High frequency fire alters C:N:P stoichiometry in forest litter

Fire is a major driver of ecosystem change and can disproportionately affect the cycling of different nutrients. Thus, a stoichiometric approach to investigate the relationships between nutrient availability and microbial resource use during decomposition is likely to provide insight into the effects of fire on ecosystem functioning. We conducted a field litter bag experiment to investigate the long-term impact of repeated fire on the stoichiometry of leaf litter C, N and P pools, and nutrient-acquiring enzyme activities during decomposition in a wet sclerophyll eucalypt forest in Queensland, Australia. Fire frequency treatments have been maintained since 1972, including burning every two years (2yrB), burning every four years (4yrB) and no burning (NB). C:N ratios in freshly fallen litter were 29-42% higher and C:P ratios were 6-25% lower for 2yrB than NB during decomposition, with correspondingly lower 2yrB N:P ratios (27-32) than for NB (34-49). Trends in litter soluble and microbial N:P ratios were similar to the overall litter N:P ratios across fire treatments. Consistent with these, the ratio of activities for N-acquiring to P-acquiring enzymes in litter was higher for 2yrB than NB while 4yrB was generally intermediate between 2yrB and NB. Decomposition rates of freshly fallen litter were significantly lower for 2yrB (72±2% mass remaining at the end of experiment) than for 4yrB (59±3%) and NB (62±3%), a difference that may be related to effects of N limitation, lower moisture content, and/or litter C quality. Results for older mixed-age litter were similar to those for freshly fallen litter although treatment differences were less pronounced. Overall, these findings show that frequent fire (2yrB) decoupled N and P cycling, as manifested in litter C:N:P stoichiometry and in microbial biomass N:P ratio and enzymatic activities. These data indicate that fire induced a transient shift to N-limited ecosystem conditions during the post-fire recovery phase. This article is protected by copyright. All rights reserved.

Crinkles in connectivity: combining genetics and other types of biological data to estimate movement and interbreeding between populations

Marine species generally have large population sizes, continuous distributions and high dispersal capacity. Despite this, they are often subdivided into separate populations, which are the basic units of fisheries management. For example, populations of some fisheries species across the deep water of the Timor Trench are genetically different, inferring minimal movement and interbreeding. When connectivity is higher than the Timor Trench example, but not so high that the populations become one, connectivity between populations is crinkled. Crinkled connectivity occurs when migration is above the threshold required to link populations genetically, but below the threshold for demographic links. In future, genetic estimates of connectivity over crinkled links could be uniquely combined with other data, such as estimates of population size and tagging and tracking data, to quantify demographic connectedness between these types of populations. Elasmobranch species may be ideal targets for this research because connectivity between populations is more likely to be crinkled than for finfish species. Fisheries stock-assessment models could be strengthened with estimates of connectivity to improve the strategic and sustainable harvesting of biological resources.

Mixture of time-dependent growth models with an application to blue swimmer crab length-frequency data

Understanding how aquatic species grow is fundamental in fisheries because stock assessment often relies on growth dependent statistical models. Length-frequency-based methods become important when more applicable data for growth model estimation are either not available or very expensive. In this article, we develop a new framework for growth estimation from length-frequency data using a generalized von Bertalanffy growth model (VBGM) framework that allows for time-dependent covariates to be incorporated. A finite mixture of normal distributions is used to model the length-frequency cohorts of each month with the means constrained to follow a VBGM. The variances of the finite mixture components are constrained to be a function of mean length, reducing the number of parameters and allowing for an estimate of the variance at any length. To optimize the likelihood, we use a minorization–maximization (MM) algorithm with a Nelder–Mead sub-step. This work was motivated by the decline in catches of the blue swimmer crab (BSC) (Portunus armatus) off the east coast of Queensland, Australia. We test the method with a simulation study and then apply it to the BSC fishery data.

Correlation between NIRS generated and chemically measured feed quality data in barley (Hordeum vulgare), and potential use in QTL analysis identification

A study was performed to investigate the value of near infrared reflectance spectroscopy (NIRS) as an alternate method to analytical techniques for identifying QTL associated with feed quality traits. Milled samples from an F6-derived recombinant inbred Tallon/Scarlett population were incubated in the rumen of fistulated cattle, recovered, washed and dried to determine the in-situ dry matter digestibility (DMD). Both pre- and post-digestion samples were analysed using NIRS to quantify key quality components relating to acid detergent fibre, starch and protein. This phenotypic data was used to identify trait associated QTL and compare them to previously identified QTL. Though a number of genetic correlations were identified between the phenotypic data sets, the only correlation of most interest was between DMD and starch digested (r = -0.382). The significance of this genetic correlation was that the NIRS data set identified a putative QTL on chromosomes 7H (LOD = 3.3) associated with starch digested. A QTL for DMD occurred in the same region of chromosome 7H, with flanking markers fAG/CAT63 and bPb-0758. The significant correlation and identification of this putative QTL, highlights the potential of technologies like NIRS in QTL analysis.