Cycles in insect populations: delayed density dependence or exogenous driving variables?

Summary Delayed density dependence, and the cycles in insect populations that it can generate, are often investigated using time-series analysis. Recently, several authors have raised concerns about the validity of using time-series analysis to detect density dependence. One particular concern is the suggestion that exogenous driving variables, such as cyclic weather patterns, can lead to the spurious detection of density dependence in natural populations.
Using non-biological data (the electricity bills of one of the authors), we show how easy it is to be misled by the results of time-series analysis. We then present 16 years' data on the gall-forming sawfly, Euura lasiolepis (Hymenoptera: Tenthredinidae), and show that cycles in weather, specifically winter precipitation, lead to the spurious detection of density dependence in time-series analysis. We conclude that time-series analysis cannot stand alone as a method for inferring the action of density dependence, and urge further investigation of the effects of apparent cycles in abiotic forces on insect populations.     

New insights into testing for density dependence

The reasons why tests for density dependence often differ in their results for a particular time-series were investigated using modelled time-series of 20 generations in lenght. The test of Pollard et al. (1987) is the most reliable; it had the greatest power with the three forms of density dependent data investigated (mean detection rates of 50.8–61.1%) and was least influenced by the form of the density dependence in time-series. Bulmer's first test (Bulmer 1975) had slightly lower power (mean detection rates of 27.4–56.8%) and was more affected by the form of density dependence present in the data. The mean power of the other tests was lower and detection rates were more variable. Rates were 24.6–46.2% for regression of k-value on abundance, 6.4–32.6% for regression of k-value on logarithmic abundance and 0.2–13.7% for Bulmer's second test (Bulmer 1975). Bulmer's second test is not useful because of low power. For one method, regression of k-value on abundance. density dependence was detected in 19.9% of timeseries generated using a random-walk model. For regression of k-value on logarithmically-transformed abundance the equivalent figure was 18.3% of series. These rates of spurious detection were significantly (P<0.001) greater than the generally accepted 5% level of type 1 errors and so these methods are not suitable for the analysis of time-series data for density dependence. Levels of spurious detection (from random-walk data) were around the 5% level and hence were acceptable for Bulmer's first test, Bulmer's second test, and the tests of Pollard et al. (1987), Reddinguis and den Boer (1989) and Crowley (1992). For all tests, except Bulmer's second test, the rate of detection and the amount of autocorrelation in time-series were negatively correlated. The degree of autocorrelation accounted for as much as 59.5–77.9% of the deviance in logit proportion detection for regression of k-value on abundance, Bulmer's first test, and the tests of Pollard et al. and Reddingius and den Boer. For regression of k-value on abundance this relationship accounted for less of the deviance (29.4%). Independent effects of density dependence were largely absent. It is concluded that these are tests of autocorrelation, not density dependence (or limitation). Autocorrelation was found to become positive (which is similar to values from random-walk data) as the intrinsic growth rate became either small or large. As the strength of density dependence (in the discrete exponential logistic equation) is dependent on the product of the intrinsic growth rate and the density dependent parameter it is unclear whether this is because of variation in the strength of density dependent mortality or reproduction per se. However, small values of the intrinsic grwoth rate cause the amount of variation in the data to become small, which might hinder detection of density dependence, and large values of the intrinsic growth rate are coincident with determinstic chaos which hinders detection. The user of these tests for density dependence should be aware of their potential weakness when variation within time-series is small (which itself is difficult to judge) or if the intrinsic growth rate is large so that chaotic dynamics might result. Power and levels of variability in rates of detection using Reddingius and den Boer's test were intermediate between those of the test of Pollard et al. and Bulmer's first test. This, combined with the strong relationship between rates of detection of limitation and the value of the autocorrelation coefficient, make testing for limitation similar to testing for density dependence. Crowley's test of attraction gave the widest range of mean detection rates from density dependent data of all the tests (20.4–60.6%). The relative rates of detection for the three forms of density dependent data were opposite to those found for Bulmer's first test and the test of Pollard et al. I conclude that testing for attraction is a complementary concept to testing for density dependence. As dynamics represented in time-series generated using a stochastic form of the exponential logistic equation became chaotic, Bulmer's first test, the test of Pollard et al. and regression of k on abundance failed to detect density dependence reliably. Conversely, Crowley's test was capable of detecting attraction with a power between 96 and 100% with time-series containing both stochastically and deterministically chaotic dynamics. This difference from other tests is in agreement with the lower influence of autocorrelation.     

