Unrecognised depression in general practice.

Patients attending their general practitioner were screened and a group with unrecognised major depressive disorder identified. This group was interviewed and the findings compared with those in a group of patients recognised correctly as depressed by their general practitioners. Half of the patients with severe depression screened in their doctors'' waiting rooms went unrecognised, and they differed in few ways from those who were recognised. The differences found were that the patients with unrecognised depression were less obviously depressed and their illness had lasted longer. Physical illness was present in nearly 30% of patients in the unrecognised group, and the depression seemed related to it. Patients with unrecognised depression were more likely to have feelings other than those of normal sadness and more likely to respond with change of mood to intercurrent events. These data suggest that patients might benefit if general practitioners were better trained to recognise depression, although it is not known whether treatment would be effective.     

Non-stationarity and local approaches to modelling the distributions of wildlife

Despite a growing interest in species distribution modelling, relatively little attention has been paid to spatial autocorrelation and non-stationarity. Both spatial autocorrelation (the tendency for adjacent locations to be more similar than distant ones) and non-stationarity (the variation in modelled relationships over space) are likely to be common properties of ecological systems. This paper focuses on non-stationarity and uses two local techniques, geographically weighted regression (GWR) and varying coefficient modelling (VCM), to assess its impact on model predictions. We extend two published studies, one on the presence–absence of calandra larks in Spain and the other on bird species richness in Britain, to compare GWR and VCM with the more usual global generalized linear modelling (GLM) and generalized additive modelling (GAM). For the calandra lark data, GWR and VCM produced better-fitting models than GLM or GAM. VCM in particular gave significantly reduced spatial autocorrelation in the model residuals. GWR showed that individual predictors became stationary at different spatial scales, indicating that distributions are influenced by ecological processes operating over multiple scales. VCM was able to predict occurrence accurately on independent data from the same geographical area as the training data but not beyond, whereas the GAM produced good results on all areas. Individual predictions from the local methods often differed substantially from the global models. For the species richness data, VCM and GWR produced far better predictions than ordinary regression. Our analyses suggest that modellers interpolating data to produce maps for practical actions (e.g. conservation) should consider local methods, whereas they should not be used for extrapolation to new areas. We argue that local methods are complementary to global methods, revealing details of habitat associations and data properties which global methods average out and miss.     

Constant relative age and size at sex change for sequentially hermaphroditic fish

A general problem in evolutionary biology is that quantitative tests of theory usually require a detailed knowledge of the underlying trade-offs, which can be very hard to measure. Consequently, tests of theory are often constrained to be qualitative and not quantitative. A solution to this problem can arise when life histories are viewed in a dimensionless way. Recently, dimensionless theory has been developed to predict the size and age at which individuals should change sex. This theory predicts that the size at sex change/maximum size (L50/L(max)), and the age at sex change/age at first breeding (tau/alpha) should both be invariant. We found support for these two predictions across 52 species of fish. Fish change sex when they are 80% of their maximum body size, and 2.5 times their age at maturity. This invariant result holds despite a 60 and 25 fold difference across species in maximum size and age at sex change. These results suggest that, despite ignoring many biological complexities, relatively simple evolutionary theory is able to explain quantitatively at what point sex change occurs across fish species. Furthermore, our results suggest some very broad generalities in how male fitness varies with size and age across fish species with different mating systems.     

Does the resource dispersion hypothesis explain group living?

The resource dispersion hypothesis (RDH) asserts that, if resources are heterogeneous in space or time, group living might be less costly than was previously thought, regardless of whether individuals gain direct benefits from group membership. The RDH was first proposed more than 20 years ago and has since accumulated considerable support. However, it is sometimes discredited because a priori tests of specific predictions are few, relevant variables have proved difficult to define and measure, and because its assumptions and predictions remain unclear. This is unfortunate because the RDH provides a potentially powerful model of grouping behavior in a diversity of conditions. Moreover, it can be generalized to predict other phenomena, including spacing behavior in nonsocial animals and utilization of resources other than food. Here, we review the empirical support, clarify the predictions of the RDH and argue that they can be used to provide better tests.     

