Demography and perturbation analyses of the coffee berry borer Hypothenemus hampei (Coleoptera: Curculionidae): Implications for management

The coffee berry borer (CBB) Hypothenemus hampei Ferrari is the most serious pest of coffee worldwide. Management of the CBB is extremely difficult because its entire life cycle occurs inside the fruit, where it is well protected. Knowing which life stages contribute most to population growth, would shed light on the population dynamics of this pest and help to improve CBB management programs. Two staged-classified matrices were constructed for CBB populations reared in the lab on artificial diets and CBB populations from artificial infestations in the field. Matrices were used to determine demographic parameters, to conduct elasticity analyses, and to perform prospective perturbation analysis. Higher values of the intrinsic rate of natural increase (rm) and population growth rate (λ): were observed for CBB populations growing in the lab than in the field (rm: 0.058, λ: 1.74 lab; rm: 0.053, λ: 1.32 field). Sensitivity values for both CBB populations were highest for the transitions from larva to pupa (G2: 0.316 lab, 0.352 field), transition from pupa to juvenile (G3: 0.345 lab, 0.515 field) and survival of adult females (P5: 0.324 lab, 0.389 field); these three vital rates can be important targets for CBB management. Prospective perturbation analyses indicated that an effective management for the CBB should consider multiple developmental stages; perturbations of >90% for each transition are necessary to reduce λ to <1. However, when the three vital rates with highest sensitivity are impacted at the same time, the percentage of perturbation is reduced to 25% for each transition; with these reductions in survival of larvae, pupae and adult females the value of λ was reduced from 1.32 to 0.96. Management programs for CBB should be focused on the use of biological and cultural measures that are known to affect these three important targets.

Life history adaptations to fluctuating environments: Combined effects of demographic buffering and lability of vital rates

Demographic buffering and lability have both been identified as important adaptive strategies to optimise long-term fitness in variable environments. These strategies are not mutually exclusive, however we lack efficient methods to measure their relative importance. Here, we define a new index to measure the total lability for a given life history, and use stochastic simulations to disentangle relative fitness effects of buffering and lability. The simulations use 81 animal matrix population models, and different scenarios to explore how the strategies vary across life histories. The highest potential for adaptive demographic lability was found for short- to intermediately long-lived species, while demographic buffering was the main response in slow-living species. This study suggests that faster-living species are more responsive to environmental variability, both for positive or negative effects. Our methods and results provide a more comprehensive view of adaptations to variability, of high relevance to predict species responses to climate change.

Identifying mechanisms of population change in two threatened grizzly bear populations

