The effects of landscape modifications on the long-term persistence of animal populations

Background: The effects of landscape modifications on the long-term persistence of wild animal populations is of crucial importance to wildlife managers and conservation biologists, but obtaining experimental evidence using real landscapes is usually impossible. To circumvent this problem we used individual-based models (IBMs) of interacting animals in experimental modifications of a real Danish landscape. The models incorporate as much as possible of the behaviour and ecology of four species with contrasting life-history characteristics: skylark (Alauda arvensis), vole (Microtus agrestis), a ground beetle (Bembidion lampros) and a linyphiid spider (Erigone atra). This allows us to quantify the population implications of experimental modifications of landscape configuration and composition. Methodology/Principal Findings: Starting with a real agricultural landscape, we progressively reduced landscape complexity by (i) homogenizing habitat patch shapes, (ii) randomizing the locations of the patches, and (iii) randomizing the size of the patches. The first two steps increased landscape fragmentation. We assessed the effects of these manipulations on the long-term persistence of animal populations by measuring equilibrium population sizes and time to recovery after disturbance. Patch rearrangement and the presence of corridors had a large effect on the population dynamics of species whose local success depends on the surrounding terrain. Landscape modifications that reduced population sizes increased recovery times in the short-dispersing species, making small populations vulnerable to increasing disturbance. The species that were most strongly affected by large disturbances fluctuated little in population sizes in years when no perturbations took place. Significance: Traditional approaches to the management and conservation of populations use either classical methods of population analysis, which fail to adequately account for the spatial configurations of landscapes, or landscape ecology, which accounts for landscape structure but has difficulty predicting the dynamics of populations living in them. Here we show how realistic and replicable individual-based models can bridge the gap between non-spatial population theory and non-dynamic landscape ecology. A major strength of the approach is its ability to identify population vulnerabilities not detected by standard population viability analyses

Underwater microscope for measuring spatial and temporal changes in bed-sediment grain size

This paper is not subject to U.S. copyright. The definitive version was published in Sedimentary Geology 202 (2007): 402-408, doi:10.1016/j.sedgeo.2007.03.020.For more than a century, studies of sedimentology and sediment transport have measured bed-sediment grain size by collecting samples and transporting them back to the laboratory for grain-size analysis. This process is slow and expensive. Moreover, most sampling systems are not selective enough to sample only the surficial grains that interact with the flow; samples typically include sediment from at least a few centimeters beneath the bed surface. New hardware and software are available for in situ measurement of grain size. The new technology permits rapid measurement of surficial bed sediment. Here we describe several systems we have deployed by boat, by hand, and by tripod in rivers, oceans, and on beaches

A deeper understanding of system interactions can explain contradictory field results on pesticide impact on honey bees

While there is widespread concern regarding the impact of pesticides on honey bees, well-replicated field experiments, to date, have failed to provide clear insights on pesticide effects. Here, we adopt a systems biology approach to gain insights into the web of interactions amongst the factors influencing honey bee health. We put the focus on the properties of the system that depend upon its architecture and not on the strength, often unknown, of each single interaction. Then we test in vivo, on caged honey bees, the predictions derived from this modelling analysis. We show that the impact of toxic compounds on honey bee health can be shaped by the concurrent stressors affecting bees. We demonstrate that the immune-suppressive capacity of the widespread pathogen of bees, deformed wing virus, can introduce a critical positive feed-back loop in the system causing bistability, i.e., two stable equilibria. Therefore, honey bees under similar initial conditions can experience different consequences when exposed to the same stressor, including prolonged survival or premature death. The latter can generate an increased vulnerability of the hive to dwindling and collapse. Our conclusions reconcile contrasting field-testing outcomes and have important implications for the application of field studies to complex systems

Multiphysics simulation tools for designing motors for traction applications in hybrid and electric vehicles

Motor manufacturers are facing a difficult challenge in designing traction motors for the latest generation of hybrid and all-electric vehicles. The efficiency with which these motors can perform is critical, as it impacts on the vehicle range and battery life. Many of the issues involved in the motor design have a complex nature which requires multiple fields of physics such as electromagnetics (EM), mechanics and thermal analysis. All these physics are usually interdependent and have to be considered collectively in order to obtain optimal performance for a particular scenario. This paper presents a multiphysics simulation tool that was implemented to address this situation. The Opera FEA software suite [1] was developed to include a multiphysics analysis that can link several EM, thermal and stress analyses. Opera’s Machines Environment (parameterised template software for designing motors and generators) has been extended to allow easy setup of coupled multiphysics analyses such as EM to thermal and EM to stress. In order to further facilitate the coupling of different analyses, a link to the Python programming language was embedded in Opera FEA software. The embedded Python facility offers options to perform certain post-processing operations during the solving stage and hence allow data transfer between different stages of the multiphysics analysis. It also extends Opera’s capabilities to interact with other FEA software

