Aggregation and a strongAllee effect in a cooperative outbreak insect

Abstract

Most species that are negatively impacted when their densities are low aggregate to minimize this effect. Aggregation has the potential to change how Allee effects are expressed at the population level. We studied the interplay between aggregation and Allee effects in the mountain pine beetle (Dendroctonus ponderosae Hopkins), an irruptive bark beetle that aggregates to overcome tree defenses. By cooperating to surpass a critical number of attacks per tree, the mountain pine beetle is able to breach host defenses, oviposit, and reproduce. Mountain pine beetles and Hymenopteran parasitoids share some biological features, the most notable of which is obligatory host death as a consequence of parasitoid attack and development. We developed spatiotemporal models of mountain pine beetle dynamics that were based on the Nicholson-Bailey framework but which featured beetle aggregation and a tree-level attack threshold. By fitting our models to data from a local mountain pine beetle outbreak, we demonstrate that due to aggregation, attack thresholds at the tree level can be overcome by a surprisingly low ratio of beetles per susceptible tree at the stand level. This results confirms the importance of considering aggregation in models of organisms that are subject to strong Allee effects.

Morphological variation associated with dispersal capacity in a tree‐killing bark beetleDendroctonus ponderosaeHopkins

“…Body weight and wing size contribute to dispersal capacity in mountain pine beetle, and such data can potentially be useful in improving dispersal model quality (Goodsman et al ., ). Although many other factors must also be considered in the parameterization of dispersal models, we have shown that large female beetles with large wings generally have an increased dispersal capacity, flying further than smaller beetles with smaller wings; autonomous populations in dispersal models can be assigned flight capacities based on morphology observed in situ through use of samples already being collected for monitoring purposes.…”

“…In such scenarios, equipment and manpower must be allocated in accessible areas that will maximize the impact on MPB spread at the same time as minimizing cost. Predictive modelling of MPB spread is one means of risk detection, and such models need to be parameterized with variables such as regional climate and dispersal capacity (Atkins, ; Robertson et al ., ; Aukema et al ., ; Goodsman et al ., ). Climatic data are often available, although the ability to identify MPB dispersal phenotypes is lacking.…”

“…Many models predict MPB dispersal at both landscape and stand levels based on spatio‐temporal patterns (Robertson et al ., ; Aukema et al ., ; Chen & Walton, ), climate (Aukema et al ., ), host vigor (Lewis et al ., ) and other environmental conditions (Safranyik et al ., ; Robertson et al ., ; Ainslie & Jackson, ). Inclusion of flight mill data can improve model accuracy by providing detailed data on flight behaviour and patterns such as stopping frequency and length of uninterrupted flight times (Goodsman et al ., ). Finding the relationships of MPB wing and body morphology to measured flight capacity should improve our ability to predict bark beetle dispersal in nature.…”

Incorporating variability in simulations of seasonally forced phenology using integral projection models

“…Beetles are not able to fly indefinitely. To account for settling in beetles that expend their energy reserves for flight, we added an exponential settling function based on laboratory measurements of beetle flight propensity (Evenden, Whitehouse, & Sykes, ) and a simple model of mountain pine beetle dispersal (Goodsman et al., ).…”

“…We do not provide a separate script containing this code as it is essentially the same as the stage-and age-structured integral projection model of mountain pine beetle demography and phenology already in the Supporting Information (Data S3). (Evenden, Whitehouse, & Sykes, 2014) and a simple model of mountain pine beetle dispersal (Goodsman et al, 2016).…”