Excess Years of Life Lost due to exposure is an important measure of health impact complementary to rate or risk statistics. I show that the total excess Years of Life Lost due to exposure can be estimated unbiasedly by calculating the corresponding excess Years of Potential Life Lost given conditions that describe study validity (like exchangeability of exposed and unexposed) and assuming that exposure is never preventive. I further demonstrate that the excess Years of Life Lost conditional on age at death cannot be estimated unbiasedly by a calculation of conditional excess Years of Potential Life Lost without adopting speculative causal models that cannot be tested empirically. Furthermore, I point out by example that the excess Years of Life Lost for a specific cause of death, like lung cancer, cannot be identified from epidemiologic data without assuming non-testable assumptions about the causal mechanism as to how exposure produces death. Hence, excess Years of Life Lost estimated from life tables or regression models, as presented by some authors for lung cancer or after stratification for age, are potentially biased. These points were already made by Robins and Greenland 1991 reasoning on an abstract level. In addition, I demonstrate by adequate life table examples designed to critically discuss the Years of Potential Life Lost analysis published by Park et al. 2002 that the potential biases involved may be fairly extreme. Although statistics conveying information about the advancement of disease onset are helpful in exposure impact analysis and especially worthwhile in exposure impact communication, I believe that attention should be drawn to the difficulties involved and that epidemiologists should always be aware of these conceptual limits of the Years of Potential Life Lost method when applying it as a regular tool in cohort analysis. This information is used to measure the effect of exposure on disease by comparisons of such statistics. Although these measures have been proven by practice and theory to be useful for this purpose, these frequency statistics are unable to reflect all causal effects of exposure in general . One reand 1998 , Greenlaand 1998 , Morfeldand 1998 ). An illTherefore, alternative measures that focus more directly on the time-shift of events or the time-shift of frequency statistics would be most welcome. One such approach aims at Years of Life Lost (YLL). Interestingly, even in the title of one of the very first articles about Years of Life Lost, Dempsey  expresseRecently, this approach was extended by Park et al. 2002 ) estimatHowever, in a letter to the editor, Morfeld 2003  claimed Here I try to explain the reasoning in detail. I expand the critique demonstrating additionally that the e-YPLL method is unjustified when applied to specific causes of death like lung cancer – the main topic of Park et al. 2002 [To summarize, the main focus of this paper is first, on a new and simple proof of the potential bias of the e-YPLL method, and second, on an illustration of the degree of potential bias involved by realistic life table examples. These examples are constructed to critically discuss the e-YPLL analysis of a cohort of US uranium miners presented by Park et al. 2002  breakthrThe aim of this paper is to scrutinise the validity of an exposure effect statistic. Therefore, a gold standard is needed against which the statistic can be evaluated to identify and to measure potential biases. A comprehensive cohort investigation is the most natural and purposeful epidemiologic approach towards causality when leaving aside all practical obstacles linked to this study design (Rothman and Greenland 1998 ). To appThese differently exposed but exchangeable subjects are assumed to be like ideal twins: if exposure status had been interchanged between both twins the health response of the subject exposed to exposure level A would have been exactly the response of his/her twin exposed to level B and vice versa. This scenario of totally exchangeable twins is at the bottom of the so called "counterfactual" approach to causality . ObviouNote that the sub-cohort of unexposed twins constitutes an ideal reference population. It is ideal with respect to 1) being an optimal reference group to define the response of the exposed cohort had it not been exposed (comparison on the group level). Over and above this optimality criterion, the reference is ideal with respect to 2) being an optimal reference on the individual level: the response of each exposed subject can be compared to the response of his/her twin. Note that optimality criterion 2) entails optimality criterion 1). Whereas an external reference population as often chosen in concrete epidemiological studies (Rothman and Greenland 1998 , BreslowIn this section I define excess Years of Life Lost (e-YLL) and excess Years of Potential Life Lost (e-YPLL). Then I analyse their relationship in the most simple setting of an ideal study: a pair-matched cohort study with binary point exposure and a death process as outcome. The cohort is assumed to consist of only two subjects, both being differently exposed but totally exchangeable twins. Figure Both twins (1 and 2) were born on the same day, b(1) = b(2). For simplicity I rescale the time axis and set b(1) = b(2) = 0. Thus, time is identical with age throughout this paper. The point exposure was allocated at an age of t(1) = t(2) > b(1) = b(2) = 0: twin 1 was exposed, twin 2 was never exposed. The twins died at ages d(1) > t(1) and d(2) > t(2), respectively, and deaths are understood as deterministic responses on the individual level. I assume the exposed twin dies earlier or at the same date as the unexposed control twin, d(1) ≤ d(2). Since both subjects are perfectly exchangeable exposure was neutral or detrimental but not preventive. Exposure caused an advancement of death by d(2) - d(1) years. Thus, the true excess Years of Life Lost due to exposure are e-YLL = d(2) - d(1).Next, I introduce potential death times pd(i), i = 1, 2. If both subjects had been unexposed, I would have expected them to die at pd(i) when relying upon the sub-set of the reference population surviving at least to d(i). Since the ideal reference population consists of the unexposed subject only, it immediately follows pd(1) = d(2) and pd(2) = d(2). For this reason I set pd(1) and d(2) equal in Figure I now calculate life expectancy at age at death, YPLL(d(i)), based on the reference population. These Years of Potential Life Lost are defined as YPLL(d(i)) = pd(i) - d(i). Hence, YPLL(d(1)) equals pd(1) - d(1) = d(2) - d(1) = e-YLL in this elementary setting. Obviously I have YPLL(d(2)) = 0. Note that the identity of YPLL(d(1)) and e-YLL relies on the elementary scenario and on the exchangeability condition .Next, the effect estimate based on YPLL is introduced. The effect estimate of exposure on health is defined as the excess Years of Potential Life Lost, e-YPLL, which is the sum of excess deaths among the exposed (i.e., the observed exposed deaths minus the expected exposed deaths) times Years of Potential Life Lost at age at death, where the summation runs over all death times t  - expected(t)) YPLL(t).Since the number of events is discrete this can always be understood as    e-YPLL = Σ observed(d(i)) YPLL(d(i)) - Σ expected(d(j)) YPLL(d(j)),where the first sum is running over all death times d(i) among the exposed twins and the second sum is running over all death times d(j) among the unexposed twins.The number of expected deaths among the exposed at age t, expected(t), is calculated as the product of the risk among the unexposed at t and the number of exposed at t. This definition is analogous to the calculation of exposed cases in an SMR analysis (Breslow and Day 1987 ):    baseline risk at t = # unexposed events at t / # unexposed twins at t    expected(t) = (baseline risk at t) (# exposed twins at t).If there are no unexposed twins at age t, I define expected(t) to be zero.Two risk sets are given in the simple scenario discussed here: one exposed death occurs at d(1) and one unexposed death at d(2). At d(1) two subjects are under risk: one exposed, the other unexposed. Since the unexposed subject does not die at d(1) we get expected (d(1)) = (0/1) (1) cases among the one (1) exposed at d(1). At d(2) no exposed but one unexposed subject is under risk. Since the unexposed dies at d(2) this leads to expected(d(2)) = (1/1) (0) cases among the zero (0) exposed at d(2). Because one exposed death is observed at d(1) and zero exposed deaths at d(2), i.e., observed(d(1)) = 1 and observed(d(2)) = 0), it follows that      e-YPLL = (1 - (0/1)1) YPLL(d(1)) + (0 - (1/1)0) YPLL(d(2))      = YPLL(d(1)) = e-YLL.Of course, using the second representation of e-YPLL given above one also gets      e-YPLL = 1(d(2) - d(1)) - 0(0) = d(2) - d(1) = e-YLL.Hence, e-YPLL measures e-YLL unbiasedly, given the elementary scenario and the exchangeability condition .a(1) ≤ da(2) ≤ db(1) ≤ db(2). ObviouslyI have proven that e-YPLL is identical to e-YLL in an elementary setting of a cohort consisting of two exchangeable subjects given appropriate conditions. But does this hold if the cohort consists of two or more pairs of exchangeable twins? For didactic reasons I will study a cohort of two pairs of twins first. If we assume that exposure is never preventive, we are left with only two principal scenarios when investigating the situation with two pairs of twins. Indexing the pairs with a and b, the first principal scenario supposes the death times of the four subjects to be ordered as da(2) - da(1) + db(2) - db(1).    e-YLL = da(1))). The same simplified notation is used to denote observed and expected numbers of cases. Following Gardner and Sanborn 1990 [a(1) are given asNow I will calculate the Years of Potential Life Lost under this scenario 1. For the sake of clarity, I add a second argument to the YPLL function denoting the pair under consideration (a or b). To simplify the notation of YPLL, I drop the letter d that is superfluous since we analyse a scenario without censoring  (i.e., YPLL(a,1) = YPLL(da(2) - da(1) + 0.5 (db(2) - da(2)).    YPLL (a,1) = da(1) and half of the unexposed subjects survive at least to da(2). Analogous considerations yieldThis follows because all unexposed subjects survive at least to db(2) - da(2)    YPLL (a,2) = db(2) - db(1)    YPLL (b,1) = d    YPLL (b,2) = 0.The observed cases among the exposed are    observed (a,1) = 1    observed (b,1) = 1and the expected, based on the unexposed, are given as    expected(a,2) = (1/2)(1) = 0.5    expected(b,2) = (1/1)(0) = 0.Finally, this leads toa(2) - da(1) + 0.5 (db(2) - da(2)) -    e-YPLL = db(2) - da(2)) +      - 0.5 (db(2) - db(1)      + d    = e-YLL.a(1) ≤ db(1) ≤ da(2) ≤ db(2).Therefore, the equality of e-YPLL and e-YLL is proven for scenario 1. Next, I will investigate the other principal scenario. In contrast to scenario 1 the second principal scenario assumes the ordering dGiven this scenario 2 we havea(2) - da(1) + db(2) - db(1)    e-YLL = d    anda(2) - da(1) + 0.5(db(2) - da(2))    YPLL(a,1) = db(2) - da(2)    YPLL(a,2) = da(2) - db(1) + 0.5(db(2) - da(2))    YPLL(b,1) = d    YPLL(b,2) = 0.Moreover, it follows    expected(a,2) = (1/2)0 = 0    expected(b,2) = (1/1)0 = 0.Therefore,    e-YPLL = (1)YPLL(a,1) + (1)YPLL(b,1) - (0)YPLL(a,2) - (0)YPLL(b,2)a(2) - da(1) + da(2) - db(1) + db(2) - da(2)    = da(2) - da(1) + db(2) - db(1) = e-YLL.    = dIf we assume exposure to be never preventive all possible configurations of the death times of both twins can be mapped onto these two principal scenarios by renaming the subjects accordingly. Thus e-YPLL = e-YLL is always valid in such an ideal study with two pairs of twins, although e-YPLL does not use the individual matching information.It is important to note the following: the true excess Years of Life Lost can be calculated for each single pair as "observed cases times potential years of life lost" minus "expected cases times potential years of life lost" only in scenario 1. This calculation is obviously invalid under scenario 2. Only the total e-YPLL equals the total e-YLL in both scenarios. Thus, scenario 2 indicates that we have to be careful when a specified version of e-YPLL is applied to estimate the excess Years of Life Lost given specific conditions. Such an analysis is possible without bias if first, optimality criterion 2 is fulfilled as introduced in the last paragraph of Chapter 1, and second, if this individual matching information is used in the analysis. However, this kind of application is not justified if only criterion 1 holds. I will deal with this important issue of potential biases in Chapters 3 and 4.A general proof of e-YPLL = e-YLL can be constructed using the principle of mathematical induction. Hence, this Chapter is a little bit technical. However, it may be skipped on first reading because it is not necessary to understand this proof to follow the arguments on potential biases developed in Chapters 3 and 4.For the sake of clarity I introduce an additional functional argument denoting the number of pairs in the study: e-YLL(n) denotes the total excess Years of Life Lost due to exposure in a study with n pairs of twins. To start the inductive argument let us assume that e-YPLL = e-YLL has already been proven to be valid in all studies with n pairs of twins: e-YPLL(n) = e-YLL(n). This is the so called induction assumption. Now I add another pair of twins to the study and ask whether e-YPLL(n+1) = e-YLL(n+1). If I can prove this equality then it follows from the principle of mathematical induction that e-YPLL = e-YLL in all studies. This extension is true because I already have demonstrated that e-YPLL = e-YLL if n = 1 (start of induction).x(1) and dx(2) accordingly. Regarding the other n pairs we know that e-YPLL(n) = e-YLL(n) due to the induction assumption. Hence it suffices to show that e-YPLL(n+1) = e-YLL(n+1) holds in the scenario with the added pair being pair x. This will prove the equation e-YPLL = e-YLL convincingly for all studies with n+1 pairs.To simplify the argument I now suppose the additional pair to have an unexposed twin with minimal survival among the unexposed n+1 subjects in the extended study. This simplication can always be assumed without any loss of generality for the following reason. If the added pair is not the one with the minimal death time among the unexposed I focus on a pair that fulfils that condition. Of course, such a pair always exists. Let us denote this pair with x and the death times of its twins with dx(1) ≤ dx(2). This means that the survival function of the exposed is the same in the extended study group with n+1 pairs as in the study group with n pairs, given we restrict the analysis to all exposed subjects with age at death greater than or equal to dx(2). Moreover, because dx(2) is minimal among the death times of all unexposed subjects the survival function of the unexposed is unchanged too. It follows that the Years of Potential Life Lost  calculated from the unexposed reference population remains the same for all exposed subjects with age at death greater than or equal to dx(2). In addition, the expected number of cases among the exposed calculated from the unexposed remains unchanged. Therefore, the difference between e-YPLL(n+1) and e-YPLL(n) can only stem from events occurring before dx(2). In the next step I will calculate the change in e-YPLL caused by these events occurring before dx(2).Since we assume throughout that exposure is never preventive we know that dx(2), 0 ≤ k ≤ n. Thus, there are n+1 - (k+1) = n - k exposed subjects of the extended study group under risk at dx(2), and it follows expected(x,2)=(n-k)/(n+1). The factor 1/(n+1) is to apply since the first event among the n+1 unexposed subjects occurs at dx(2). The amount of change in e-YPLL due to the additionally expected deaths at dx(2) is therefore given by -[(n-k)/(n+1)]YPLL(x,2). This is the first kind of contribution to the change in e-YPLL I have to consider. The second kind of contribution stems from the new exposed case dying at dx(1). This amount of change is simply the same as the Potential Years of Life Lost for this case: YPLL(x,1) = dx(2) - dx(1) + [n/(n+1)]YPLL(x,2) where n/(n+1) is the probability to survive dx(2) given the unexposed reference population. The third kind of contribution to the difference between e-YPLL(n+1) and e-YPLL(n) stems from the k exposed cases dying before dx(2). Their potential survival is shortened after introducing the new additional death at dx(2). Note that the reference-based probability to die at dx(2) is 1/(n+1) for each of these exposed cases. Therefore, the Years of Potential Life Lost is reduced for each of these cases by the amount [1/(n+1)]YPLL(x,2). This sums to [k/(n+1)]YPLL(x,2) for all k exposed cases dying before dx(2). Now I can calculate how e-YPLL changes when the pair x is added to the n pairs:I assume that k of the n exposed in the study with n pairs may die before dx(2) - dx(1) +    e-YPLL(n+1) = e-YPLL(n) + d      + [n/(n+1) - (n-k)/(n+1) - k/(n+1)] YPLL(x,2)x(2) - dx(1)      = e-YPLL(n) + dx(2) - dx(1)      = e-YLL(n) + d      = e-YLL(n+1).x(2) - dx(1) if the pair x is added to the n pairs I started with. Hence, I have proven that e-YPLL equals e-YLL in all ideal studies consisting of pairs of exchangeable twins given that exposure is never preventive. Note that the measures are always calculated with respect to deaths from all causes. It is remarkable that the equality e-YPLL = e-YLL holds although e-YPLL does not make use of the individual matching information.The second to last equation follows from the induction assumption. The last equality is based on the obvious fact that the true excess Years of Life Lost increases by dI have shown in Chapter 2 that the total e-YLL of the whole study group can be measured accurately by the total e-YPLL provided the assumptions of an ideal study hold including optimality criterion 2 (cf. last paragraph of Chapter 1) and provided the analysed response is death from all causes. However, this identity of e-YLL and e-YPLL no longer holds if I am interested in e-YLL conditional on age at death. To see why consider the scenarios illustrated in Figure a(1) are smaller in scenario 1 than in scenario 2 whereas it is vice versa at db(1). Irrespective of the scenario, the e-YPLL are always calculated in the same way. Therefore, we get in both scenarios the same e-YPLL at da(1) as well as the same e-YPLL at db(1). Note that the calculation of e-YPLL does not make any use of the individual matching information.In both scenarios Fig.  the totaHence, the true excess Years of Life Lost conditional on age at death are not identifiable without having access to a perfect control twin or without supposing a specific mechanism for how exposure causes death. Note that criterion 1 alone – as defined in the last paragraph of Chapter 1 and hopefully fulfilled in usual cohort data – does not render the excess Years of Life Lost stratified on age at death identifiable. Consequently, estimates based on e-YPLL conditional on age at death are potentially biased in all usual settings.Next, I analyse a theoretical birth cohort of 100,000 men to demonstrate this potential bias by a realistic life table example  for short. In contrast to the causal effect, e-YLL(lung cancer) comprises exactly that part of e-YLL from all causes of death that is due to the occurrence of lung cancer deaths among the exposed. In the example Figure  these e-The concept presented supposes that each subject has a set of hypothetical (deterministic) death times for each combination of level of exposure and cause of death. Just one of these death times is effective, the others are latent. For the exposed twin 1 lung cancer is effective as a cause of death and heart attack is latent. It is vice versa for the unexposed twin 2. Both causes of death compete within each twin for the effective position. Of course, the result of this competition depends on the exposure conditions applied. This result motivates to introduce a second (competing) exposure into the scenario, in which I deal with two competing responses (lung cancer and heart attack). Figure In Figure From Figure     e-YLL = e-YLL(lung cancer) = e-YPLL =a(2) - da(1) + db(2) - db(1).    dThe first equation holds because the only cause of death among all asbestos exposed is lung cancer and the second because I have shown that e-YLL = e-YPLL is true in an ideal study given the aforementioned assumptions. The third equation follows from a simple evaluation of e-YLL.Next, I calculate e-YPLL(lung cancer) according to Park et al. 2002 [lungcancer(t) - expectedlungcancer(t)) YPLL(t).    