Developing a subpopulation-based model for the olive fruit fly Bactrocera oleae (Diptera: Tephritidae): conceptual model out-line

Bactrocera oleae Rossi (olive fruit fly) is a dipteran of the family Tephritidae, considered the key pest of olives in Mediterranean countries, where it causes losses of great economic impact. Nat ural pest control is an important alternative or complement to the use of plant protection products against B. oleae. This is an ecosystem service that can be enhanced if we are able to predict its behav ior, which can be done through computer models simulating interactions between animals, agricul tural management and climate. In this paper we present the conceptual model of a spatially explicit subpopulation-based model being developed for B. oleae in olive groves. In this modelling tech nique, the simulated dynamic landscape is segmented into non-overlapping cells, where the sub populations of B. oleae are represented as separate but interacting entities. Our model is based on the Animal Landscape Man Simulation System (ALMaSS), which comprises a highly detailed and realistic landscape representation, incorporating data on different aspects of farm management, crop development, and climate, and where simulated entities operate. We present the general out line of B. oleae’s life cycle, as well as succinct information on how these organisms interact with their environment. This is a step for the development of the final model and its implementation in AL MaSS.info:eu-repo/semantics/publishedVersio

High accuracy monitoring of honey bee colony development by a quantitative method

Honey bees are key insect pollinators, providing important economic and ecological value for human beings and ecosystems. This has triggered the development of several monitoring methods for assessing the temporal development of colony size, food storage, brood and pathogens. Nonetheless, most of these methods are based on visual assessments that are observer-dependent and prone to bias. Furthermore, the impact on colony development (invasiveness), as well as accuracy, were rarely considered when implementing new methods. In this study, we present and test a novel accurate and observer-independent method for honey bee colony assessment, capable of being fully standardized. Honey bee colony size is quantified by assessing the weight of adult bees, while brood and provision are assessed by taking photos and conducting image analysis of the combs with the image analysis software DeepbeeVR . The invasiveness and accuracy of the method were investigated using field data from two experimental apiaries in Portugal, comparing results from test and control colonies. At the end of each field experiment, most of the tested colonies had the same colony size, brood levels and honey production as the control colonies. Nonetheless, continuous weight data indicated some disturbance in tested colonies in the first year of monitoring. The overall accuracy of the image analysis software was improved by training, indicating that it is possible to adapt the software to local conditions. We conclude that the use of this fully quantitative method offers a more accurate alternative to classic visual colony assessments, with negligible impact on colony development.This work was supported by European Food Safety Authority under grant OC/EFSA/SCER/2017/02; FCT provided financial support by national funds (FCT/MCTES) to CFE (UIDB/04004/2020) and CIMO (UIDB/00690/2020); NC was financed by FCT under PhD grant SFRH/BD/133352/2017; YLD by DCE (Danish Centre for Environment and Energy) under grant 21628-82105.info:eu-repo/semantics/publishedVersio

Temporal and spatial patterns of bovine Escherichia coli O157 prevalence and comparison of temporal changes in the patterns of phage types associated with bovine shedding and human E. coli O157 cases in Scotland between 1998-2000 and 2002-2004

Background: Escherichia coli O157 is an important cause of acute diarrhoea, haemorrhagic colitis and, especially in children, haemolytic uraemic syndrome (HUS). Incidence rates for human E. coli O157 infection in Scotland are higher than most other United Kingdom, European and North American countries. Cattle are considered the main reservoir for E. coli O157. Significant associations between livestock related exposures and human infection have been identified in a number of studies. Results: Animal Studies: There were no statistically significant differences (P = 0.831) in the mean farm-level prevalence between the two studies (SEERAD: 0.218 (95% CI: 0.141-0.32); IPRAVE: 0.205 (95% CI: 0.135-0.296)). However, the mean pat-level prevalence decreased from 0.089 (95% CI: 0.075-0.105) to 0.040 (95% CI: 0.028-0.053) between the SEERAD and IPRAVE studies respectively (P < 0.001). Highly significant (P < 0.001) reductions in mean pat-level prevalence were also observed in the spring, in the North East and Central Scotland, and in the shedding of phage type (PT) 21/28. Human Cases: Contrasting the same time periods, there was a decline in the overall comparative annual reported incidence of human cases as well as in all the major PT groups except 'Other' PTs. For both cattle and humans, the predominant phage type between 1998 and 2004 was PT21/28 comprising over 50% of the positive cattle isolates and reported human cases respectively. The proportion of PT32, however, was represented by few (<5%) of reported human cases despite comprising over 10% of cattle isolates. Across the two studies there were differences in the proportion of PTs 21/28, 32 and 'Other' PTs in both cattle isolates and reported human cases; however, only differences in the cattle isolates were statistically significant (P = 0.002). Conclusion: There was no significant decrease in the mean farm-level prevalence of E. coli O157 between 1998 and 2004 in Scotland, despite significant declines in mean pat-level prevalence. Although there were declines in the number of human cases between the two study periods, there is no statistically significant evidence that the overall rate (per 100,000 population) of human E. coli O157 infections in Scotland over the last 10 years has altered. Comparable patterns in the distribution of PTs 21/28 and 32 between cattle and humans support a hypothesized link between the bovine reservoir and human infections. This emphasizes the need to apply and improve methods to reduce bovine shedding of E. coli O157 in Scotland where rates appear higher in both cattle and human populations, than in other countrie

