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To identify general patterns in the effects of climate change on the outbreak dynamics of forest-defoliating insect species, we examined a 212-year record (1800-2011) of outbreaks of five pine-defoliating species (Bupalus piniarius, Panolis flammea, Lymantria monacha, Dendrolimus pini, and Diprion pini) in Bavaria, Germany for the evidence of climate-driven changes in the severity, cyclicity, and frequency of outbreaks. We also accounted for historical changes in forestry practices and examined effects of past insecticide use to suppress outbreaks. Analysis of relationships between severity or occurrence of outbreaks and detrended measures of temperature and precipitation revealed a mixture of positive and negative relationships between temperature and outbreak activity. Two moth species (P. flammea and Dendrolimus pini) exhibited lower outbreak activity following years or decades of unusually warm temperatures, whereas a sawfly (Diprion pini), for which voltinism is influenced by temperature, displayed increased outbreak occurrence in years of high summer temperatures. We detected only one apparent effect of precipitation, which showed Dendrolimus pini outbreaks tending to follow drought. Wavelet analysis of outbreak time series suggested climate change may be associated with collapse of L. monacha and Dendrolimus pini outbreak cycles (loss of cyclicity and discontinuation of outbreaks, respectively), but high-frequency cycles for B. piniarius and P. flammea in the late 1900s. Regional outbreak severity was generally not related to past suppression efforts (area treated with insecticides). Recent shifts in forestry practices affecting tree species composition roughly coincided with high-frequency outbreak cycles in B. piniarius and P. flammea but are unlikely to explain the detected relationships between climate and outbreak severity or collapses of outbreak cycles. Our results highlight both individualistic responses of different pine-defoliating species to climate changes and some patterns that are consistent across defoliator species in this and other forest systems, including collapsing of population cycles.

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Abstract. Estimation of population sizes and species ranges is central to population and conservation biology. It is widely appreciated that imperfect detection of mobile animals must be accounted for when estimating population size from presence-absence data. Sessile organisms also are imperfectly detected, but correction for detection probability in estimating their population sizes is rare. We illustrate challenges of detection probability and population estimation of sessile organisms using censuses of red wood ant (Formica rufa-group) nests as a case study. These ants, widespread in the northern hemisphere, can make large (up to 2 m tall), highly visible nests. Using data from a mapping campaign by eight observers with varying experience of sixteen 3600-m 2 plots in the Black Forest region of southwest Germany, we compared three different statistical approaches (a nest-level data-augmentation patch-occupancy model with event-specific covariates; a plot-level Bayesian and maximum-likelihood model; nonparametric Chao-type estimators) for quantifying detection probability of sessile organisms. Detection probabilities by individual observers of red wood ant nests ranged from 0.31 to 0.64 for small nests, depending on observer experience and nest size (detection rates were approximately 0.17 higher for large nests), but not on habitat characteristics (forest type, local vegetation). Robust estimation of population density of sessile organisms-even highly apparent ones such as red wood ant nests-thus requires estimation of detection probability, just as it does when estimating population density of rare or cryptic species. Our models additionally provide approaches to calculate the number of observers needed for a required level of accuracy. Estimating detection probability is vital not only when censuses are conducted by experts, but also when citizen-scientists are engaged in mapping and monitoring of both common and rare species.

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