e-YPLL(lung cancer) = Σ  - da(1) + 0.5 (db(2) - da(2))    YPLL (a,1) = db(2) - db(1),    YPLL (b,1) = dfrom which I derivea(2) - da(1) + 0.5 (db(2) - da(2)) +    e-YPLL(lung cancer) = db(2) - db(1)      + db(2) - da(2)),      = e-YLL(lung cancer) + 0.5 (dsince no expected lung cancer deaths are to be subtracted.b(2) - da(2)) which is half of the causal effect of smoking on age at death from all causes  according to Figure Hence, e-YPLL(lung cancer) is biased upward by 0.5  for death from all causes can be accurately determined by calculating the corresponding excess Years of Potential Life Lost (e-YPLL) provided conditions hold that are also often cited for general study validity. However, the equality of e-YLL and e-YPLL does not hold in general under certain conditions of interest because under these certain conditions we need to assure criterion 2: each exposed subject has to have an ideal unexposed control partner so that the effect of exposure can be measured on the individual level . The equality also requires that this information about individual matching is used in the analysis. I pointed out that the e-YPLL conditional on age at death are potentially biased as they were published in some analyses  the aveStatistical measures that focus on the advancement of disease onset are assumed to be helpful in communicating risk factor impact on disease (e.g., Peto 1980 ). FischhStatistics conveying information about the advancement of disease onset are therefore helpful in exposure impact analysis and especially worthwhile in exposure impact communication. However, attention should be drawn to the difficulties involved and that epidemiologists should always be aware of the conceptual limits of the Years of Potential Life Lost method when applying it as a regular tool in cohort analysis.Aside from Years of Life Lost, other approaches are available to convey information on the impact of a risk factor on the onset of a disease and may thus facilitate communication of epidemiological findings. One such concept is the risk and rate advancement period (RAP) introduced by Brenner et al. 1993  which coThe main conclusions about non-identifiability of e-YLL are derived as an application of a causal theory based on counterfactuals (for a review see Greenland 2000 ). Some aThe ideas of counterfactual reasoning can at least be traced back to philosophers in the eighteenth and nineteenth centuries, like Hume and Mill. Even the oldest clearly structured theory of causality, developed by Aristotle, has some similarities to counterfactual reasoning due to its manipulative four-causes concept (Vorländer 1979 ). In itsSome authors resist a counterfactual approach and argue against the speculative and metaphysical background of counterfactual worlds (Dawid 2000 ): How caNote further that the mathematically consistent analytical treatment of causal questions by counterfactual theory is obviously related to the so called multiverse approach (Everett 1957 ). The laIn addition, this link to quantum mechanics disproves the repeatedly made statement by Dawid  that coWhereas the counterfactual approach can help to clarify terminology and substance of causal relations, it points simultaneously at some ambiguities when discussing competing causes of death. In this scenario, it is unclear what kind of action should be taken to cause a suppression of competing risks (Greenland 2002 ). Hence,In conclusion, the excess Years of Potential Life Lost estimates the excess Years of Life Lost due to exposure unbiasedly if we are interested in a) death from all causes and b) total excess Years of Life Lost summed up across the whole cohort. However, the method of calculating excess Years of Potential Life Lost due to exposure is potentially biased if it is applied 1) to estimate the impact of exposure on specific causes of death, like lung cancer, in the presence of competing causes or 2) to estimate the impact of exposure  conditional on age at death. These potential biases can be rather severe in published analyses  declares that he has no competing interests.The following example is based on the counterfactual framework presented in Chapter 1. It demonstrates that neither relative risks nor relative rates can be used in general to estimate the probability of causation unbiasedly. In particular, I show that an estimate of the attributable hazard derived from a Cox model (Breslow and Day 1987 ) fails tend>0. The time scale can be chosen arbitrarily as age, calendar time or time since start of follow-up without affecting the following arguments. I assume that the exposed subject A may experience the event (death) during the follow-up period at t1 years (t1 > 0), if unexposed counterfactually (A's twin) at t2 > t1, t2 < tend. No event may occur in subject B and his/her twin during follow-up (i.e., neither under exposure nor when unexposed). Thus, among the two exposed subjects, A and B, only one case occurs and this case is causally affected by exposure since t1 < t2. It follows that the probability of causation  is exactly 100%.The cohort is supposed to comprise four subjects: A, A's twin, B, and B's twin. In addition, it is assumed that the cohort is followed up in mortality until the fixed censoring date tIn contrast, the incidence proportion (Rothman and Greenland 1998 ) is 0.5 1 + tend) and among the unexposed is 1/(t2 + tend). Consequently, the rate ratio is (t2 + tend)/(t1 + tend) > 1 because t2 > t1. The attributable rate among the exposed can be determined as (rate ratio -1) / rate ratio = 1 - 1/rate ratio. Thus, I conclude 0 < 1 - (t1 + tend)/(t2 + tend) < 1, again proving a systematic underestimate of the true probability of causation among the exposed by the attributable rate ratio. Note that the rare disease assumption is of no help. Assuming n exposed subjects (n>>1) that neither react nor do their n unexposed twins, we get an attributable rate among the exposed of 1 - (t1 + (n)(tend)) / (t2 + (n)(tend)) < 1. If n approaches infinity the attributable rate among the exposed decreases to zero whereas the true probability of causation remains always constant at 100%. Hence, the discrepancy is even sharpened under the rare disease assumption.Note that a change from risks to rates does not overcome the problem. The rate among the exposed is 1/ comprises 4 subjects and 2 risk sets. One set is generated by the exposed case (A), the other set by the unexposed (A's twin). Therefore, a Cox analysis of the cohort yields the following.Assuming a relative hazard (rate) in the Cox model of0 = exp (β exposure), exposure = binary exposure indicator    λ / λwe get the partial likelihood    which is maximized at b = ln 2/2.Therefore, the estimated hazard ratio is    exp (b) = √ 2yielding an estimated attributable hazard among the exposed of-1 < 1 = probability of causation.1 - (√2)Note that the rare disease assumption is again of no help to overcome this discrepancy because the estimated attributable hazard among the exposed approaches 0 when the number of controls is rising indefinitely.Greenland 1999  emphasizLife table analysis with calculation of excess Years of Potential Life Lost e-YPLL according to Park et al. 2002. Basic data (unexposed) from BEIR IV (1988), Table 2A-10, p. 133: death rates of the male US population, surviving at least 30 years, applied to a birth cohort of 100,000. Exposure impact: advancement of certain fractions of deaths. For details of assumed mechanism see Table 2 .Click here for fileTwo different exposure-response mechanisms compatible with the life table analysis in Table 1. Fractions of the unexposed deaths are advanced by 0 yr or 5 yr according to mechanism 1 and by 0 yr, 5 yr or 10 yr according to mechanism 2. The age distribution of all exposed deaths is identical under both mechanisms. The distribution of the true excess Years of Life Lost e-YLL differs between mechanisms and both diverge from e-YPLL , whereas the totals agree. (advcm = advancement)Click here for fileLife table analysis with calculation of excess Years of Potential Life Lost e-YPLL and true excess Years of Life Lost e-YLL for overall and lung cancer mortality (ICD9-162). Basic data (unexposed) from BEIR IV (1988), Table 2A-10, p. 133: overall and lung cancer death rates of the male US population, surviving at least 30 years applied to a birth cohort of 100,000. Exposure impact: advancement of factual and hypothetical lung cancer deaths by 5 years, mixture of advancements among deaths from all causes. It is assumed that the advancement of hypothetical lung cancer deaths leads to an excess of 50% of lung cancer deaths among exposed in each age category. The overall e-YLL for lung cancer are less than the number of exposed lung cancer deaths times 5 years. The e-YPLL for overall death and lung cancer death are determined according to Park et al. 2002. For all deaths e-YPLL must equal e-YLL, but e-YPLL is obviously biased for lung cancer death.Click here for fileExcel sheet explaining the calculations in Additional files 1 and 2.Click here for fileExcel sheet explaining the calculations in Additional file 3.Click here for file


Phospholipase D (PLD) is involved in many signaling pathways. In most systems, the activity of PLD is primarily regulated by the members of the ADP-Ribosylation Factor (ARF) family of GTPases, but the mechanism of activation of PLD and ARF by extracellular signals has not been fully established. Here we tested the hypothesis that ARF-guanine nucleotide exchange factors (ARF-GEFs) of the cytohesin/ARNO family mediate the activation of ARF and PLD by insulin.Wild type ARNO transiently transfected in HIRcB cells was translocated to the plasma membrane in an insulin-dependent manner and promoted the translocation of ARF to the membranes. ARNO mutants: ΔCC-ARNO and CC-ARNO were partially translocated to the membranes while ΔPH-ARNO and PH-ARNO could not be translocated to the membranes. Sec7 domain mutants of ARNO did not facilitate the ARF translocation. Overexpression of wild type ARNO significantly increased insulin-stimulated PLD activity, and mutations in the Sec7 and PH domains, or deletion of the PH or CC domains inhibited the effects of insulin.Small ARF-GEFs of the cytohesin/ARNO family mediate the activation of ARF and PLD by the insulin receptor.The online version of this article (doi:10.1186/1471-2121-4-13) contains supplementary material, which is available to authorized users. Small GTPases of the ADP-ribosylation factor (ARF) family play a major role in membrane trafficking in eukaryotic cells . ARF act2+, protein kinase C, tyrosine kinases, and G proteins [Phospholipase D (PLD) catalyzes the hydrolysis of phosphatidylcholine (PC) to produce phosphatidic acid (PA). It is involved in a variety of signaling pathways and membrane traffic processes , 10. Manproteins –17. Amonproteins –24. HoweThis study was designed to investigate the role of ARNO in the regulation of PLD activity by insulin in HIRcB cells, a Rat-1 fibroblast cell line that overexpresses human insulin receptors. The objectives were: 1) to test if insulin induces the translocation of wild type ARNO to the plasma membrane in transiently transfected HIRcB cells; 2) to determine whether ARNO translocation is accompanied by activation and subcellular translocation of ARF; 3) to explore if overexpression of wild type ARNO in HIRcB cells alters insulin-dependent PLD activity; and 4) to investigate the function of individual domains of ARNO in insulin-dependent PLD and ARF activation.The translocation of ARNO and ARNO mutants to the membranes was studied in HIRcB cells using a digitonin permeabilization assay. For these experiments, HIRcB cells were transiently transfected with myc-tagged wild type ARNO and the following mutants: ΔPH-ARNO, PH-ARNO, ΔCC-ARNO, CC-ARNO, E156K-ARNO and R280D-ARNO. This assay is based on the formation of pores in the plasma membrane induced by digitonin to allow cytosolic proteins to leak out of treated cells upon centrifugation. Fig. Since ARNO is an activation factor of ARF, we tested the hypothesis that agonist-dependent ARNO translocation facilitates the local binding of ARF proteins to the membrane. An