Understanding the population dynamics of a rare, polyvoltine butterfly

Understanding the effects of endogenous and exogenous factors on population dynamics is essential for assessing the viability of populations, setting recovery goals for endangered species, and evaluating management options. Invertebrates are particularly difficult to monitor and few long-term datasets are available for these species. Additionally, limited resources make it necessary to perform monitoring as efficiently as possible. Here, I use the bivoltine Karner blue butterfly Lycaeides melissa samuelis to demonstrate how analyzing the effects of density-dependent factors and weather on separate life stages can be utilized to understand monitoring data, assess populations and identify critical life-history parameters. My first step was to compare the use of peak numbers as an index of population size with estimates obtained from a more data intensive methodology. Peak numbers proved to be an effective index, and so I utilized this index to analyze 10 and 13 years of monitoring data at two Karner blue butterfly sites in New York, USA. I modeled the effects of weather and density dependence on two distinct population growth rates ( λ ) per year. Analysis with Akaike's Information Criteria indicated that both sites were primarily influenced by density dependence during the summer period and by weather conditions during the winter period. Large population declines occurred in the winter period and were a result of the previous year's dry summer and cool spring weather. I conclude that recovery goals for this endangered species should include a second brood-carrying capacity, mean winter growth rate and multiple sites with independent populations. This study represents a rare, long-term study on the population dynamics of a polyvoltine species. Understanding the population dynamics of polyvoltine species, such as the Karner blue butterfly, will assist in the conservation of many invertebrate and small mammal species.     

Density dependence in cereal aphid populations

Long sequences of data on the incidence of cereal aphids from five European countries were analysed for evidence of density dependent processes occurring between years. Using a randomisation test (Pollard, Lakhani & Rotheray, 1987), density dependence was revealed in all (16) population censuses of Metopolo-phium dirhodum , 60% of Rhopalosiphum padi censuses (10 of 17) but only 25% of Sitobion avenae population censuses (4 of 16). Correcting for density independent effects of weather revealed the existence of significant direct density dependence in some populations censuses where it was previously undetected. The implications of density dependence in cereal aphid populations are considered.     

Census error and the detection of density dependence

1. Studies aiming to identify the prevalence and nature of density dependence in ecological populations have often used statistical analysis of ecological time-series of population counts. Such time-series are also being used increasingly to parameterize models that may be used in population management. 2. If time-series contain measurement errors, tests that rely on detecting a negative relationship between log population change and population size are biased and prone to spuriously detecting density dependence (Type I error). This is because the measurement error in density for a given year appears in the corresponding change in population density, with equal magnitude but opposite sign. 3. This effect introduces bias that may invalidate comparisons of ecological data with density-independent time-series. Unless census error can be accounted for, time-series may appear to show strongly density-dependent dynamics, even though the density-dependent signal may in reality be weak or absent. 4. We distinguish two forms of census error, both of which have serious consequences for detecting density dependence. 5. First, estimates of population density are based rarely on exact counts, but on samples. Hence there exists sampling error, with the level of error depending on the method employed and the number of replicates on which the population estimate is based. 6. Secondly, the group of organisms measured is often not a truly self-contained population, but part of a wider ecological population, defined in terms of location or behaviour. Consequently, the subpopulation studied may effectively be a sample of the population and spurious density dependence may be detected in the dynamics of a single subpopulation. In this case, density dependence is detected erroneously, even if numbers within the subpopulation are censused without sampling error. 7. In order to illustrate how process variation and measurement error may be distinguished we review data sets (counts of numbers of birds by single observers) for which both census error and long-term variance in population density can be estimated. 8. Tests for density dependence need to obviate the problem that measured population sizes are typically estimates rather than exact counts. It is possible that in some cases it may be possible to test for density dependence in the presence of unknown levels of census error, for example by uncovering nonlinearities in the density response. However, it seems likely that these may lack power compared with analyses that are able to explicitly include census error and we review some recently developed methods.     

