Jonathan M. Yearsley

Charring temperatures are driven by the fuel types burned in a peatland wildfire

Peatlands represent a globally important carbon store; however, the human exploitation of this ecosystem is increasing both the frequency and severity of fires on drained peatlands. Yet, the interactions between the hydrological conditions (ecotopes), the fuel types being burned, the burn severity, and the charring temperatures (pyrolysis intensity) remain poorly understood. Here we present a post-burn assessment of a fire on a lowland raised bog in Co. Offaly, Ireland (All Saints Bog). Three burn severities were identified in the field (light, moderate, and deeply burned), and surface charcoals were taken from 17 sites across all burn severities. Charcoals were classified into two fuel type categories (either ground or aboveground fuel) and the reflectance of each charcoal particle was measured under oil using reflectance microscopy. Charcoal reflectance shows a positive relationship with charring temperature and as such can be used as a temperature proxy to reconstruct minimum charring temperatures after a fire event. Resulting median reflectance values for ground fuels are 1.09 ± 0.32%Romedian, corresponding to estimated minimum charring temperatures of 447°C ± 49°C. In contrast, the median charring temperatures of aboveground fuels were found to be considerably higher, 646°C ± 73°C (3.58 ± 0.77%Romedian). A mixed-effects modeling approach was used to demonstrate that the interaction effects of burn severity, as well as ecotope classes, on the charcoal reflectance is small compared to the main effect of fuel type. Our findings reveal that the different fuel types on raised bogs are capable of charring at different temperatures within the same fire, and that the pyrolysis intensity of the fire on All Saints Bog was primarily driven by the fuel types burning, with only a weak association to the burn severity or ecotope classes.

Inferring landscape effects on dispersal from genetic distances: how far can we go?

Functional connectivity affects demography and gene dynamics in fragmented populations. Besides species‐specific dispersal ability, the connectivity between local populations is affected by the landscape elements encountered during dispersal. Documenting these effects is thus a central issue for the conservation and management of fragmented populations. In this study, we compare the power and accuracy of three methods (partial correlations, regressions and Approximate Bayesian Computations) that use genetic distances to infer the effect of landscape upon dispersal. We use stochastic individual‐based simulations of fragmented populations surrounded by landscape elements that differ in their permeability to dispersal. The power and accuracy of all three methods are good when there is a strong contrast between the permeability of different landscape elements. The power and accuracy can be further improved by restricting analyses to adjacent pairs of populations. Landscape elements that strongly impede dispersal are the easiest to identify. However, power and accuracy decrease drastically when landscape complexity increases and the contrast between the permeability of landscape elements decreases. We provide guidelines for future studies and underline the needs to evaluate or develop approaches that are more powerful.

Baseline intrinsic flammability of Earth’s ecosystems estimated from paleoatmospheric oxygen over the past 350 million years

Atmospheric oxygen (O2) is estimated to have varied greatly throughout Earth’s history and has been capable of influencing wildfire activity wherever fuel and ignition sources were present. Fires consume huge quantities of biomass in all ecosystems and play an important role in biogeochemical cycles. This means that understanding the influence of O2 on past fire activity has far-reaching consequences for the evolution of life and Earth’s biodiversity over geological timescales. We have used a strong electrical ignition source to ignite smoldering fires, and we measured their self-sustaining propagation in atmospheres of different oxygen concentrations. These data have been used to build a model that we use to estimate the baseline intrinsic flammability of Earth’s ecosystems according to variations in O2 over the past 350 million years (Ma). Our aim is to highlight times in Earth’s history when fire has been capable of influencing the Earth system. We reveal that fire activity would be greatly suppressed below 18.5% O2, entirely switched off below 16% O2, and rapidly enhanced between 19–22% O2. We show that fire activity and, therefore, its influence on the Earth system would have been high during the Carboniferous (350–300 Ma) and Cretaceous (145–65 Ma) periods; intermediate in the Permian (299–251 Ma), Late Triassic (285–201 Ma), and Jurassic (201–145 Ma) periods; and surprisingly low to lacking in the Early–Middle Triassic period between 250–240 Ma. These baseline variations in Earth’s flammability must be factored into our understanding of past vegetation, biodiversity, evolution, and biogeochemical cycles.