Using problem formulation for fit‐for‐purpose pre‐market environmental risk assessments of regulated stressors

Pre‐market/prospective environmental risk assessments (ERAs) contribute to risk analyses performed to facilitate decisions about the market introduction of regulated stressors. Robust ERAs begin with an explicit problem formulation, which involves among other steps: (1) formally devising plausible pathways to harm that describe how the deployment of a regulated stressor could be harmful; (2) formulating risk hypotheses about the likelihood and severity of such events; (3) identifying the information that will be useful to test the risk hypotheses; and (4) developing a plan to acquire new data for hypothesis testing should tests with existing information be insufficient for decision‐making. Here, we apply problem formulation to the assessment of possible adverse effects of RNA interference‐based insecticidal genetically modified (GM) plants, GM growth hormone coho salmon, gene drive‐modified mosquitoes and classical biological weed control agents on non‐target organisms in a prospective manner, and of neonicotinoid insecticides on bees in a retrospective manner. In addition, specific considerations for the problem formulation for the ERA of nanomaterials and for landscape‐scale population‐level ERAs are given. We argue that applying problem formulation to ERA maximises the usefulness of ERA studies for decision‐making, through an iterative process, because: (1) harm is defined explicitly from the start; (2) the construction of risk hypotheses is guided by policy rather than an exhaustive attempt to address any possible differences; (3) existing information is used effectively; (4) new data are collected with a clear purpose; (5) risk is characterised against well‐defined criteria of hypothesis corroboration or falsification; and (6) risk assessment conclusions can be communicated clearly. However, problem formulation is still often hindered by the absence of clear policy goals and decision‐making criteria (e.g. definition of protection goals and what constitutes harm) that are needed to guide the interpretation of scientific information. We therefore advocate further dialogue between risk assessors and risk managers to clarify how ERAs can address policy goals and decision‐making criteria. Ideally, this dialogue should take place for all classes of regulated stressors, as this can promote alignment and consistency on the desired level of protection and maximum tolerable impacts across regulated stressors

Resistant Starches Protect against Colonic DNA Damage and Alter Microbiota and Gene Expression in Rats Fed a Western Diet123

Resistant starch (RS), fed as high amylose maize starch (HAMS) or butyrylated HAMS (HAMSB), opposes dietary protein-induced colonocyte DNA damage in rats. In this study, rats were fed Western-type diets moderate in fat (19%) and protein (20%) containing digestible starches [low amylose maize starch (LAMS) or low amylose whole wheat (LAW)] or RS [HAMS, HAMSB, or a whole high amylose wheat (HAW) generated by RNA interference] for 11 wk (n = 10/group). A control diet included 7% fat, 13% protein, and LAMS. Colonocyte DNA single-strand breaks (SSB) were significantly higher (by 70%) in rats fed the Western diet containing LAMS relative to controls. Dietary HAW, HAMS, and HAMSB opposed this effect while raising digesta levels of SCFA and lowering ammonia and phenol levels. SSB correlated inversely with total large bowel SCFA, including colonic butyrate concentration (R2 = 0.40; P = 0.009), and positively with colonic ammonia concentration (R2 = 0.40; P = 0.014). Analysis of gut microbiota populations using a phylogenetic microarray revealed profiles that fell into 3 distinct groups: control and LAMS; HAMS and HAMSB; and LAW and HAW. The expression of colonic genes associated with the maintenance of genomic integrity (notably Mdm2, Top1, Msh3, Ung, Rere, Cebpa, Gmnn, and Parg) was altered and varied with RS source. HAW is as effective as HAMS and HAMSB in opposing diet-induced colonic DNA damage in rats, but their effects on the large bowel microbiota and colonocyte gene expression differ, possibly due to the presence of other fiber components in HAW