Effects of density-dependence and weather on population changes of English passerines using a non-experimental paradigm

The Common Birds Census documents changes in the populations of the more abundant British land birds. Here we analyse the CBC data for various English passerines to discover the separate effects of weather and of density-dependent feedback on their annual population changes. Density dependence is generally apparent in the data from woodland plots, less so in those from farmland. There are clear effects of weather, particularly in farmland. Prolonged snowfall in winter reduces populations of most species; frost and low temperatures appear much less important. Rainfall in March and April increases numbers censused in the spring but this may be an artefact. We discuss ways in which analyses such as these should be taken forward.
We consider whether this work is respectable science, arguing that monitoring through the CBC goes much further than mere surveillance of numbers, that such monitoring is important in wildlife management, and that density dependence is not a bankrupt paradigm. Long-term data gathering is an essential part of ecological science, even in programmes not designed at the outset to test specific hypotheses.     

Time series analysis of climate‐related factors and their impact on a red‐listed noble crayfish population in northern Sweden

1. Global climate change is predicted to raise water temperatures and alter flow regimes in northern river systems. Climate‐related factors might have profound impacts on survival, reproduction and distribution of freshwater species such as red‐listed noble crayfish (Astacus astacus) in its northern limit of distribution. 2. In this study, noble crayfish capture data over 27 years from the River Ljungan, Sweden, were examined. Time series of catch per unit effort (CPUE) were analysed in relation to the North Atlantic Oscillation (NAO) index, regional weather factors and water flow. CPUE was assumed to reflect differences in population size. Two models were constructed to explore the relative impact of different climate factors and density dependence on variability of catch sizes. 3. The most parsimonious model for CPUE time series, explaining 72% of the variance in CPUE, included density‐dependent population dynamics of the crayfish and climate or weather factors. The specific effect from density dependence in the model was 37%, while climate/weather factors contributed with 35% of the variation. The most important climate/weather factors are variations in NAO index and water flow. Temperature did not improve the model fit to capture data. 4. The best model was evaluated using independent data sets that gave correlations between model predictions and data ranging from 0.44 to 0.53. The density dependence shows a time lag of 1 year, while climate variables show time lags from 2 to 6 years in relation to CPUE, indicating effects on different cohorts of the crayfish population. 5. Both density dependence and climatic factors play a significant role in population fluctuations of noble crayfish. A 6‐year time lag for NAO index is puzzling but indicates that some as yet unidentified factors related to NAO might act on the juvenile stages of the population. Water flow shows a 2‐year lag to the CPUE, and high flow in the river may affect adult survival. The reasons for fluctuation of crayfish catches in response to climate need to be identified, and fishing quotas should consider the different cohort sizes because of variation in environment. Reintroduction programmes for crayfish need to consider effects of climate change when designing management strategies.     

The relative roles of density and climatic variation on population dynamics and fecundity rates in three contrasting ungulate species

The relative influences of density-dependent and -independent processes on vital rates and population dynamics have been debated in ecology for over half a century, yet it is only recently that both processes have been shown to operate within the same population. However, generalizations on the role of each process across species are rare. Using a process-orientated generalized linear modelling approach we show that variations in fecundity rates in populations of three species of ungulates with contrasting life histories are associated with density and winter weather in a remarkably similar manner. However, there are differences and we speculate that they are a result of differences in size between the species. Much previous research exploring the association between vital rates, population dynamics and density-dependent and -independent processes has used pattern-orientated approaches to decompose time-series into contributions from density-dependent and -independent processes. Results from these analyses are sometimes used to infer associations between vital rates, density and climatic variables. We compare results from pattern-orientated analyses of time-series with process-orientated analyses and report that the two approaches give different results. The approach of analysing relationships between vital rates, density and climatic variables may detect important processes influencing population dynamics that time-series methodologies may overlook.     

Phenological responses in a sycamore–aphid–parasitoid system and consequences for aphid population dynamics: A 20 year case study

Species interactions have a spatiotemporal component driven by environmental cues, which if altered by climate change can drive shifts in community dynamics. There is insufficient understanding of the precise time windows during which inter‐annual variation in weather drives phenological shifts and the consequences for mismatches between interacting species and resultant population dynamics—particularly for insects. We use a 20 year study on a tri‐trophic system: sycamore Acer pseudoplatanus, two associated aphid species Drepanosiphum platanoidis and Periphyllus testudinaceus and their hymenopteran parasitoids. Using a sliding window approach, we assess climatic drivers of phenology in all three trophic levels. We quantify the magnitude of resultant trophic mismatches between aphids and their plant hosts and parasitoids, and then model the impacts of these mismatches, direct weather effects and density dependence on local‐scale aphid population dynamics. Warmer temperatures in mid‐March to late‐April were associated with advanced sycamore budburst, parasitoid attack and (marginally) D. platanoidis emergence. The precise time window during which spring weather advances phenology varies considerably across each species. Crucially, warmer temperatures in late winter delayed the emergence of both aphid species. Seasonal variation in warming rates thus generates marked shifts in the relative timing of spring events across trophic levels and mismatches in the phenology of interacting species. Despite this, we found no evidence that aphid population growth rates were adversely impacted by the magnitude of mismatch with their host plants or parasitoids, or direct impacts of temperature and precipitation. Strong density dependence effects occurred in both aphid species and probably buffered populations, through density‐dependent compensation, from adverse impacts of the marked inter‐annual climatic variation that occurred during the study period. These findings explain the resilience of aphid populations to climate change and uncover a key mechanism, warmer winter temperatures delaying insect phenology, by which climate change drives asynchronous shifts between interacting species.     