The evolution of the control of food intake

The ultimate goal of an organism is to maximise its inclusive fitness, and an important sub-goal must be the optimisation of the lifetime pattern of food intake, in order to meet the nutrient demands of survival, growth and reproduction. The conventional assumption that fitness is maximised by maximising daily food intake, subject to physical and physiological constraints, has been challenged recently. Instead, it can be argued that fitness is maximised by balancing benefits and costs over the organisms lifetime. The fitness benefits of food intake are a function of its contribution to survival, growth (including necessary body reserves) and reproduction. Against these benefits must be set costs. These costs include not only extrinsic foraging costs and risks, such as those due to predation, but also intrinsic costs associated with food intake, such as obesity and oxidative metabolism that may reduce vitality and lifespan. We argue that the aggregate of benefits and costs form the fitness function of food intake and present examples of such an approach to predicting optimal food intake.

GENETIC DRIFT AND COLLECTIVE DISPERSAL CAN RESULT IN CHAOTIC GENETIC PATCHINESS

Chaotic genetic patchiness denotes unexpected patterns of genetic differentiation that are observed at a fine scale and are not stable in time. These patterns have been described in marine species with free‐living larvae, but are unexpected because they occur at a scale below the dispersal range of pelagic larvae. At the scale where most larvae are immigrants, theory predicts spatially homogeneous, temporally stable genetic variation. Empirical studies have suggested that genetic drift interacts with complex dispersal patterns to create chaotic genetic patchiness. Here we use a coancestry model and individual‐based simulations to test this idea. We found that chaotic genetic patterns (qualified by global FST and spatio‐temporal variation in FSTs between pairs of samples) arise from the combined effects of (1) genetic drift created by the small local effective population sizes of the sessile phase and variance in contribution among breeding groups and (2) collective dispersal of related individuals in the larval phase. Simulations show that patchiness levels qualitatively comparable to empirical results can be produced by a combination of strong variance in reproductive success and mild collective dispersal. These results call for empirical studies of the effective number of breeders producing larval cohorts, and population genetics at the larval stage.

Associations between basal metabolic rate and reproductive performance in C57BL/6J mice

SUMMARY Basal metabolic rate (BMR) is highly variable, both between and within species. One hypothesis is that this variation may be linked to the capacity for sustained rate of energy expenditure, leading to associations between high BMR and performance during energy-demanding periods of life history, such as reproduction. However, despite the attractive nature of this hypothesis, previous studies have failed to show an association between BMR and fecundity. Our approach was to mate 304 C57BL/6J mice and allow them to wean pups before measuring BMR by indirect calorimetry. We did not find an association between BMR and litter mass, size or pup mass at birth or weaning that could not be accounted for by the body mass of the dam. There was also no relationship between BMR (or BMR corrected for body mass) and birth or weaning success, losses during weaning, or sex ratio. However, a significant relationship was found between BMR and gestational weight loss indicative of foetal resorption. This suggests that during pregnancy the available energy may be limited and partitioned away from the growing foetus and towards maintenance of the mother. In this context, a high BMR may actually be disadvantageous, conflicting with the idea that high BMR may bring reproductive benefits.

A Theory of Associating Food Types with Their Postingestive Consequences

Animals often face complex and changing food environments. While such environments are challenging, an animal should make an association between a food type and its properties (such as the presence of a nutrient or toxin). We use information theory concepts, such as mutual information, to establish a theory for the development of these associations. In this theory, associations are assumed to maximize the mutual information between foods and their consequences. We show that associations are invariably imperfect. An association’s accuracy increases with the length of a feeding session and the relative frequency of a food type but decreases as time delay between consumption and postingestive consequence increases. Surprisingly, the accuracy of an association is independent of the number of additional food types in the environment. The rate of information transfer between novel foods and a forager depends on the forager’s diet. In light of this theory, an animal’s diet may have two competing goals: first, the provision of an appropriate balance of nutrients, and second, the ability to quickly and accurately learn the properties of novel foods. We discuss the ecological and behavioral implications of making associational errors and contrast the timescale and mechanisms of our theory with those of existing theory.