initial set of real-time studies was done using HeLa cells that had been stably transfected with an ARF1-GFP construct . These cOur previous work has shown that the insulin receptor co-immunoprecipitates with ARF in an agonist-dependent manner . FurtherFigure We then transfected HIRcB cells with myc-tagged ARNO constructs. Fig. We have shown so far that ARNO mediates the translocation of ARF proteins to the plasma membrane with insulin stimulation. Since ARF proteins mediate the activation of PLD by insulin , we testFig. Several studies have demonstrated that ARF proteins may mediate receptor-dependent activation of PLD. Stimulation of cell surface receptors with agonists, such as insulin, promotes the translocation of ARF proteins to the cell membranes and the activation of ARF proteins and the subsequent activation of PLD , 21, 23.ARF GEFs of the cytohesin/ARNO family have been shown to be recruited to cell membranes by mechanisms that are influenced by extracellular agonists , 26. TheOur studies showed that insulin promoted the translocation of myc-tagged ARNO constructs to the plasma membrane. This result is in agreement with data previously published by Venkateswarlu et al  and LangThe linkage between ARNO translocation to specific subcellular fractions and ARF activation was studied using myc-tagged ARNO and ARF-GFP constructs in two different cell types. Our data showed conclusively that insulin promoted the co-localization of wild type myc-ARNO and ARF1-GFP on the surface of HIRcB and HeLa cells. Interestingly, insulin, acting through ARNO, promoted the translocation of ARF1-GFP to the plasma membrane. ARF1, like most members of the ARF family, is primarily a cytosolic protein that exerts its function on specific membranes to which it is recruited by specific activators that promote the binding of GTP. However, ARF1 seems to act primarily at the Golgi, promoting the binding of coatomer proteins to the Golgi membrane , 37. NevThe ability of insulin to promote the translocation of ARNO and ARF to the plasma membrane correlated well with the ability of insulin to promote the activation of PLD. Therefore, our data support the hypothesis that the activation of PLD by insulin is mediated by ARF-GEFs of the cytohesin/ARNO family by a mechanism that involves the interaction of the PH and CC domains of these GEFs with some specific cellular targets. This conclusion is based on the demonstration that ARNO constructs with catalytically inactive domain or the mutants with defective PH and CC domains acted as dominant inhibitors of insulin-dependent PLD activation. The dominant negative effects of E156K-ARNO were not unexpected, since this mutant contains the intact PH and the CC domains and is therefore likely to compete with endogenous ARNO. The dominant negative effect of the PH and the CC domain deletion mutants on PLD activation was of particular interest. These mutants were at best partially translocated to the membrane but blocked the ability of insulin to promote ARF and PLD activation. This result was somewhat surprising since these deletion mutants contain an intact Sec7 domain and, therefore, would have been expected to support ARF and PLD activity. However, this was not the case, suggesting that all regions of ARNO play an important role in the regulation of this protein. Moreover, the failure of the ΔCC mutant to activate ARF and PLD indicates that other cellular targets that bind to the CC doma in of ARNO and regulate the subcellular location or the function of the signaling protein complex may exist. In fact, some proteins that interact strongly with the CC domain of members of the ARNO family, such as CASP and GRASP, have already been identified , 40. ConOn the other hand, our data also strongly support the hypothesis that the regulation of ARNO activity by insulin involves, at least transiently, a direct interaction of the insulin receptor with ARNO. Consistently, the presence of an ARNO-like activity and ARNO in the immunoprecipitated materials was confirmed by biochemical experiments. Finally, ARNO constructs lacking either the CC or the PH domain, or with a defective PH domain, failed to co-immunoprecipitate with the insulin receptor. These findings suggest a mechanism of the activation in which the binding of ARNO to the membrane is regulated by the insulin receptor at two different levels: 1) ARNO must interact with the receptor; and 2) ARNO must interact with the membrane, either via binding to polyphosphoinositides or through the interaction with specific protein targets. Our data strongly support the idea that both CC and PH domains play a crucial role in this phenomenon.This study suggests a general model for the activation of PLD with insulin stimulation. Insulin, upon binding to its receptor, promotes the phosphorylation of IRS-1 and the activation of PI3 kinase. This results in the accumulation of polyphosphoinositides on the plasma membrane. In parallel, the insulin-bound receptor promotes the recruitment of ARNO (and/or other members of the ARNO family, such as GRP-1) to the plasma membrane, either by direct interaction with their CC and PH domains or by promoting the interaction of ARNO with other as yet unidentified targets. The binding of ARF-GEFs to the plasma membrane is stabilized by the interactions of their PH domain with polyphosphoinositides generated by the action of PI3 kinase. Once on the membrane, the ARF-GEFs catalyze the activation of membrane-bound ARF6 or cytosolic ARF proteins that are then recruited to the membrane where they may activate PLD.Rat-1 fibroblasts overexpressing the human insulin receptors (HIRcB cells) were cultured in Dulbecco's modified Eagle's medium (DMEM)/Ham's F-12, supplemented with 10% fetal bovine serum, antibiotics, and 100 nM methotrexate, as previously described . Cells wHeLa cells were cultured in DMEM supplemented with 10% fetal bovine serum and antibiotics. HeLa-ARF1-GFP stable transfectants were obtained by using G418 as a selection agent as described elsewhere . Clonal Subconfluent (70–90%) HIRcB cells were transfected with LipofectAMINE (Invitrogen) for biochemical analyses or Superfect (QIAGEN) for imaging analyses. Transfection was performed according to the manufacturer's instructions. Transfection efficiencies were 70–90% for LipofectAMINE and 40–50% for Superfect transfection reagent as previously described .3 and PI-(4,5)-P3. The following myc-tagged ARNO constructs were generated: wt-ARNO, ΔPH-ARNO, PH-ARNO, ΔCC-ARNO, E156K-ARNO, and R250D-ARNO. wt-ARNO, ΔPH-ARNO (amino acids 1 to 269), PH-ARNO (amino acids 262–399), and ΔCC-ARNO (amino acids 51–399)  in PH domain . In thisTransfected and serum-starved HIRcB cells were washed with ice-cold PBS, scraped, and collected by centrifugation. The cell pellets were solubilized on ice for 1 hr in a solution of 50 mM Hepes, pH 7.45, containing 100 mM NaCl, 1.5% sodium cholate, 1 mM EDTA, 1 mM EGTA, 5 ug/ml leupeptin, 1 mM PMSF, and 1 mg/ml soybean trypsin inhibitor. Insoluble materials were removed by centrifugation. The cell lysate was immunoprecipitated with anti-mouse IgG agarose that had been equilibrated with a monoclonal antibody 83.7 (which recognizes the α subunit of the human insulin receptor). Immunoprecipitation was carried out overnight (approximately 20 hrs) at 4°C. The immunoprecipitates were washed with lysis buffer, resuspended in SDS-PAGE sample buffer, and subjected to Western blotting analysis.Proteins were separated by SDS-PAGE, transferred to a nitrocellulose membrane, and blocked with 5% non-fat milk in PBS containing 0.1% Tween at room temperature for 2 hrs. The membrane was then cut in half horizontally. The upper part was used to detect the β subunit of the insulin receptor with a monoclonal antibody, CT-1, that recognizes the carboxyl terminus of the β subunit of the human insulin receptor. The lower part was used to detect ARNO proteins with a monoclonal antibody anti-myc or a polyclonal antibody anti-GFP.3H-palmitate (5 μCi/ml) in serum-free medium. The cells were stimulated with insulin (100 nM) in the presence of 0.5–1% ethanol for 20 min. The reaction was stopped by addition of chloroform: methanol (1:1). The lipid phase was extracted and developed by thin layer chromatography (TLC) on silica gel 60 plates using ethyl acetate: trimethylpentane: acetic acid (9: 5: 2) as a solvent. The position of major phospholipids was determined using true standards  and autoradiography. The TLC plates were scraped and the total amount of radioactivity associated with each lipid species was determined by liquid scintillation counting. The data were expressed as the number of counts associated with the phosphatidylethanol (PtdEtOH) spot normalized by the total number of counts of lipid.Serum-starved HIRcB cells were labeled overnight with Serum-starved HIRcB cells were collected, resuspended in PBS, and treated with 10 μM digitonin in the presence or absence of insulin (100 nM), ATP (1 mM), and GTPγS (100 μM) at 37°C for 15 min. To release intracellular proteins, the digitonin-treated cells were centrifuged in a microcentrifuge for 20 min. The supernatants and the cell pellets were collected separately, and subjected to SDS-PAGE. ARNO proteins were detected by immunoblotting as described above.35S] (1 μCi) in 20 mM Hepes buffer containing 2 mM MgCl2/ 0.1% Na-cholate / 1 mM ATP. At the indicated time points, the reaction was quenched by addition of 100 μM ice-cold, unlabeled GTPγS and the protein-bound nucleotide was determined by filtration through nitrocellulose filters as described [ARF activation was determined by the binding of GTPγS to the purified, myristoylated recombinant human ARF1 (mhARF1), as described by Shome and coworkers . The insescribed .HIRcB cells were plated on poly-L-lysine coated glass coverslips and transfected with the constructs as indicated above. Cells were serum starved overnight and stimulated with 100 nM insulin. Live cells were imaged in a LSM5 Zeiss laser scanning confocal microscope equipped with a 63X oil immersion objective.For ARF and ARNO colocalization experiments, HIRcB cells were plated on poly-L-lysine coated coverslips as described above and co-transfected with myc-ARNO and ARF-GFP constructs using Superfect transfection reagent according to the manufacturer's instructions. Following insulin stimulation, the cells were fixed with 4% fresh paraformaldehyde in PBS at 4°C for 30 min, and permeabilized in 0.1% Triton X-100 at room temperature for 2 min. After permeabilization, the cells were blocked with 3% bovine serum albumin in PBS at room temperature for 30 min, and immunostained with a monoclonal antibody 9E10 (Upstate Biotechnology) that recognizes the myc epitope. After extensively washing, the cells were incubated with a Cy5-conjugated donkey anti-mouse secondary antibody (Jackson Immunoresearch) and imaged using a Zeiss laser scanning confocal microscope with filters appropriate for the detection of GFP and Cy5.