Extreme sex ratio variation in relation to change in condition around conception

Adaptive theory predicts that mothers would be advantaged by adjusting the sex ratio of their offspring in relation to their offspring's future reproductive success. Studies investigating sex ratio variation in mammals have produced notoriously inconsistent results, although recent studies suggest more consistency if sex ratio variation is related to maternal condition at conception, potentially mediated by changes in circulating glucose level. Consequently, we hypothesized that change in condition might better predict sex ratio variation than condition per se. Here, we investigate sex ratio variation in feral horses (Equus caballus), where sex ratio variation was previously shown to be related to maternal condition at conception. We used condition measures before and after conception to measure the change in condition around conception in individual mothers. The relationship with sex ratio was substantially more extreme than previously reported: 3% of females losing condition gave birth to a son, whereas 80% of those females that were gaining condition gave birth to a son. Change in condition is more predictive of sex ratio than actual condition, supporting previous studies, and shows the most extreme variation in mammals ever reported.     

A comparison of life events scoring methods as predictors of psychological symptomatology

Seven scoring methods for the Life Events Survey (LES) were compared to determine which, if any, is superior for prediction of psychological symptomatology as measured by the Brief Symptom Inventory (BSI). Every scoring method tested, except one utilizing an individual's positive ratings of events, was significantly correlated with symptomatology. The method using an individual's negative ratings of events was a significantly better predictor than any other. These findings suggest several conclusions. First, nomothetic methods for weighting life events do not increase a scale's predictive ability beyond that achieved by a frequency count of events. Second, frequency of life events predicts psychological symptomatology only insofar as life events are perceived as negative. That is, positively perceived events do not predict symptomatology. Finally, a life events scale's predictive ability is increased by utilizing the individual's negative perceptions of events.     

Extrapolating a hierarchy of building block systems towards future neural network organisms

Is it possible to predict future life forms? In this paper it is argued that the answer to this question may well be positive. As a basis for predictions a rationale is used that is derived from historical data, e.g. from a hierarchical classification that ranks all building block systems, that have evolved so far. This classification is based on specific emergent properties that allow stepwise transitions, from low level building blocks to higher level ones. This paper shows how this hierarchy can be used for predicting future life forms. The extrapolations suggest several future neural network organisms. Major aspects of the structures of these organisms are predicted. The results can be considered of fundamental importance for several reasons. Firstly, assuming that the operator hierarchy is a proper basis for predictions, the result yields insight into the structure of future organisms. Secondly, the predictions are not extrapolations of presently observed trends, but are fully integrated with all historical system transitions in evolution. Thirdly, the extrapolations suggest the structures of intelligences that, one day, will possess more powerful brains than human beings. This study ends with a discussion of possibilities for falsification of the present theory, the implications of the present predictions in relation to recent developments in artificial intelligence and the philosophical implications of the role of humanity in evolution with regard to the creation of future neural network organisms.     

Modern African ape populations as genetic and demographic models of the last common ancestor of humans, chimpanzees, and gorillas

In order to fully understand human evolutionary history through the use of molecular data, it is essential to include our closest relatives as a comparison. We provide here estimates of nucleotide diversity and effective population size of modern African ape species using data from several independent noncoding nuclear loci, and use these estimates to make predictions about the nature of the ancestral population that eventually gave rise to the living species of African apes, including humans. Chimpanzees, bonobos, and gorillas possess two to three times more nucleotide diversity than modern humans. We hypothesize that the last common ancestor (LCA) of these species had an effective population size more similar to modern apes than modern humans. In addition, estimated dates for the divergence of the Homo, Pan, and Gorilla lineages suggest that the LCA may have had stronger geographic structuring to its mtDNA than its nuclear DNA, perhaps indicative of strong female philopatry or a dispersal system analogous to gorillas, where females disperse only short distances from their natal group. Synthesizing different classes of data, and the inferences drawn from them, allows us to predict some of the genetic and demographic properties of the LCA of humans, chimpanzees, and gorillas.     

Some problems in the measurement of stress and social readjustment.

Although the Social Readjustment Rating Scale has been a useful stimulant to the study of life change and illness, it has important limitations. The existing scale cannot be used to determine the role of varying types of life changes (e.g., favorable or adverse) in the occurrence of illness. Other problems discussed are ambiguity of items, the confounding of independent and dependent variables, and lack of item specification. Suggestions are made for improved measurement of life change and for more effective study of life change and illness.     