Breeding season weather determines long‐tailed tit reproductive success through impacts on recruitment

Productivity is a key demographic trait that can be influenced by climate change, but there are substantial gaps in our understanding of the impact of weather on productivity and recruitment in birds. Weather is known to influence reproductive success in numerous species, although such effects have not been reported in all studies, perhaps because they are masked by high nest predation rates or buffered by density dependence. Here, we use a 19‐yr study of a population of individually marked long‐tailed tits Aegithalos caudatus to quantify the impacts of weather on productivity in the nest (from eggs to fledging) and subsequent recruitment, while taking nest predation rates and density dependence into account. We find that weather has negligible effects on clutch size, hatching success, brood size, probability of fledging and number of fledglings. Annual variation in nest predation rates is a strong predictor of productivity, but we find no evidence that the magnitude of nest predation is determined by weather. Recruitment was strongly associated with breeding season weather, even when taking density dependence effects into account. This contrasts with the conventional view that first year survival of temperate passerines is primarily determined by winter weather. Recruitment was reduced when March temperatures were high, perhaps caused by earlier peaks in caterpillar abundance and thus reduced food availability at the time of fledging. Recruitment increased following high May temperatures, perhaps due to an improved thermo‐regulatory environment for young fledglings. These opposing effects of warm March and May temperatures highlight the importance of considering asymmetrical rates of warming in different months when predicting climate change impacts.     

Avoiding erroneously high levels of detection in combinations of semi-independent tests

A randomization procedure is proposed which allows statistical tests to be combined into a single test to maintain specified and acceptable levels of false detection. This method was applied to the problem of detecting density dependence in 135 unpublished time-series (of 10 generations) from insect populations, and to simulated density-dependent and density-independent data, so that the correctness of observed levels of detection from the published data could be verified. To allow the application of the randomization procedure to Bulmer's (1975) tests and Varley and Gradwell's (1960) test, these were recast as randomization tests. The randomization procedure was tested with 39 combinations of tests for density dependence (and limitation/attraction); it generally producedcombined tests with levels of detection that were intermediate between detection levels of the constituent tests (and hence was limite by these). The specified rate of false detection (5%) was never exceeded (by more than 1%) when combined tests were applied to time-series from a random-walk model. Two different combinations of tests produced levels of detection from the published time-series which were slightly greater than their constituent tests when they were combined into single tests. These were the randomized form of Bulmer's (1975) first test with the tests of Pollard et al. (1987) and Reddingius and den Boer (1989) with the randomized form of Bulmer's second test. The combination of Bulmer's first and Pollard et al.'s test produced a greater level of detection (21.5%) than any other single test or combination of tests. These results were confirmed by the analysis of modelled density dependent data. Although the increase in power of combinations of tests over single tests is small with the data we used, the combined tests (listed above) had rates of detection that were less influenced by the form of data (of two forms of density-dependent data) than were their constituent tests. Hence, it appears that the combined tests are of greater generality than single test statistics. The method presented here for combining several statistical tests into a single randomization test is applicable in many other areas of ecology where we wish to apply several tests and take the most probable result of these; and if the tests being conducted are, or can be expressed as, randomization tests.     