Having it all: historical energy intakes do not generate the anticipated trade-offs in fecundity

An axiom of life-history theory, and fundamental to our understanding of ageing, is that animals must trade-off their allocation of resources since energy and nutrients are limited. Therefore, animals cannot ‘have it all’—combine high rates of fecundity with extended lifespans. The idea of life-history trade-offs was recently challenged by the discovery that ageing may be governed by a small subset of molecular processes independent of fitness. We tested the ‘trade-off’ and ‘having it all’ theories by examining the fecundities of C57BL/6J mice placed onto four different dietary treatments that generated caloric intakes from −21 to +8.6% of controls. We predicted body fat would be deposited in relation to caloric intake. Excessive body fat is known to cause co-morbidities that shorten lifespan, while caloric restriction enhances somatic protection and increases longevity. The trade-off model predicts that increased fat would be tolerated because reproductive gain offsets shortened longevity, while animals on a restricted intake would sacrifice reproduction for lifespan extension. The responses of body fat to treatments followed our expectations, however, there was a negative relationship between reproductive performance (fecundity, litter mass) and historical intake/body fat. Our dietary restricted animals had lower protein oxidative damage and appeared able to combine life-history traits in a manner contrary to traditional expectations by having increased fecundity with the potential to have extended lifespans.

Chapter 6 – Infrared Image Analysis as a Tool for Studying the Horizontal Smoldering Propagation of Laboratory Peat Fires

Smoldering fires in peatlands can consume large areas of peat and release important amounts of carbon to the atmosphere as they self-propagate. This chapter focuses on the use of infrared images to characterize the horizontal propagation of smoldering fires in laboratory experiments. In these laboratory experiments an infrared camera takes images of the peat surface at regular intervals during the experiment. We present methods to process and analyze these infrared images that identify the shape and position of the smoldering front, quantify the maximum energy flux, the spread rate and direction of the front and its variability to time. To demonstrate our methods we analyze images from experiments that record the smoldering of dry peats (25% moisture content, mass of water per mass of dry peat) and wet peats (100% moisture content). Infrared images are used to quantify the effect of moisture content upon the smoldering fronts. Our methods demonstrate that smoldering combustion in dry peats has a wider front (6.8 ± 1 cm for the dry peat, 2.4 ± 0.7 cm for the wet peat), a faster spread rate (4.3 ± 1 cm/h for dry peat, 2.6 ± 0.7 cm/h for wet peat), and a lower peak of radiative energy flux (7.1 ± 0.7 kW/m2 for dry peat, 10.51 ± 2.1 kW/m2 for wet peat). Our infrared image analysis is a useful tool to characterize peat fires at an experimental scale. These methods can be applied to peats with different characteristics to identify and compare smoldering propagation dynamics.

Inferring recent migration rates from individual genotypes

We present a novel and straightforward method for estimating recent migration rates between discrete populations using multilocus genotype data. The approach builds upon a two‐step sampling design, where individual genotypes are sampled before and after dispersal. We develop a model that estimates all pairwise backwards migration rates (mij, the probability that an individual sampled in population i is a migrant from population j) between a set of populations. The method is validated with simulated data and compared with the methods of BayesAss and Structure. First, we use data for an island model and then we consider more realistic data simulations for a metapopulation of the greater white‐toothed shrew (Crocidura russula). We show that the precision and bias of estimates primarily depend upon the proportion of individuals sampled in each population. Weak sampling designs may particularly affect the quality of the coverage provided by 95% highest posterior density intervals. We further show that it is relatively insensitive to the number of loci sampled and the overall strength of genetic structure. The method can easily be extended and makes fewer assumptions about the underlying demographic and genetic processes than currently available methods. It allows backwards migration rates to be estimated across a wide range of realistic conditions.