Human faces provide important signals in social interactions by inferring two main types of information, individual identity and emotional expression. The ability to readily assess both, the variability and consistency among emotional expressions in different individuals, is central to one's own interpretation of the imminent environment. A factorial design was used to systematically test the interaction of either constant or variable emotional expressions with constant or variable facial identities in areas involved in face processing using functional magnetic resonance imaging.Previous studies suggest a predominant role of the amygdala in the assessment of emotional variability. Here we extend this view by showing that this structure activated to faces with changing identities that display constant emotional expressions. Within this condition, amygdala activation was dependent on the type and intensity of displayed emotion, with significant responses to fearful expressions and, to a lesser extent so to neutral and happy expressions. In contrast, the lateral fusiform gyrus showed a binary pattern of increased activation to changing stimulus features while it was also differentially responsive to the intensity of displayed emotion when processing different facial identities.These results suggest that the amygdala might serve to detect constant facial emotions in different individuals, complementing its established role for detecting emotional variability. Facial expressions and facial identities are important cues for the evaluation of social contexts ,2. Two mVisual analysis of human faces has been suggested to be achieved by a core system comprising the fusiform gyrus together with the inferior occipital gyrus, the superior temporal sulcus, and the amygdala ,8. The aRecent advances to assess the effects of facial identity and emotional expression include one report on increased left amygdala activation to blocks of multiple novel vs. single identical faces displaying neutral or emotionless expressions . AnimateICE), (b) variable identity, constant expression (VICE), (c) constant identity, variable expression (CIVE), (d) variable identity, variable expression (VIVE). To control for attentional effects during the procedure, an oddball task was included to avoid confounds by an emotional judgment or a gender differentiation task .fearful expressions (shown in different identities) would elicit the strongest amygdala activation given the evidence from lesion and imaging studies that this expression is a particularly potent activator of the amygdala [Given that numerous reports demonstrate amygdala activation in the processing of sequential facial expressions ,12,27-29amygdala ,30,31. Bamygdala ,32 we sy1,10 = 4.859, p < 0.06), with longer RTs in the conditions of changing identities, but no significant effects of emotion or an interaction .Analysis of the behavioral data showed that subjects maintained a high degree of task attention ). There was a trend of a main effect of identity in the reaction times (RTs) to the oddball stimuli . For other brain areas the threshold was set to a threshold of p < 0.05, corrected for the entire brain. Post-hoc contrasts of specific experimental conditions for detailed characterization of the experimental effects were carried out at an uncorrected significance threshold.In the amygdala we expected to find higher responses to constant emotional expressions in different facial identities. Thus in our 2 × 2 factorial design, this can be formally tested by an interaction. Data for all experimental conditions in the peak activation voxels found in our masked interaction contrast  are shown in the top left and right panels of Figure ICE were only significantly more active than responses to CICE : T = 3.75, p < 10-4; right amygdala (18 0 -18): T = 4.76, p < 10-4), and also more active than the other two experimental conditions (CIVE and VIVE): left amygdala (-15 0 -15): VICE vs. CIVE: T = 1.77, p < 0.05; VICE vs. VIVE: T = 2.40, p < 0.01; right amygdala (18 0 -18): VICE vs. CIVE: T = 2.19, p < 0.01; VICE vs. VIVE: T = 3.30, p < 0.001.Post-hoc contrasts of the experimental conditions revealed that amygdala responses to VICE we decomposed this condition with respect to different emotional expressions. This analysis revealed that the VICE activation is primarily caused by the response to maximally fearful expressions and (to a lesser degree) to other emotional expressions, but only marginal to neutral expressions  for the respective boxcar and exponential decay regressor with the canonical response function created by SPM during design specification. Figure In order to characterize the time-course of the amygdala responses to the emotion-specific responses in condition VBased on previous work ,32 we exICE, CIVE, VIVE) than by the condition with repeated presentation of the same picture (CICE): left lateral fusiform gyrus (-39 -54 -24): VICE vs. CICE: T = 2.75 p < 0.01, CIVE vs. CICE: T = 3.09, p < 0.01, VIVE vs. CICE: T = 3.77, p < 0.001; right lateral fusiform gyrus (36 -54 -24): VICE vs. CICE: T = 2.98, p < 0.01, CIVE vs. CICE: T = 2.79, p < 0.01, VIVE vs. CICE: T = 3.01, p < 0.01.Pair-wise post-hoc contrasts of the four experimental conditions revealed that the lateral fusiform gyrus was significantly more activated by all experimental conditions with at least one changing stimulus feature : Fear Max vs. Fear Min: T = 2.6, p < 0.01; Fear Max vs. Neutral: T = 2.79, p < 0.001; Fear Max vs. Happy Min: T = 2.38, p < 0.01; right lateral fusiform gyrus (36 -54 -24): Fear Max vs. Fear Min: T = 1.72, p < 0.05; Fear Max vs. Neutral: T = 1.94, p < 0.05; Fear Max vs. Happy Min: T = 2.27, p < 0.01; indicated by asterisks (*) in the bottom panels of Figure A further post-hoc analysis of the condition Vus -39 -5 -24: FeaICE experimental condition we found an effect of emotion intensity in the fusiform gyrus, with stronger activations to maximally intense expression irrespective of valence when compared with neutral or modestly intense expressions. While we found a (non-significant) trend for the same modulation of the VICE activation by emotion intensity in the left amygdala the general pattern when including the right amygdala rather conforms to a specific sensitivity for maximally fearful expression which elicited the strongest activation.We investigated the interaction of facial identity and expression in 2 × 2 factorial blocked fMRI study which uniquely enabled us to compare all combinations of facial identity and expression within the same experiment, therefore the results of the present study complement and extend the results of previous studies on facial identity and expression processing in the human brain. In support for our hypotheses we found distinct response patterns in bilateral amygdala and bilateral lateral fusiform gyrus. While the lateral fusiform gyri can be characterized by a binary response pattern corresponding to an effect of changing stimulus feature, the amygdala responded maximally to constant emotional facial expressions in combination with changing facial identity. In addition, in the VICE of the present study) compared to blocks of the repetition of the same identity (our condition CICE) [The binary response pattern conforming to sensitivity for changes in stimulus features in the lateral fusiform gyrus might be explained by reference to the proposed network of face processing in humans . Haxby aon CICE) . Anotheron CICE) . Our datICE with higher activation to maximally fearful and happy expressions relative to with neutral expressions, paralleling earlier findings which report stronger activation to fearful than to neutral expressions [ICE), suggesting an identity repetition × emotion interaction [Additionally, we found an effect of emotion intensity in the fusiform gyrus activity in condition Vressions ,27,33,35eraction , while oeraction . This aperaction , which aeraction , as theyTwo recent studies suggest that the low frequency information of facial stimuli might drive the modulatory effect of arousal, which is thought to be a feedback influence of the amygdala, which also displays a preference for low frequency information ,35.ICE) compared to all three other main conditions (CICE, CIVE and VIVE). Furthermore, within this condition (VICE) maximally fearful expressions elicited the strongest amygdala activity. The result of the present study replicates and extends, in part, the earlier findings that also utilized blocked presentations of facial expression with stimulus configurations resembling one or more of our experimental conditions  as compared to static and repeated presentation of the same facial stimuli (the condition CICE in the present study). A closer inspection of our findings in Figure ICE condition still elicited a larger signal change than the condition CICE. Thus, our comprehensive factorial design can relate these earlier findings in the broader context of constancy and variability of identity and expression.Our 2 × 2 factorial blocked fMRI design allowed us to compare between the possible conditions of constant and variable facial identity and expression. We show for the first time a maximum of activation of the amygdala to variable facial identities displaying the same emotion . In liglleagues  also repICE supports this claim. However, recent studies, which used different sensory modalities and carefully controlled for the often confounded dimensions of valence and intensity, argued for an effect of emotion intensity  in the amygdala [Many imaging and lesion studies have documented activations or behavioral impairments following the presentation of fear-related stimuli (for review see ). Our deamygdala -42. Furtamygdala . The autInterestingly, Winston and colleagues , employiICE condition in the present study in terms of presented stimuli but not with respect to block length and block order. Interestingly, when the CICE condition in the present study is analyzed for the comparison of maximum fear vs. neutral blocks a non-significant trend for left ventro-lateral amygdala activity (data not shown) is seen, indirectly supporting those findings.The temporal properties of amygdala responses to facial expressions are crucial. Many studies show habituation of the amygdala response to blocks of directly adjacent fearful vs. neutral or happy expressions with constant identities over the course of 30 to 80 sec ,15,38. TICE conditions showing within-block and across-block habituation with fixed alternating block order [ICE vs. CICE blocks [ICE and CICE conditions in the amygdala. The response profile of the amygdala for maximal fearful expressions in the VICE condition trends toward a within-block habituation effect  were reduced because within those blocks variable emotional and neutral expressions were intermixed, possibly leading to smaller signal changes within those blocks. However, subjects were presented with the same number of stimuli of each emotional expression in every experimental condition. Therefore, differences cannot be attributed to a varying number of emotional expressions seen in different conditions. Thus, if the conditions with variable emotions elicit a smaller signal change because of the neutral faces within them, this effect should also apply to the blocks of constant neutral expressions in different faces (VICE condition). This effect is in fact shown in the lower bar graphs in Figure ICE conditions. Because we found an overall elevated response in the amygdala to the condition with constant emotions in changing identities (VICE) compared to conditions with variable emotions , this does not diminish the main point of this study, namely that the human amygdala is most responsive to the sequential presentation of faces with constant emotion (fearful) and varying identity. To further confirm and generalize this finding future studies are needed using additional emotions (e.g. anger, sadness, etc.).One might argue that the responses in the conditions with variable emotional expression  are compared to each other to detect changes to stability in the emotion, a concept that can be described as Furthermore, facial expressions differ in their degree of saliency which often reflects the biological relevance of the stimulus that evoked the expression ,44. For ICE and found significant activation in the left dorsal amygdala with constant fearful expressions [IVE, VIVE) can be seen as noise in the context of the detection of constant emotions among others and thus, it is not surprising that the amygdala shows less signal change in these conditions.Our findings gain support and extend the findings of earlier imaging studies on the processing of human facial emotions which employed an experimental design similar to our condition Vressions ,12. TakeThe perception of emotional facial expressions in others yields insights into their evaluations of the environment and guides one's own emotional and behavioral reactions. Encountering the same emotional expression in many different people (emotion constancy) is an especially valid and readily available cue for making subsequent inferences about the potential harmfulness of an environmental situation. These inferences would have direct implications for evolutionary survival and must have been a central feature of human ancestral cognitive abilities . They alEmotional facial expressions are an important cue for the appraisal of an environmental situation. Our study has demonstrated a new perspective on the functional characterization of the amygdala involved in the perceptual processing of human faces by incorporating the dimensions of constancy and variability detection in these stimuli. These are essential for the assessment of the temporal dynamics of social situations.ICE), (b) variable identity, constant expression (VICE), (c) constant identity, variable expression (CIVE), (d) variable identity, variable expression (VIVE). Figure A 2 × 2 factorial design  across 4 different block types was used: (a) constant identity, constant expression  participated in this fMRI study. The mean age was 25.6 (SD 7.8). The data sets of two subjects were excluded from further image analysis because of radio frequency artifacts caused by the scanner leaving a total of 11. All subjects were fully informed about the experimental procedure and signed a consent statement which was approved by the local ethics committee.The procedure was completed in one imaging session with 4 runs each containing 20 blocks of stimulus presentation. Within one run the blocks were interleaved with 15 s of central fixation (rest period). Each run started with a rest period of 20 s. 20 stimuli were presented per block at a rate of 1 Hz. Stimuli were shown for 900 ms interposed with a 100 ms gray blank of mean luminance to make transitions between stimuli less abrupt. Numbers and types of both facial expressions and facial identities were counterbalanced across the entire experiment. Conditions varied only in terms of the sequential configuration of the stimuli within the blocks. Stimulus order within each block was pseudo-randomized and fixed. The sequence of blocks within each run was counterbalanced, pseudo-randomized and fixed to minimize stimulus order effects. Additionally, the sequence of the four runs was pseudo-randomized across subjects assuring different orders of runs in each subject.Stimuli consisted of 4 different faces  drawn from the Ekman series of facial affect  with 5 dIn order to maintain and control for attentional effects during the procedure we included an oddball task as we were seeking to avoid confounds by an emotional judgment or a gender differentiation task . As odd2, matrix 64 × 64) were acquired. A total of 204 volumes were collected per run.Imaging was performed on a 1.5 T Magnetom Vision (Siemens, Erlangen, Germany) scanner. 43 transversal slices of echo-planar (EPI) T2* weighted images in each volume with a slice thickness of 2 mm and 1 mm gap  allowed a decomposition into emotion-specific components. Thus each of these two conditions involved 10 regressors , while each of the other conditions (CIVE, VIVE) were specified with two regressors. Data were high-pass filtered at 1/120 Hz. Serial autocorrelation was controlled by superimposing a known autocorrelation in form of temporal smoothing using a low-pass filter at 4 sec filter width. Successively, contrasts for each experimental condition were computed by averaging the same block type across runs and multiplying the design matrix with the contrast vectors.The data of 11 subjects were included in the statistical analysis. Data analysis was performed using the mass univariate general linear model as implemented in SPM99 and commenced by specifying the design matrix for each subject using a boxcar and an exponential decay regressor for modeling the hemodynamic response to each experimental condition. The boxcar regressor models the mean activation within the block while the exponential decay regressor (time constant 4 s) models decreases and increases (through negative contrast weights) within the block. The conditions with constant emotional expressions (CThese single-subject contrast images were then taken to the second level oneway ANOVA ,53 in SPICE > CICE) > (VIVE > CIVE), p < .01 for the purpose of visualization] for the amygdala and masked it with the contrast [(VICE > CIVE), p < .05] to exclude regions showing higher activations to CIVE than to VICE. For our hypothesis in the fusiform gyrus we used a contrast that compared conditions with at least one changing stimulus feature  with the condition in which the same stimulus was shown for the entire block [(VICE + CIVE+ VIVE) > 3 × CICE].In order to detect voxels that show elevated responses to the same expression displayed in different individuals we constructed the interaction contrast of our 2 × 2 design. Hence, we created the contrast [ which refer to the probabilistic behavior of Gaussian random fields . Our thrzer task ; see TabICE (Figure For the emotion-specific analyses of condition VEPI: echo-planar imagingfMRI: functional magnetic resonance imagingLOC: lateral occipital complexRT: reaction timeSPM: statistical parametric mapIVE: variable identity, variable emotionVICE: variable identity, constant emotionVICE: constant identity, constant emotionCIVE: constant identity. variable emotionCFear/Happy Max: maximally fearful/happy expressionFear/Happy Min: minimal fearful/happy expression.J.G. and O.T. designed, coordinated, and conducted data collection, analysis, and interpretation. C.B. and C.W. conceived of the study and participated in its design, analysis and interpretation. All authors read and approved the final manuscript.


Endurance exercise training can promote an adaptive muscle fiber transformation and an increase of mitochondrial biogenesis by triggering scripted changes in gene expression. However, no transcription factor has yet been identified that can direct this process. We describe the engineering of a mouse capable of continuous running of up to twice the distance of a wild-type littermate. This was achieved by targeted expression of an activated form of peroxisome proliferator-activated receptor δ (PPARδ) in skeletal muscle, which induces a switch to form increased numbers of type I muscle fibers. Treatment of wild-type mice with PPARδ agonist elicits a similar type I fiber gene expression profile in muscle. Moreover, these genetically generated fibers confer resistance to obesity with improved metabolic profiles, even in the absence of exercise. These results demonstrate that complex physiologic properties such as fatigue, endurance, and running capacity can be molecularly analyzed and manipulated. Engineered expression of the peroxisome proliferator-activated receptor δ in skeletal muscle increases type I muscle fibers allowing the modified mice to run twice the distance of wild-type littermates Skeletal muscle fibers are generally classified as type I (oxidative/slow) or type II (glycolytic/fast) fibers. They display marked differences in respect to contraction, metabolism, and susceptibility to fatigue. Type I fibers are mitochondria-rich and mainly use oxidative metabolism for energy production, which provides a stable and long-lasting supply of ATP, and thus are fatigue-resistant. Type II fibers comprise three subtypes, IIa, IIx, and IIb. Type IIb fibers have the lowest levels of mitochondrial content and oxidative enzymes, rely on glycolytic metabolism as a major energy source, and are susceptible to fatigue, while the oxidative and contraction functions of type IIa and IIx lie between type I and IIb . Adult sMuscle fiber specification appears to be associated with obesity and diabetes. For instance, rodents that gain the most weight on high-fat diets possess fewer type I fibers . In obesWe have previously established that peroxisome proliferator-activated receptor (PPAR) δ is a major transcriptional regulator of fat burning in adipose tissue through activation of enzymes associated with long-chain fatty-acid β-oxidation . AlthougA role of PPARδ in muscle fiber was suggested by its enhanced expression—at levels 10-fold and 50-fold greater than PPARα and γ isoforms, respectively (unpublished data). An examination of PPARδ in different muscle fibers reveals a significantly higher level in type I muscle (soleus) relative to type II–rich muscle (extensor digitorum longus) or type I and type II mixed muscle (gastrocnemius) A; this eTo directly assess the role of activation of PPARδ in control of muscle fiber plasticity and mitochondrial biogenesis, we generated mice expressing a transgene in which the 78-amino-acid VP16 activation domain was fused to the N-terminus of full-length PPARδ, under control of the 2.2-kb human α-skeletal actin promoter. In agreement with the previous characterization of this promoter , the VP1Type I muscle can be readily distinguished from type II or mixed muscle by its red color, because of its high concentration of myoglobin, a protein typically expressed in oxidative muscle fibers. We found that muscles in the transgenic mice appeared redder A, which A number of previous studies have shown that obese individuals have fewer oxidative fibers, implying that the presence of oxidative fibers alone may play a part in obesity resistance. To test this possibility, we fed the transgenic mice and their wild-type littermates with a high-fat diet for 97 d. Although the initial body weights of the two groups were very similar, the transgenic mice had gained less than 50% at day 47, and only one-third at day 97, of the weight gained by the wild-type animals A. The trp > 0.35, n = 4) were observed between the transgenic and control mice. Thus, the remarkable increase in endurance is the physiologic manifestation of muscle fiber transformation. This suggests that genetically directed muscle fiber switch is physiologically and functionally relevant. In addition, we looked at what effect the absence of PPARδ function has on exercise endurance. In the treadmill test, the PPARδ-null mice could sustain only 38% of the running time and 34% of the distance of their age- and weight-matched wild-type counterparts  of VP16 was fused in frame with the N-terminus of mouse PPARδ. The VP16-PPARδ fusion cDNA was placed downstream of the human α-skeletal actin promoter , and upsMouse EST clones were obtained from ATCC (Manassas, Virginia, United States), verified by sequencing, and used as Northern probes. Antibodies were obtained from Santa Cruz Biotechnology . Total muscle protein extracts  and nuclPrior to the exercise performance test, the mice were accustomed to the treadmill (Columbus Instruments, Columbus, Ohio, United States) with a 5-min run at 7 m/min once per day for 2 d. The exercise test regimen was 10 m/min for the first 60 min, followed by 1 m/min increment increases at 15-min intervals. Exhaustion was defined when mice were unable to avoid repetitive electrical shocks.Muscle fiber typing was essentially performed using metachromatic dye–ATPase methods as described . Muscle p-values.Number of mice for each group used in experiments is indicated in figure legends. Values are presented as mean ± SEM. A two-tailed Student's t test was used to calculate Video S1This video shows the exercise performance of a representative of the transgenic mice (right chamber) and a representative of wild-type control littermates (left chamber) on the treadmill 15 min into the exercise challenge.(52.4 MB MOV).Click here for additional data file.Video S2This video shows the exercise performance of a representative of the transgenic mice (right chamber) and a representative of wild-type control littermates (left chamber) on the treadmill 90 min into the exercise challenge.(41.7 MB MOV).Click here for additional data file.