Main determinants of rodent population fluctuations in managed Central European temperate lowland forests

Whilst studies have shown that climatic (North Atlantic Oscillation (NAO)) and biotic (acorn production) factors influence rodent populations, mechanisms driving temporal and spatial fluctuation of rodent populations are understudied. This study evaluates relationships between the influence of environmental factors (biotic and abiotic) and phenotypic characteristics across two rodent feeding guilds (granivorous and non-granivorous species) represented by four species of rodents in Central Europe. We hypothesise that the relationship between acorn density and population growth rate are indirectly affected by climatic factors (winter NAO) and that these effects differ amongst herbivorous and granivorous species. In addition, we also tested whether effects of weather and competition on individual phenotype characteristic vary amongst mast and non-mast years. Rodent populations were estimated by catching individuals in snap traps during the growing season (from March to November) over a period of 9 years at three sites. The results of the generalised linear model provide evidence that acorn production best explained the population fluctuations. We therefore conclude that the between-year population fluctuations in rodent abundance were governed by density dependence and initiated primarily by acorn mast years. Auto-regressive models also revealed direct density dependence in combination with the direct effects of mast years. Therefore, strong intraspecific competition for food is likely in years following mast years. Our results also showed that abundance of non-granivorous species is mainly influenced by local weather conditions which could regulate food quality and abundance. On the other hand, population dynamics of granivorous species are caused directly by acorn density and indirectly by climatic condition influencing acorn production.     

The use of fuzzy plant species density to indicate the effects of land-cover changes on biodiversity

The assessment of the effects of land-cover changes on biodiversity requires expensive and time-consuming surveys. It is often the case that few data are readily available and time constraints and limited resources do not allow further data collection. In such cases, cost-effective assessment methods are needed. This study puts forward an assessment method which is based on approximate reasoning and is supported by Similarity theory. The suggested method uses Geographic Information Systems and free or low-cost floristic databases and time-series land-cover data. A main assumption is made, that species density, i.e. the number of plant species per unit area, could be used as a good indicator to estimate spatial biodiversity changes due to land-cover changes. The method produces, for a given area, “plausible maps of plant species density” considering land-cover maps. The method, based on approximate reasoning, overcomes the uncertainties inherent to floristic databases, allowing environmental conservation efforts to benefit from these information sources. An example application of the suggested assessment method is presented for the area of Butser Hill in the United Kingdom. The performance of the suggested approach and its limitations are then discussed.     

Detection of density dependence from annual censuses of bracken-feeding insects

Summary A variety of techniques were used to test for density dependence in 32 time series from bracken-feeding insects. Seventeen taxa (primarily species, but including some pooled data from two or more closely related species whose larvae could not be distinguished in frond surveys) occurred on an open site; a woodland site held 15 taxa. For series of 12 years, collected on the open habitat, direct density dependence was detected by one or more of the techniques in 10 (58.8%) of 17 taxa, compared to only 5 (33.3%) of 15 taxa with time series of 8 years in length from the woodland habitat. Delayed density dependence was detected in 6 cases for the open site and in no cases at the woodland site. Either direct or delayed density dependence was found in 13 (76.5%) of 17 taxa for the open site and 13 (86.7%) of the 15 taxa which occurred on both sites. Although these results suggest a high frequency of density dependence in the species making up the bracken insect community, results from individual tests were extremely variable. Density dependence was detected least often by Vickery and Nudds' (1984) test, and most frequently by Varley and Gradwell's (1960) test, although the latter is prone to high rates of detecting spurious density dependence. Direct density dependence was detected most frequently in taxa that were univoltine and did not have delayed diapause, i.e. in those taxa whose life-histories conform most closely to the assumptions of the models underlying the analyses. Delayed density dependence occurred more frequently in species with more complex life-histories at the open site (taxa that were either bivoltine or multivoltine, or had delayed diapause). The results are consistent with the view that that the bracken herbivore assemblage consists of populations which are independently regulated by density dependent processes, although the present analyses suggest that we cannot rely on these tests to firmly show whether density dependence is present or not in an individual time series of the lengths considered here.     

Accounting for interspecific competition and age structure in demographic analyses of density dependence improves predictions of fluctuations in population size

Understanding species coexistence has long been a major goal of ecology. Coexistence theory for two competing species posits that intraspecific density dependence should be stronger than interspecific density dependence. Great tits and blue tits are two bird species that compete for food resources and nesting cavities. On the basis of long‐term monitoring of these two competing species at sites across Europe, combining observational and manipulative approaches, we show that the strength of density regulation is similar for both species, and that individuals have contrasting abilities to compete depending on their age. For great tits, density regulation is driven mainly by intraspecific competition. In contrast, for blue tits, interspecific competition contributes as much as intraspecific competition, consistent with asymmetric competition between the two species. In addition, including age‐specific effects of intra‐ and interspecific competition in density‐dependence models improves predictions of fluctuations in population size by up to three times.     

