptation to the mean phenology of the populations. By subtracting the within-population temporal slope from the spatial slope, we will thus achieve a direct measure of local adaptation (10), henceforth called Δb (30).Importantly, the temporal slope (i.e., the local phenological response to local year-to-year variation in the cue) can be either steeper or more shallow than the spatial slope (Fig. 1B vs. Fig. 1C)—the former being a sign of countergradient local adaptation, the latter of cogradient local adaptation (20, 21, 32). For a worked-through example of how this methodology is applied to the current data, see SI Appendix, Text S1.Here, we adopt temperature sums as widely used predictors of phenological events (33–35) and treat the difference between the spatial and temporal slopes of phenological events on such sums as our estimates of local adaptation in reaction norms (SI Appendix, Text S1). Pinpointing the relative roles of plasticity and microevolution from spatiotemporal observations in the absence of direct measures of fitness will, per necessity, rely on several assumptions (for a full discussion, see ref. 36). However, given the adequate precaution, such quantification allows a tractable way toward estimating local adaption on a large scale (8, 10, 30, 36–38).A key requirement for the successful application of this approach to resolving patterns across events of different relative timing is the existence of abundant data covering a large geographic area (30, 36). The extensive phenological data-collection scheme implemented at hundreds of nature reserves and other monitoring sites within the area of the former Soviet Union offers unique opportunities for addressing community-level phenology across a large space and long time (39). From this comprehensive dataset spanning 472 monitoring sites, 510,165 events and a time series of up to 118 y (Fig. 2 and ref. 39), we selected those 178 phenological events for which we have at least 100 data points that represent at least 10 locations (SI Appendix, Table S1). These events concerned 91 distinct taxa (SI Appendix, Table S1).Open in a separate windowFig. 2.Study sites and spatiotemporal patterns in climatic and phenological data. A shows the depth of the data and the spatial distribution of monitoring sites, with the size of the symbol proportional to the number of events scored locally. Since the selection of sites differed between events (39), in A, we have pooled sites located within 300 km from each other for illustration purposes. B shows the mean timing (day of year) of a phenological event: the onset of blooming in dandelion (Taraxacum officinale). C shows the mean timing (day of year) of a climatic event: the day of the year when the temperature sum providing the highest temporal slope for the onset of blooming in dandelion was first exceeded, computed as the mean over the years considered in B. For a worked-through example estimating reaction norms and metrics of local adaptation (Δb) for this species, see SI Appendix, Text S1.To express data on species phenology and abiotic conditions in the same currency, we related the dates of the phenological events (e.g., the first observation of an animal, or first flowering time of a plant species; SI Appendix, Fig. S1) to the dates when a given thermal sum (34, 35) was first exceeded. This choice of units has a convenient consequence in terms of the interpretation of slope values: if the date of phenology changes follows one-to-one the date of attaining a given temperature sum, then the slope will be one—an assumption frequently made but rarely tested in studies based on growth-degree days. The observed reaction norms can then be compared to this value. A value below 1 will signal undercompensation, i.e., that the earlier the cue, the larger the relative delay of the phenological event compared to its cue. By contrast, a value larger than 1 would signal overcompensation, i.e., that with an advancement of the cue, the timing of the phenological event will be advanced even more.Since thermal sums can be formed using a variety of thresholds, we used a generic approach and considered dates for exceeding a wide range of both heating and chilling degree-day sums (34, 35) (see Material and Methods for more information). As there is also evidence that sensitivity to temperature arises after a certain time point (13, 36), we calculated each heating and chilling degree-days sum for a range of starting dates. For each of the resulting 2,926 events, we then picked the variable that offered the highest temporal slope estimate, i.e., the largest within-location change in the timing of the event with a change in the timing of the cue (see Material and Methods for more information). Following the rationale outline above, this will be the most appropriate optimization criterion, since it selects the cue to which the phenological event responds the strongest to over time.     

Hubs disruption in mesial temporal lobe epilepsy. A resting‐state fMRI study on a language‐and‐memory network