One recovery gradient undergoes natural succession to mature tropical rainforest, while the other involves plantation of jhum fallows with teak Tectona grandis monoculture.Community recovery following primary habitat alteration can provide tests for various hypotheses in ecology and conservation biology. Prominent among these are questions related to the manner and rate of community assembly after habitat perturbation. Here we use space-for-time substitution to analyse frog and lizard community assembly along two gradients of habitat recovery following slash and burn agriculture (Frog and lizard communities accumulated species steadily during natural succession, attaining characteristics similar to those from mature forest after 30 years of regeneration. Lizards showed higher turnover and lower augmentation of species relative to frogs. Niche based classification identified a number of guilds, some of which contained both frogs and lizards. Successional change in species richness was due to increase in the number of guilds as well as the number of species per guild. Phylogenetic structure increased with succession for some guilds. Communities along the teak plantation gradient on the other hand, did not show any sign of change with chronosere age. Factor analysis revealed sets of habitat variables that independently determined changes in community and guild composition during habitat recovery.jhum cultivation cycles and plantation forestry could result in landscapes without mature forest. Lack of source pools of genetic diversity will then lead to altered vegetation succession and faunal community reassembly. It is therefore important that the value of habitat mosaics containing even patches of primary forest and successional secondary habitats be taken into account.The timescale of frog and lizard community recovery was comparable with that reported by previous studies on different faunal groups in other tropical regions. Both communities converged on primary habitat attributes during natural vegetation succession, the recovery being driven by deterministic, nonlinear changes in habitat characteristics. On the other hand, very little faunal recovery was seen even in relatively old teak plantation. In general, tree monocultures are unlikely to support recovery of natural forest communities and the combined effect of shortened  Evaluation of the importance of various processes determining community structure and function is an important topic in ecology. Unlike just a decade or so ago, few studies today question whether or not community assembly is strictly random, recognizing the role of both stochastic and deterministic processes . This chThis newfound view of community ecology is an excitingly realistic one, and has the potential to make valuable contributions to conservation biology as well ,7. HowevCircumventing this problem is obviously very difficult. One possible approach is to study communities along gradients of habitat succession using space-for-time substitution (SFT) to obtain chronosequential communities . Thus, ijhum fallows giving way to mature forest, and (b) 1-yr jhum fallows planted over with teak, leading to monoculture stands. Slash-and-burn or shifting cultivation (jhum) agriculture involves clearing and burning of forest patches, so the original rainforest communities are effectively obliterated, and succession involves recovery of communities from scratch. The following questions were addressed in this study:This study takes an SFT approach to compare changes in frog and lizard community structure in two contrasting habitat succession gradients: (a) 1-yr 1. How much does frog and lizard community succession differ between the two gradients of habitat recovery?2. Does composition of the entire community change in synchrony, or does the recovery pattern differ between subcommunities such as frogs vs. lizards and guilds?3. What aspects of habitat change influence frog and lizard community recovery, and if habitat parameters are linked to niche axes, do they predict changes in guild composition?4. Do successional changes in guilds also show trends in phylogenetic structure? This last question is expected to yield interesting insights into possible evolutionary mechanisms underlying changes in community composition , but hasIn this paper, a chronosere is defined as a habitat that has recovered from perturbation for a known length of time, and can be assigned a place in the SFT. An assemblage is the set of all species of a taxonomic group in a landscape of interest. Ecological groups (EGs) are species' subsets of the assemblage with similar niche characteristics. Communities comprise species of the assemblage which share a habitat stratum (i.e., chronosere) in the landscape. Guilds are members of the EGs that actually coexist in the same chronosere i.e., belong to the same community, and are thus likely to have ecological and evolutionary interactions (cf. ).To draw inferences about what aspects of habitat change determine sequential communities, habitat and frog-lizard community data were analysed hierarchically. As a first step, species richness and turnover of frog and lizard communities along habitat recovery gradients was summarised, and the entire assemblage classified into ecological groups (EGs) based on niche similarities. Guilds identified from this classification were then examined for phylogenetic structure. Using factor analysis, orthogonal combinations of variables that described biotic and abiotic aspects of habitat transition were extracted. We then tested for correspondence between these composite variables and composition of frogs and lizard communities and guilds. Based upon the relationships between different habitat factors and frog and lizard communities, variables were interpreted as composite adaptive zones, and we tested whether they predicted successional changes at different levels of community organization.jhum fallows (plots jh1A and B) were dominated by herbaceous plants, tall grass, shrubs and wild bananas, along with saplings and surviving crop plants. The 4 to 5-yr post-jhum plot (jh5) was dominated by almost homogeneous stands of the bamboo Melocanna baccifera, interspersed with a few shrubs and trees. Herbs were rare, and the understorey sparse. The 7 to 10-yr post-jhum plot (jh10) was very similar to jh5. However, here the bamboo culms were more sparsely distributed, and along with M. baccifera, two other bamboos- Dendrocalamus longispathus and Bambusa tulda were in greater abundance, and woody plants were relatively more common. Compared to the other plots, a larger area was included in the 30 to 35-yr jhum plot (jh35) because it contained a greater range of ages and hence perhaps more variability. Also, this was the only accessible site in the study area that represented a chronosere aged between 30–50 years. Although M. baccifera was common, this site had a greater abundance of other bamboos and trees than any of the previous stages. Though most trees were small, woody vegetation formed a significant part of the canopy. Herbs and shrubs were rare, and the understorey generally sparse.Table The three mature forest plots (matA, B, and C) were of untraceable age . They wLantana camara) and occasional herbs. The 22-yr teak plantation site (tk22) had a monotonous, uniform structure characteristic of a mature, managed teak monoculture. Undergrowth was sparse, consisting mostly of tall grass and Lantana sp. Table The 4-yr teak plot (tk4) was a young plantation characterized by a monodominant stand of teak trees. The understorey was sparse, with some tall grass, shrubs , and by Varimax rotation of the factor structure, which explained a cumulative 85.8% of the variation (see methods). Eigenvalues, factor loadings and factor scores are given in In general, although there is a change towards a tree dominated habitat in both recovery gradients, the end result is very different because the 22 year teak plantation is a monoculture, whereas the mature forest consists of a diverse tree community.jhum fallows and teak plantation communities on one hand, and the mature forest and the 35 year jhum fallows on the other.The three sampling techniques used in conjunction during the study (see methods) yielded sixteen frog and seventeen lizard species. Figure jhum to mature teak plantations on the other hand, seems to show little change in species richness or composition even after 22 years of plantation growth.The pattern of recovery is very different for the two gradients. For the mature forest succession gradient, the rate of frog and lizard community recovery is similar to that found for birds by Raman  in the sIt is worth noting that there are dissimilarities in the manner of species accumulation for frogs vs. lizards. There is much less augmentation of species number in the case of the latter, the main reason for this being that younger chronoseres support more lizard than frog species richness. Species accumulation curves see  show thaEcological groups defined by non-metric multidimensional scaling (NMDS) of the niche based dissimilarity matrix for the entire assemblage are shown in Figure Figure Figure These results on successional changes in guild structure and representation indicate a distinctly non-random sequence of community assembly, as certain guilds appear in later stages, followed by increase in their species richness and in many cases, phylogenetic structure. Habitat attributes that determine these changes are explored in the next section (see below).Table 2 = 0.85, and 0.97, respectively), and represents deterministic, linear aspects of vegetation succession. It has high positive loadings for tree species richness, and macro-habitat variables such as tree density, canopy cover, and canopy height, most of which increase deterministically along both gradients of habitat change. Among the measured variables, these are primary and independent, which over time drive changes in secondary (microhabitat) variables such as bamboo density, shrub abundance, and various measures of habitat heterogeneity (see methods). This factor clearly influences species composition at all levels of frog and lizard community structure.The strongest association is between factor 2 and overall species composition (frogs and lizards combined) across chronoseres. Factor 2 was strongly and non-linearly correlated with age along both teak and mature forest succession gradients  and diurnal-terrestrial (DT) groups, were correlated with factor 2. This suggests that in contrast to other guilds, these two, which are both made up only of lizards, are directly influenced by a hierarchically higher order of habitat attributes.These were also the two groups that showed non directional trends in species richness as well as phylogenetic trends along habitat recovery gradients Figures  and 6.nd order polynomial fit, R2= 0.99, and 0.82, respectively). This factor had high loading . This factor had no strong loadings, but is associated with shrub density, canopy height variability and tree density, all of which also affect ground cover, and can be considered macrohabitat variables.Along with factor 2, the crepuscular-nocturnal terrestrial (CT) group was correlated with factor 1 and 4. Factor 1 scores increase and then decrease with plot age along both habitat recovery gradients (22 = 0.209 and 0.18 for teak and mature forest gradients, respectively). It has high positive loading for CV of soil moisture, which as mentioned above, is highest in chronoseres with spatial and/or