<p>Identifying the mechanisms causing population change is essential for conserving small and declining populations. Substantial range contraction of many carnivore species has resulted in fragmented global populations with numerous small isolates in need of conservation. Here I investigate the rate and possible agents of change in two threatened grizzly bear (Ursus arctos) populations in southwestern British Columbia, Canada. I use a combination of population vital rates estimates, population trends, habitat quality analyses, and comparisons to what has been described in the literature, to carefully compare among possible mechanisms of change. First, I estimate population density, realized growth rates (λ), and the demographic components of population change for each population using DNA based capture-recapture data in both spatially explicit capture-recapture (SECR) and non-spatial Pradel robust design frameworks. The larger population had 21.5 bears/1000km2 and between 2006 and 2016 was growing (λPradel = 1.02 ± 0.02 SE, λsecr = 1.01 ± 4.6 x10-5 SE) following the cessation of hunting. The adjacent but smaller population had 6.3 bears/1000km2 and between 2005 and 2017 was likely declining (λPradel = 0.95 ± 0.03 SE, λsecr = 0.98 ± 0.02 SE). Estimates of apparent survival and recruitment indicated that lower recruitment was the dominant factor limiting population growth in the smaller population. Then I use data from GPS-collared bears to estimate reproduction, survival and projected population change (λ) in both populations. Adult female survival was 0.96 (95% CI: 0.80-0.99) in the larger population (McGillvary Mountains or MM) and 0.87 (95% CI: 0.69-0.95) in the small, isolated population (North Stein-Nahatlatch or NSN). Cub survival was also higher in the MM (0.85, 95%CI: 0.62-0.95) than the NSN population (0.33, 95%CI: 0.11-0.67). This analysis identifies both low adult female survival and low cub survival as the demographic factors associated with population decline in the smaller population. By comparing the vital rates from these two populations with other small populations, I suggest that when grizzly bear populations are isolated, there appears to be a tipping point (de Silva and Leimgruber 2019) around 50 individuals, below which adult female mortality, even with intensive management, becomes prohibitive for population recovery. This analysis provides the first detailed estimates of population vital rates for a grizzly bear population of this size, and this information has been important for subsequent management action. To determine whether bottom-up factors (i.e. food) are limiting population growth and recovery in the small isolated population I use resource selection analysis from GPS collar data. I develop resource selection functions (RSF) for four dominant foraging seasons: the spring-early summer season when bears feed predominantly on herbaceous plants and dig for bulbs, the early fruit season where they feed on low elevation berries and cherries, the huckleberry season and the post berry season when foraging behaviours are most diverse but whitebark pine nuts are a relatively common food source. The differences in overall availability of high-quality habitats for different food types, especially huckleberries, between populations suggests that season specific bottom-up effects may account for some differences in population densities. Resource selections are a very common tool used for estimating resource distribution and availability, however, their ability to estimate food abundance on the ground are usually not tested. I assessed the accuracy of the resulting RSF models for predicting huckleberry presence and abundance measured in field plots. My results show that berry specific models did predict berry abundance in previously disturbed sites though varied in accuracy depending on how the models were categorized and projected across the landscape. Finally, I combine spatially explicit capture-recapture methods and models developed from resource selection modelling to estimate the effect of seasonal habitat availability and open road density, as a surrogate for top-down effects, on the bear density in the two populations. I found that population density is most strongly connected to habitats selected during a season when bears fed on huckleberries, the major high-energy food bears eat during hyperphagia in this area, as well as a large baseline difference between populations. The abundance of high-quality huckleberry habitat appears to be an important factor enabling the recovery of the larger population that is also genetically connected to other bears. The adjacent, smaller and genetically isolated population is not growing. The relatively low abundance of high-quality berry habitat in this population may be contributing to the lack of growth of the population. However, it is likely that the legacy of historic mortality and current stochastic effects, inbreeding effects, or other Allee effects, are also contributing to the continued low density observed. While these small population effects may be more challenging to overcome, this analysis suggests that the landscape can accommodate a higher population density than that currently observed.</p>

Identifying mechanisms of population change in two threatened grizzly bear populations