Variability in 20th century climate change reconstructions and its consequences for predicting geographic responses of California mammals

Empirical species distribution models are widely used to predict the effects of climate change on biodiversity distribution but rely on multiple assumptions about the certainty of the locality and climate data. Here, we assess the effect of historical climate data variability when forecasting geographic responses of California mammals to 20th century climate change. We first used two methods to derive gridded climate surfaces from weather station data (ANUSPLIN and PRISM) representing two sampling eras: historic (1900–1940) and current (1980–2005). We then used the two sources of climate data in conjunction with a maximum entropy algorithm (MAXENT) to predict both the historic and current distributions of all major mammal species vouchered historically in California. Results indicate that levels of disagreement between the two climate datasets are considerably greater in the historical era than in the current era. For the bioclimatic variables used in modeling historical mammal distributions, precipitation variables were less concordant than temperature variables. These discrepancies are reflected in the low agreement between historic mammal range predictions and further propagated when the historic models are projected to present day. Nonetheless, some common patterns exist across mammal species and climate estimates. Range stability is the most common prediction between the two eras, followed by expansion and contraction. Jepson ecoregions with relatively high levels of range stability include parts of the Great Central Valley and Sierra Nevada, while other parts of the Central Valley, the Sonoran desert, and Central- and Southwestern California yield predictions of range shifts. Historical species distribution modeling can greatly inform studies attempting to describe how species will continue to move geographically in response to future changes in climate. We suggest that alternative estimates of historical climate and their uncertainties are ultimately required in order to provide a quantitative measure of the confidence in predicted changes in distribution.     

The problem of prediction in invasion biology

Invasion biology is a relatively young discipline which is important, interesting and currently in turmoil. Biological invaders can threaten native ecosystems and global biodiversity; they can incur massive economic costs and even introduce diseases. Invasion biologists generally agree that being able to predict when and where an invasion will occur is essential for progress in their field. However, successful predictions of this type remain elusive. This has caused a rift, as some researchers are pessimistic and believe that invasion biology has no future, whereas others are more optimistic and believe that the key to successful prediction is the creation of a general, unified theoretical framework which encompasses all invasion events. Although I agree that there is a future for invasion biology, extensive synthesis is not the way to better predictions. I argue that the causes of invasion phenomena are exceedingly complex and heterogeneous, hence it is impossible to make generalizations over particular events without sacrificing causal detail. However, this causal detail is just what is needed for the specific predictions which the scientists wish to produce. Instead, I show that a limited type of synthesis (integration of data and methods) is a more useful tool for generating successful predictions. An important implication of my view is that it points to a more pluralistic approach to invasion biology, where generalization and prediction are treated as important yet distinct research goals.     

Sources of individual differences in stressful life event exposure in male and female twins.

The roles of genetic and environmental influences on stressful life events were examined in 3938 twin pairs (MZ, same-sex DZ, and opposite-sex DZ) using a sex-limitation model. Life events were assessed by personal interview, and were categorized as being either personal (i.e., events that occur directly to the individual) or network (i.e., events that occur to someone within the individual's social network, thus affecting the individual indirectly). Consistent with previous reports, genetic factors were found to exert more influence on personal events than network events. Genetic correlations between males and females suggest that many of the same genetic factors are acting within both genders.     

Everyday problems and life change events: ongoing versus acute sources of stress

Research on the relationship between life stress and illness has focused largely on stress caused by change. The present study examines a relatively neglected source of stress: everyday problems, defined as ongoing, often chronic situations, which are stressful for a substantial period of time. An inventory of everyday problems was developed, designed to minimize as much as possible potential confounds present in earlier work. It was administered to 281 undergraduate women along with a life events inventory, the Hopkins Symptom Checklist, and a social support scale, which measured family and nonfamily sources of support. Stepwise regression analyses indicated that everyday problems were more effective than life events in predicting psychological symptoms. Everyday problems were significant predictors of symptoms even after statistically controlling for life events, whereas life events had no predictive ability beyond that attributed to everyday problems. In addition, a significant interaction between everyday problems and life events was found. Multiple regression analyses also showed an interaction between everyday problems and nonfamily social support, as predicted by the buffering hypothesis. Within the methodological limitations of this study, these findings are interpreted as supporting the importance of everyday problems as a significant source of stress.     