Mesial temporal lobe epilepsy (mTLE) affects the brain networks at several levels and patients suffering from mTLE experience cognitive impairment for language and memory. Considering the importance of language and memory reorganization in this condition, the present study explores changes of the embedded language‐and‐memory network (LMN) in terms of functional connectivity (FC) at rest, as measured with functional MRI. We also evaluate the cognitive efficiency of the reorganization, that is, whether or not the reorganizations support or allow the maintenance of optimal cognitive functioning despite the seizure‐related damage. Data from 37 patients presenting unifocal mTLE were analyzed and compared to 48 healthy volunteers in terms of LMN‐FC using two methods: pairwise correlations (region of interest [ROI]‐to‐ROI) and graph theory. The cognitive efficiency of the LMN‐FC reorganization was measured using correlations between FC parameters and language and memory scores. Our findings revealed a large perturbation of the LMN hubs in patients. We observed a hyperconnectivity of limbic areas near the dysfunctional hippocampus and mainly a hypoconnectivity for several cortical regions remote from the dysfunctional hippocampus. The loss of FC was more important in left mTLE (L‐mTLE) than in right (R‐mTLE) patients. The LMN‐FC reorganization may not be always compensatory and not always useful for patients as it may be associated with lower cognitive performance. We discuss the different connectivity patterns obtained and conclude that interpretation of FC changes in relation to neuropsychological scores is important to determine cognitive efficiency, suggesting the concept of “connectome” would gain to be associated with a “cognitome” concept.     

Response of the Axial Skeleton to Bipedal Loading Behaviors in an Experimental Animal Model

Many derived aspects of modern human axial skeletal morphology reflect our reliance on obligate bipedal locomotion. Insight into the adaptive significance of features, particularly in the spine, has been gained through experimental studies that induce bipedal standing or walking in quadrupedal mammals. Using an experimental animal model (Rattus norvegicus), the present study builds on earlier work by incorporating additional metrics of the cranium, employing quantitative methods established in the paleoanthropological literature, and exploring how variation in mechanical loading regimes impacts axial anatomy. Rats were assigned to one of five experimental groups, including “fully loaded bipedal walking,” “partially loaded bipedal walking,” “standing bipedally,” “quadrupedal walking,” and “no exercise control,” and engaged in the behavior over 12-weeks. From μCT data obtained at the beginning and end of the experiment, we measured foramen magnum position and orientation, lumbar vertebral body wedging, cranial surface area of the lumbar and first sacral vertebral bodies, and sacral mediolateral width. Results demonstrate that bipedal rodents generally have more anteriorly positioned foramina magna, more dorsally wedged lumbar vertebrae, greater articular surface areas of lumbar and first sacral vertebral bodies, and sacra that exhibit greater mediolateral widths, compared to quadrupedal rodents. We further document variation among bipedal loading behavior groups (e.g., bipedal standing vs. walking). Our experimental animal model reveals how loading behaviors and adaptations may be specifically linked, and implicates a potential role for developmental plasticity in the evolutionary acquisition of bipedal adaptations in the hominin lineage. Anat Rec, 2018. © 2018 American Association for Anatomy.     

Effects of metformin on recognition memory and hippocampal neuroplasticity in rats with metabolic syndrome

Metabolic syndrome (MS) is a health problem that is characterized by body fat accumulation, hypertension, dyslipidemia, and hyperglycemia; recently, it has been demonstrated that MS also damages memory processes. The first-line drug in the treatment of MS and type 2 diabetes mellitus is metformin, which is an antihyperglycemic agent. This drug has been shown to produce neuroprotection and to improve memory processes. However, the mechanism involved in this neuroprotection is unknown. A 90-day administration of metformin improved the cognitive processes of rats with MS as evaluated by the novel object recognition test, and this finding could be explained by an increase in the neuronal spine density and spine length. We also found that metformin increased the immunoreactivity of synaptophysin, sirtuin-1, AMP-activated protein kinase, and brain-derived neuronal factor, which are important plasticity markers. We conclude that metformin is an important therapeutic agent that increases neural plasticity and protects cognitive processes. The use of this drug is important in the minimization of the damage caused by MS.     

亚急性期脑卒中康复过程中半球间皮质结构可塑性差异

探讨脑卒中患者亚急性期康复过程中不同半球间皮质结构可塑性机制差异。     

Recent developments with tau-based drug discovery

Introduction: Alzheimer’s disease (AD), which accounts for three fourth of all cases of dementia, is a major public health problem in modern society and, yet, there is no effective treatment available that can prevent or inhibit this chronic progressive neurodegenerative disease. A major current drug target is intraneuronal abnormally hyperphosphorylated microtubule-associated protein tau which is a histopathological hallmark of this disease and of a family of neurodegenerative diseases called tauopathies.

Areas covered: In this review, the authors discuss a growing number of studies that describe the nature and mechanism of tau pathology and various drug discovery options and most recent developments in tau-based therapeutics. PubMed was used to obtain relevant literature while clinicaltrials.gov site and Google search were employed to obtain the latest information on tau based AD clinical trials.

Expert opinion: In authors’ opinion, loss of neuronal connectivity leads to the hyperphosphorylation of tau and is thus a key therapeutic target. Rescue of neuronal connectivity loss and hyperphosphorylation of tau are most promising approaches. Consequently, tau immunotherapy has a high therapeutic potential.