<p>Identifying the mechanisms causing population change is essential for conserving small and declining populations. Substantial range contraction of many carnivore species has resulted in fragmented global populations with numerous small isolates in need of conservation. Here I investigate the rate and possible agents of change in two threatened grizzly bear (Ursus arctos) populations in southwestern British Columbia, Canada. I use a combination of population vital rates estimates, population trends, habitat quality analyses, and comparisons to what has been described in the literature, to carefully compare among possible mechanisms of change. First, I estimate population density, realized growth rates (λ), and the demographic components of population change for each population using DNA based capture-recapture data in both spatially explicit capture-recapture (SECR) and non-spatial Pradel robust design frameworks. The larger population had 21.5 bears/1000km2 and between 2006 and 2016 was growing (λPradel = 1.02 ± 0.02 SE, λsecr = 1.01 ± 4.6 x10-5 SE) following the cessation of hunting. The adjacent but smaller population had 6.3 bears/1000km2 and between 2005 and 2017 was likely declining (λPradel = 0.95 ± 0.03 SE, λsecr = 0.98 ± 0.02 SE). Estimates of apparent survival and recruitment indicated that lower recruitment was the dominant factor limiting population growth in the smaller population. Then I use data from GPS-collared bears to estimate reproduction, survival and projected population change (λ) in both populations. Adult female survival was 0.96 (95% CI: 0.80-0.99) in the larger population (McGillvary Mountains or MM) and 0.87 (95% CI: 0.69-0.95) in the small, isolated population (North Stein-Nahatlatch or NSN). Cub survival was also higher in the MM (0.85, 95%CI: 0.62-0.95) than the NSN population (0.33, 95%CI: 0.11-0.67). This analysis identifies both low adult female survival and low cub survival as the demographic factors associated with population decline in the smaller population. By comparing the vital rates from these two populations with other small populations, I suggest that when grizzly bear populations are isolated, there appears to be a tipping point (de Silva and Leimgruber 2019) around 50 individuals, below which adult female mortality, even with intensive management, becomes prohibitive for population recovery. This analysis provides the first detailed estimates of population vital rates for a grizzly bear population of this size, and this information has been important for subsequent management action. To determine whether bottom-up factors (i.e. food) are limiting population growth and recovery in the small isolated population I use resource selection analysis from GPS collar data. I develop resource selection functions (RSF) for four dominant foraging seasons: the spring-early summer season when bears feed predominantly on herbaceous plants and dig for bulbs, the early fruit season where they feed on low elevation berries and cherries, the huckleberry season and the post berry season when foraging behaviours are most diverse but whitebark pine nuts are a relatively common food source. The differences in overall availability of high-quality habitats for different food types, especially huckleberries, between populations suggests that season specific bottom-up effects may account for some differences in population densities. Resource selections are a very common tool used for estimating resource distribution and availability, however, their ability to estimate food abundance on the ground are usually not tested. I assessed the accuracy of the resulting RSF models for predicting huckleberry presence and abundance measured in field plots. My results show that berry specific models did predict berry abundance in previously disturbed sites though varied in accuracy depending on how the models were categorized and projected across the landscape. Finally, I combine spatially explicit capture-recapture methods and models developed from resource selection modelling to estimate the effect of seasonal habitat availability and open road density, as a surrogate for top-down effects, on the bear density in the two populations. I found that population density is most strongly connected to habitats selected during a season when bears fed on huckleberries, the major high-energy food bears eat during hyperphagia in this area, as well as a large baseline difference between populations. The abundance of high-quality huckleberry habitat appears to be an important factor enabling the recovery of the larger population that is also genetically connected to other bears. The adjacent, smaller and genetically isolated population is not growing. The relatively low abundance of high-quality berry habitat in this population may be contributing to the lack of growth of the population. However, it is likely that the legacy of historic mortality and current stochastic effects, inbreeding effects, or other Allee effects, are also contributing to the continued low density observed. While these small population effects may be more challenging to overcome, this analysis suggests that the landscape can accommodate a higher population density than that currently observed.</p>

Thermal asymmetries influence effects of warming on stage and size-dependent predator-prey interactions

Climate warming directly influences the developmental and feeding rates of organisms. Changes in these rates are likely to have consequences for species interactions, particularly for organisms affected by stage- or size-dependent predation. However, because of differences in species-specific responses to warming, predicting the impact of warming on predator and prey densities can be difficult. We present a general model of stage-dependent predation with temperature-dependent vital rates to explore the effects of warming when predator and prey have different thermal optima. We found that warming generally favored the interactor with the higher thermal optimum. Part of this effect occurred due to the stage-dependent nature of the interaction, and part due to thermal asymmetries. Furthermore, large differences in thermal optima between predators and prey (i.e., a high degree of asymmetry) led to a weaker interaction. Interestingly, below the predator and prey thermal optima, warming caused prey densities to decline, even as increasing temperature improved prey performance. We also parameterize our model using values from a well-studied system, Arctia virginalis and Formica lasioides, in which the predator has a warmer optimum. Overall, our results provide a general framework for understanding stage- and temperature-dependent predator-prey interactions, and illustrate that the thermal niche of both predator and prey are important to consider when predicting the effects of climate warming.

Initial dispersal behavior and survival of non-native juvenile Burmese pythons (Python bivittatus) in South Florida

Background Dispersal behavior is a critical component of invasive species dynamics, impacting both spatial spread and population density. In South Florida, Burmese pythons (Python bivittatus) are an invasive species that disrupt ecosystems and have the potential to expand their range northward. Control of python populations is limited by a lack of information on movement behavior and vital rates, especially within the younger age classes. We radio-tracked 28 Burmese pythons from hatching until natural mortality for approximately 3 years. Pythons were chosen from 4 clutches deposited by adult females in 4 different habitats: forested wetland, urban interface, upland pine, and agricultural interface. Results Known-fate survival estimate was 35.7% (95% CI = 18% - 53%) in the first 6 months, and only 2 snakes survived 3 years post hatching. Snakes moving through ‘natural’ habitats had higher survival than snakes dispersing through ‘modified’ habitats in the first 6- months post-hatching. Predation was the most common source of mortality. Snakes from the agricultural interface utilized canals and displayed the largest net movements. Conclusions Our results suggest that pythons may have lower survival if clutches are deposited in or near urbanized areas. Alternatively, juvenile pythons could quickly disperse to new locations by utilizing canals that facilitate linear movement. This study provides critical information about behavioral and life history characteristics of juvenile Burmese pythons that will inform management practices.