temporal variation in insolation. Among the measured variables, factor 2 probably subsumes most habitat parameters that affect both NA groups directly (see next section).The nocturnal arboreal frog group (NA(F)), was correlated with factor 7, which is non-deterministic with respect to chronosere age. This factor as a high loading for soil moisture, which by itself is difficult to interpret as a variable directly affecting this ecomorphological group. It is likely that this factor is a surrogate for an unmeasured or unclassified variable. Lastly, the nocturnal arboreal lizard group (NA(L)) is correlated with factor 8 along with factor 2. Factor 8 shows a weak negative linear relationship with recovery age along both gradients gradient . It canFactors 3, 5, and 6 showed no significant association with any level of community composition. The obvious reason for this appears to be that unlike other factors, these are completely non-deterministic with respect to age of succession, thus representing temporally and/or spatially stochastic attributes that were unlikely to show any influence on the conspicuously deterministic nature of frog and lizard community and guild (except for the DT and DA groups) succession  The two gradients of habitat recovery are very different and accordingly affect frog and lizard community assembly differently, (2) Although both groups increased in species richness with habitat recovery, lizards had higher species turnover, combined with lower species augmentation within each recovery gradient (3) Looking at a finer scale of community organization, assembly appears to be driven by changes in guild representation and composition, where some guilds change directionally with age of habitat recovery by species augmentation, while others change by species turnover (4) Guilds that showed directional increase in species richness also increased in phylogenetic structure (5) Hierarchies of community organisation were affected by composite, nested habitat attributes that correspond to particular niche axes, and (6) the increase in species richness along the mature forest gradient in contrast to lack of change along the teak gradient was due to availability (or lack thereof) of variables that comprise these complex adaptive zones. Also, the results show that a niche-based guild classification reveals patterns that would have been hidden in the gross response pattern of the entire community.Some indication of the qualitative nature of potential evolutionary and ecological processes in community turnover comes from the fact that changes in phylogenetic structure are tied to guild structure in the communities. Using phylogenetic techniques, recent work has demonstrated the importance of evolutionary adaptation in assembling ecological communities . It is cIt is an open question as to what extent vegetation succession leads to changes in the number of adaptive peaks and corresponding changes in mean fitnesses of species' populations such that multiple species can persist in the same habitat. In more ecological terms this is same as asking how habitat succession leads to changes in niche availability, occupancy, and overlap (due to character displacement, for example). Another related question, that was partly explored using the S/G ratios in this paper, is whether similar adaptive zones (or niches or adaptive peaks) tend to be occupied by more closely related taxa. The results here do indicate that this may be true for such gradients of community change, as phylogenetic and guild structure increase directionally and in tandem with succession towards mature forest. Whether this change is driven by immigration from the regional gene pool or due to local divergent adaptation is an interesting question . Reptilejhum-rainforest succession gradient, which is about 30 years for both frogs and lizards, and suggests that recovery of diverse communities can be relatively fast, as has been reported for other fauna [jhum chronoseres are gradually replaced by trees. This vegetation succession is obviously reliant on seed rain/dispersal from nearby mature forests. In this region and many other areas Southeast Asia, apart from continued pressure from shifting cultivation and shortening cultivation cycles, it has also become popular practice to plant and maintain monocultures of timber species. As the results of this study indicate, such plantations are unlikely to support natural recovery of faunal communities, and will harbour lower biological diversity compared to primary forest.The time scale of recovery on the er fauna . Howeverjhum cycles, plantation forestry and invasion by non-native species such as Lantana and Eupatorium will lead to the local extirpation of even remnant forest patches. This loss of recolonisation pools for flora and fauna, will alter natural trajectories of succession, and strongly impact the biological diversity supported by the landscape. It is therefore important that conservation and prioritisation agencies in these areas consider the value of habitat mosaics containing even small patches of primary forest vegetation.It is possible that the combined effects of short Tectona grandis plantations and abandoned shifting cultivation (jhum) fallows of varying ages. All primary forest is referred to as "mature forest" throughout the paper because it is often difficult to determine the age of ostensibly primary tropical forest, especially in areas with poorly known history of land use and recovery [The study was carried out from November 1998 to April 1999 in and around Ngengpui Wildlife Sanctuary  in south Mizoram, Northeast India. The study area covers about 200 sq. km. see  for mapsrecovery ,33. Furtrecovery .Ten sampling plots representing mature and successional vegetation stages of known ages were established  Table 11. To conVegetation composition and habitat structure variables were sampled on randomly located 10 × 25 m belt transects ,17. TranPrincipal Components Analysis (PCA) was used to identify different aspects of habitat change with vegetation succession and collapse the list of raw variables into composite factors that could potentially predict frog and lizard community structure. The factor structure was rotated using the Varimax method to obtain clear loading patterns . As addiAs the objective of the analysis was to combine variables into composite, orthogonal factors that could potentially account for community and guild structure, all factors with eigenvalues = 0.8 were extracted, irrespective of the number of factors thus extracted. Although somewhat arbitrary, in essence this eigenvalue threshold ensured that a factor was included only if it extracted approximately as much as one raw variable . AlthougThe low abundance of amphibians and reptiles and unstandardised sampling methodology in tropical Asia reduces the reliability of species diversity estimates and hence community structure analyses . Taking To improve detection and gather information for delineation of EGs (see below), the traditional transect method was modified by eliminating pseudoreplication and sampling both nocturnal and diurnal species on the same transect ,35. The within a chronosere, it varied considerably across plots. The DAW time on the other hand, was more or less consistent. This strategy was used because just as sampling effort needs to be proportional to habitat heterogeneity, higher microhabitat complexity calls for proportionally greater searching effort. Figure Although time taken for the NAW was more or less constant This technique was used to supplement species inventorying from the belt transects, and for an unbiased measure of the effects of weather on herpetofaunal activity and hence sampling efficiency. Comparisons of trapping frequency across plots over the study period are not used in this paper. Each pitfall array was, 'Y'-shaped, with three terminal (30 cm diam. × 60 cm depth) and one central (50 cm diam. × 70 cm depth) cylindrical aluminium funnel pitfall traps buried in the ground. The traps were connected with three opaque plastic-sheet fences (the arms of the 'Y') 0.4 m high and 5 m long, held up by bamboo stakes. In all, 22 arrays were placed, with two in each plot except for the large Jh35, which had four. Arrays were at comparable distance from plot edges, and on similar slope. Systematic trapping was initiated ten days after trap were established. Traps were opened for 5–10 consecutive days, and checked according to habitat characteristics, taking into consideration the level of exposure trapped animals were likely to be subjected to; plots with open habitat, such as jh1A were checked most  and those with relatively closed habitat such as matA were checked least frequently (every third day). Most specimens (95.2 %) obtained from pitfall trapping were released a minimum of 100 m away from the array, either in the same site, or in similar habitat elsewhere. A few were retained as voucher specimens.At the end of a sampling session in a plot, far ranging searches were carried out. This augmented species inventorying, and provided information crucial for EG classification (see below). Periodically, nocturnal searches were also made to collect information about the refuge of diurnally active animals, and also to confirm the presence or absence of species in different chronoseres.Irrespective of the sampling technique, animals detected were caught whenever possible, and identified in hand. All those that escaped were identified to a justifiable level or excluded from the analyses. A few individuals of taxonomically problematic species or taxa were preserved for later identification.The effectiveness of sampling was evaluated by species accumulation curves see , and theOverlap between recovering frog and lizard communities was measured with the Bray-Curtis measure between all possible pairs of chronoseres using presence absence data of all species see . The resLife history and behavioural traits were used to group species. These are often called guilds (e.g., ), but arPhylogenetic structure was measured as the ratio of number of species to the number of genera (S/G ratio) in each EG. A similar approach has been used in studies addressing questions about phylogenetic structure in ecological communities .To test which habitat attributes influenced community structure, Mantel tests of correspondence between dissimilarity (distance) matrices ,19,38,39To test whether the availability of composite variables (PCA factors) that predicted community and guild structure identified by the matrix correspondence tests did indeed influence community succession and phylogenetic structure along gradients of habitat recovery, correlations between sums of factor scores and species richness, ratio of species number/guild number and S/G ratios across chronoseres were tested.SSP conceived the study, carried out the fieldwork, performed the data analyses, and drafted the manuscript. BCC and GSR participated in design and coordination of the study. GSR also supervised the vegetation identification and habitat classification. All authors read and approved the final manuscript.Photographs of habitat types. Representative photographs of habitat typesClick here for fileResults of PCA along with list of habitat variables used. PCA ResultsClick here for fileSpecies accumulation curves and frog and lizard species' lists. Species accumulation curves and listsClick here for fileMaps of study area. Location map of study area sampling plots with respect to vegetation typesClick here for file