Modeling multi-drug chemotherapy: tailoring treatment to individuals

BACKGROUND: Predicting and tailoring optimal cancer treatments presents a major challenge. METHODS: A computational model (kinetically tailored treatment, or KITT model) is developed to predict drug combinations, doses, and schedules likely to be effective in reducing tumor size and prolonging patient life. Treatment strategies may be tailored to individuals based on tumor cell kinetics. The model incorporates intra-tumor heterogeneity and evolution of drug resistance, apoptotic rates, and cell division rates. Tumor growth may follow an exponential or a Gompertzian trajectory. Drug pharmacodynamic and pharmacokinetic models are used. Toxicity is modeled in several ways. RESULTS: A key prediction of KITT is that including cytostatic drugs like tamoxifen and herceptin during treatment with cytotoxic drugs substantially increases the probability of cure and prolongs patient life. Results also suggest that altering drug scheduling may be more effective but not more toxic than dose escalation. CAF chemotherapy (cyclophosphamide, adriamycin, and 5-fluorouracil) is predicted to be more effective than CMF (cyclophosphamide, methotrexate, and 5-fluorouracil). KITT also suggests that tumors with a high proliferative index (PI) may respond better to drug combinations incorporating two cell-cycle phase-specific drugs than do tumors with a low PI. Tumors with a low PI, in contrast, are predicted to respond better to regimens involving two cell-cycle phase-non-specific drugs than do tumors with a high PI. These predictions are borne out by clinical trial results published in the literature, which are discussed.Simulated predictions of the model match well with results from a clinical trial by Silvestrini et al. (2000. Int. J. Cancer 87, 405). The results of simulating the growth of 26896 tumors are used to construct a decision tree for prognosis to identify the key tumor and treatment variables. CONCLUSION: Additional tests of the model are needed in which physicians collect information on apoptotic and proliferative indices, cell-cycle times, and drug resistance from biopsies of each individual's tumor. Computational models may become important tools to help optimize and tailor cancer treatments.     

Phenological shifts and the fate of mutualisms

Climate change is altering the timing of life history events in a wide array of species, many of which are involved in mutualistic interactions. Because many mutualisms can form only if partner species are able to locate each other in time, differential phenological shifts are likely to influence their strength, duration and outcome. At the extreme, climate change‐driven shifts in phenology may result in phenological mismatch: the partial or complete loss of temporal overlap of mutualistic species. We have a growing understanding of how, when, and why phenological change can alter one type of mutualism–pollination. However, as we show here, there has been a surprising lack of attention to other types of mutualism. We generate a set of predictions about the characteristics that may predispose mutualisms in general to phenological mismatches. We focus not on the consequences of such mismatches but rather on the likelihood that mismatches will develop. We explore the influence of three key characteristics of mutualism: 1) intimacy, 2) seasonality and duration, and 3) obligacy and specificity. We predict that the following characteristics of mutualism may increase the likelihood of phenological mismatch: 1) a non‐symbiotic life history in which co‐dispersal is absent; 2) brief, seasonal interactions; and 3) facultative, generalized interactions. We then review the limited available data in light of our a priori predictions and point to mutualisms that are more and less likely to be at risk of becoming phenologically mismatched, emphasizing the need for research on mutualisms other than plant–pollinator interactions. Future studies should explicitly focus on mutualism characteristics to determine whether and how changing phenologies will affect mutualistic interactions.     

Life event classifications and the event-illness relationship.

Several theoretically important and distinct categories of life change are found in most life event scales. These categories can be organized in terms of at least three dimensions: the person's control over the event, the desirability of the event, and whether or not the independent variable of the event is confounded with the dependent variable of illness. It is important to separate conceptually and, to the extent possible, to distinguish empirically among events according to these dimensions, because several different models of the event-illness relationship are implied when events from several categories are combined. A secondary analysis of recently published data shows that the kinds of events associated with illness are undesirable events within the subject's control. It may not be necessary to consider these dimensions in predicting illness, but the prevention and understanding of illness are furthered by their consideration.     

Global perceived stress level as a moderator of the relationship between negative life events and depression

The present research investigated the proposal that global perceived stress level moderates the degree of relationship between negative life events and depression. Accordingly, subjects in this study completed the Beck Depression Inventory (BDI), the Life Experiences Survey (LES), and the Perceived Stress Scale (PSS). The PSS provides a measure of global perceived stress level, or the general tendency to view one's life as being unpredictable, out of control, and overwhelming. Consistent with past research, the findings revealed an increase in depression level as negative life change scores increased. Of special importance, however, was the finding that global level of stress significantly moderated the relationship between depression and negative life events. For those low on perceived stress, negative life changes had only a minimal impact on depression level. In contrast, for those high on perceived stress, the relationship was more pronounced. These findings were then discussed with regard to the possible role of cognitive appraisals in enhancing the symptoms of psychopathology experienced by individuals high on global level of perceived stress.     