Building integral projection models with non-independent vital rates

Population dynamics are functions of several demographic processes including survival, reproduction, somatic growth, and maturation. The rates or probabilities for these processes can vary by time, by location, and by individual. These processes can co-vary and interact to varying degrees, e.g., an animal can only reproduce when it is in a particular maturation state. Population dynamics models that treat the processes as independent may yield somewhat biased or imprecise parameter estimates, as well as predictions of population abundances or densities. However, commonly used integral projection models (IPMs) typically assume independence across these demographic processes. We examine several approaches for modelling between process dependence in IPMs, and include cases where the processes co-vary as a function of time (temporal variation), co-vary within each individual (individual heterogeneity), and combinations of these (temporal variation and individual heterogeneity). We compare our methods to conventional IPMs, which treat vital rates independent, using simulations and a case study of Soay sheep (Ovis aries). In particular, our results indicate that correlation between vital rates can moderately affect variability of some population-level statistics. Therefore, including such dependent structures is generally advisable when fitting IPMs to ascertain whether or not such between vital rate dependencies exist, which in turn can have subsequent impact on population management or life-history evolution.

Past insecticide exposure reduces bee reproduction and population growth rate

Pesticides are linked to global insect declines, with impacts on biodiversity and essential ecosystem services. In addition to well-documented direct impacts of pesticides at the current stage or time, potential delayed “carryover” effects from past exposure at a different life stage may augment impacts on individuals and populations. We investigated the effects of current exposure and the carryover effects of past insecticide exposure on the individual vital rates and population growth of the solitary bee, Osmia lignaria. Bees in flight cages freely foraged on wildflowers, some treated with the common insecticide, imidacloprid, in a fully crossed design over 2 y, with insecticide exposure or no exposure in each year. Insecticide exposure directly to foraging adults and via carryover effects from past exposure reduced reproduction. Repeated exposure across 2 y additively impaired individual performance, leading to a nearly fourfold reduction in bee population growth. Exposure to even a single insecticide application can have persistent effects on vital rates and can reduce population growth for multiple generations. Carryover effects had profound implications for population persistence and must be considered in risk assessment, conservation, and management decisions for pollinators to mitigate the effects of insecticide exposure.

Resource limitation determines realized thermal performance and the potential for metabolic meltdown.

1) As temperatures rise across the globe, many species may approach or even surpass their physiological tolerance to withstand high temperatures. Thermal performance curves, which depict how vital rates vary with temperature, are often measured under ideal laboratory conditions and then used to determine the physiological or demographic limits of persistence. However, this approach fails to consider how interactions with other factors (e.g. resources, water availability) may buffer or magnify the effect of temperature change. Recent work has demonstrated that the breadth and shape of a consumer’s thermal performance curve change with resource densities, highlighting the potential for temperature interactions and leading to a potential ‘metabolic meltdown’ when resources decline during warming (Huey and Kingsolver 2019). 2) Here, we further develop the basis for the interaction between temperature and resource density on thermal performance, persistence, and population dynamics by analyzing consumer-resource dynamic models. We find that the coupling of consumer and resource dynamics relaxes the potential for metabolic meltdown because a reduction in top-down control of resources occurs as consumers approach the limits of their thermal niche. However, when both consumers and resources have vital rates that depend on temperature, asymmetry between their responses can generate the necessary conditions for metabolic meltdown. 3) Moreover, we define the concept of a ‘realized’ thermal performance curve that takes into account the dynamic interaction between consumers, resources and temperature, and we describe an important role for this concept moving forward. 4) Synthesis. A better understanding of the link between temperature change, species interactions, and persistence allows us to improve forecasts of community response to climate change. Our work elucidates the importance of thermal asymmetries between interacting species, and resource limitation as a key ingredient underlying realized thermal niches.