Estimating density dependence in time-series of age-structured populations

For a life history with age at maturity alpha, and stochasticity and density dependence in adult recruitment and mortality, we derive a linearized autoregressive equation with time-lags of from 1 to alpha years. Contrary to current interpretations, the coefficients for different time-lags in the autoregressive dynamics do not simply measure delayed density dependence, but also depend on life-history parameters. We define a new measure of total density dependence in a life history, D, as the negative elasticity of population growth rate per generation with respect to change in population size, D = - partial differential lnlambda(T)/partial differential lnN, where lambda is the asymptotic multiplicative growth rate per year, T is the generation time and N is adult population size. We show that D can be estimated from the sum of the autoregression coefficients. We estimated D in populations of six avian species for which life-history data and unusually long time-series of complete population censuses were available. Estimates of D were in the order of 1 or higher, indicating strong, statistically significant density dependence in four of the six species.     

Testing for predator dependence in predator-prey dynamics: a non-parametric approach

The functional response is a key element in all predator-prey interactions. Although functional responses are traditionally modelled as being a function of prey density only, evidence is accumulating that predator density also has an important effect. However, much of the evidence comes from artificial experimental arenas under conditions not necessarily representative of the natural system, and neglecting the temporal dynamics of the organism (in particular the effects of prey depletion on the estimated functional response). Here we present a method that removes these limitations by reconstructing the functional response non-parametrically from predator-prey time-series data. This method is applied to data on a protozoan predator-prey interaction, and we obtain significant evidence of predator dependence in the functional response. A crucial element in this analysis is to include time-lags in the prey and predator reproduction rates, and we show that these delays improve the fit of the model significantly. Finally, we compare the non-parametrically reconstructed functional response to parametric forms, and suggest that a modified version of the Hassell-Varley predator interference model provides a simple and flexible function for theoretical investigation and applied modelling.     

Modeling density dependence and climatic disturbances in caribou: a case study from the Bathurst Island complex, Canadian High Arctic

Peary caribou Rangifer tarandus pearyi is the northernmost subspecies of Rangifer in North America and endemic to the Canadian High Arctic. Because of severe population declines following years of unfavorable winter weather with ice coating on the ground or thicker snow cover, it is believed that density-independent disturbance events are the primary driver for Peary caribou population dynamics. However, it is unclear to what extent density dependence may affect population dynamics of this species. Here, we test for different levels of density dependence in a stochastic, single-stage population model, based on available empirical information for the Bathurst Island complex (BIC) population in the Canadian High Arctic. We compare predicted densities with observed densities during 1961–2001 under various assumptions of the strength of density dependence. On the basis of our model, we found that scenarios with no or very low density dependence led to population densities far above observed densities. For average observed disturbance regimes, a carrying capacity of 0.1 caribou km⁻² generated an average caribou density similar to that estimated for the BIC population over the past four decades. With our model we also tested the potential effects of climate change-related increases in the probability and severity of disturbance years, that is unusually poor winter conditions. On the basis of our simulation results, we found that, in particular, potential increases in disturbance severity (as opposed to disturbance frequency) may pose a considerable threat to the persistence of this species.     

Weather-induced changes in moth activity bias measurement of long-term population dynamics from light trap samples

Interpretation of light trap catches of moths is complicated by daily variation in weather that alters flight activity and numbers caught. Light trap efficiency is also modified by wind and fog, and daily weather may effect absolute abundance (numbers actually present). However, actograph experiments and other sampling methods suggest that changes in daily activity are large by comparison to changes in absolute abundance. Daily variation in weather (other than wind and fog) is therefore a form of sampling error in absolute abundance estimates. We investigated the extent of this sampling bias in 26 years of population dynamics from 133 moth species. In a subset of 20 noctuid and geometrid species, daily numbers caught were positively correlated with temperature in 14 species, and negatively correlated with rainfall in 11 species. The strength of correlations varied between species, making it difficult to standardize catches to constant conditions. We overcame this by establishing how weather variation changed with time and duration of the flight period. Species flying later in the summer and for shorter periods experienced more variable temperatures, making sampling error greater for these species. Of the 133 moth species, those with shorter flight periods had greater population variability and more showed significant temporal density dependence. However, these effects were weak, which is encouraging because it suggests that population analyses of light trap data largely reflect factors other than sampling error.