Genetic Change and Rates of Cladogenesis

Models are introduced which predict ratios of mean levels of genetic divergence in species-rich versus species-poor phylads under two competing assumptions: (1) genetic differentiation is a function of time, unrelated to the number of cladogenetic events and (2) genetic differentiation is proportional to the number of speciation events in the group. The models are simple, general, and biologically real, but not precise. They lead to qualitatively distinct predictions about levels of genetic divergence depending upon the relationship between rates of speciation and amount of genetic change. When genetic distance between species is a function of time, mean genetic distances in speciose and depauperate phylads of equal evolutionary age are very similar. On the contrary, when genetic distance is a function of the number of speciations in the history of a phylad, the ratio of mean genetic distances separating species in speciose versus depauperate phylads is greater than one, and increases rapidly as the frequency of speciations in one group relative to the other increases. The models may be tested with data from natural populations to assess (1) possible correlations between rates of anagenesis and cladogenesis and (2) the amount of genetic differentiation accompanying the speciation process. The data collected in electrophoretic surveys and other kinds of studies can be used to test the predictions of the models. For this purpose genetic distances need to be measured in speciose and depauperate phylads of equal evolutionary age. The limited information presently available agrees better with the model predicting that genetic change is primarily a function of time, and is not correlated with rates of speciation. Further testing of the models is, however, required before firm conclusions can be drawn.     

Triadic social interactions operate across time: a field experiment with wild chimpanzees

Social animals cooperate with bonding partners to outcompete others. Predicting a competitor''s supporter is likely to be beneficial, regardless of whether the supporting relationship is stable or transient, or whether the support happens immediately or later. Although humans make such predictions frequently, it is unclear to what extent animals have the cognitive abilities to recognize others’ transient bond partners and to predict others'' coalitions that extend beyond the immediate present. We conducted playback experiments with wild chimpanzees to test this. About 2 h after fighting, subjects heard recordings of aggressive barks of a bystander, who was or was not a bond partner of the former opponent. Subjects looked longer and moved away more often from barks of the former opponents’ bond partners than non-bond partners. In an additional experiment, subjects moved away more from barks than socially benign calls of the same bond partner. These effects were present despite differences in genetic relatedness and considerable time delays between the two events. Chimpanzees, it appears, integrate memories of social interactions from different sources to make inferences about current interactions. This ability is crucial for connecting triadic social interactions across time, a requirement for predicting aggressive support even after a time delay.     

Gimme shelter – the relative sensitivity of parasitic nematodes with direct and indirect life cycles to climate change

Climate change is expected to alter the dynamics of host–parasite systems globally. One key element in developing predictive models for these impacts is the life cycle of the parasite. It is, for example, commonly assumed that parasites with an indirect life cycle would be more sensitive to changing environmental conditions than parasites with a direct life cycle due to the greater chance that at least one of their obligate host species will go extinct. Here, we challenge this notion by contrasting parasitic nematodes with a direct life cycle against those with an indirect life cycle. Specifically, we suggest that behavioral thermoregulation by the intermediate host may buffer the larvae of indirectly transmitted parasites against temperature extremes, and hence climate warming. We term this the ‘shelter effect’. Formalizing each life cycle in a comprehensive model reveals a fitness advantage for the direct life cycle over the indirect life cycle at low temperatures, but the shelter effect reverses this advantage at high temperatures. When examined for seasonal environments, the models suggest that climate warming may in some regions create a temporal niche in mid‐summer that excludes parasites with a direct life cycle, but allows parasites with an indirect life cycle to persist. These patterns are amplified if parasite larvae are able to manipulate their intermediate host to increase ingestion probability by definite hosts. Furthermore, our results suggest that exploiting the benefits of host sheltering may have aided the evolution of indirect life cycles. Our modeling framework utilizes the Metabolic Theory of Ecology to synthesize the complexities of host behavioral thermoregulation and its impacts on various temperature‐dependent parasite life history components in a single measure of fitness, R₀. It allows quantitative predictions of climate change impacts, and is easily generalized to many host–parasite systems.