Hierarchical modelling of temperature and habitat size effects on population dynamics of North Atlantic cod

Understanding how temperature affects cod (Gadus morhua) ecology is important for forecasting how populations will develop as climate changes in future. The effects of spawning-season temperature and habitat size on cod recruitment dynamics have been investigated across the North Atlantic. Ricker and Beverton and Holt stock–recruitment (SR) models were extended by applying hierarchical methods, mixed-effects models, and Bayesian inference to incorporate the influence of these ecosystem factors on model parameters representing cod maximum reproductive rate and carrying capacity. We identified the pattern of temperature effects on cod productivity at the species level and estimated SR model parameters with increased precision. Temperature impacts vary geographically, being positive in areas where temperatures are <5°C, and negative for higher temperatures. Using the relationship derived, it is possible to predict expected changes in population-specific reproductive rates and carrying capacities resulting from temperature increases. Further, carrying capacity covaries with available habitat size, explaining at least half its variability across stocks. These patterns improve our understanding of environmental impacts on key population parameters, which is required for an ecosystem approach to cod management, particularly under ocean-warming scenarios. Key words: carrying capacity , cod , hierarchical models , North Atlantic , temperature , uncertaint

Recognizability bias in citizen science photographs

Citizen science and automated collection methods increasinglydepend on image recognition to provide the amountsof observational data research and management needs.Recognition models, meanwhile, also require large amounts ofdata from these sources, creating a feedback loop between themethods and tools. Species that are harder to recognize, bothfor humans and machine learning algorithms, are likely to beunder-reported, and thus be less prevalent in the trainingdata. As a result, the feedback loop may hamper trainingmostly for species that already pose the greatest challenge. Inthis study, we trained recognition models for various taxa, andfound evidence for a‘recognizability bias’, where species thatare more readily identified by humans and recognitionmodels alike are more prevalent in the available image data.This pattern is present across multiple taxa, and does notappear to relate to differences in picture quality, biologicaltraits or data collection metrics other than recognizability. Thishas implications for the expected performance of futuremodels trained with more data, including such challenging species. citizen science, image recognition, machinelearning, recognizability, artificial intelligence/environmental science/ecology, Ecology, conservation and global change biologypublishedVersio

The Point Process Framework for Integrated Modelling of Biodiversity Data

The quantity and types of biodiversity data being collected have increased in recent years. If we are to model and monitor biodiversity effectively, we need to respect how different data sets were collected, and effectively integrate these data types together. The framework that has emerged to do this is based on a point process formulation, with individuals as points and their distribution as a realisation of a random field. We describe this formulation and how the process model for the actual distribution is linked to the data that is collected through observation models. The observation models describe the data collection process and its uncertainties and biases. We provide an example of using these methods to model species of Norwegian freshwater fish, which shows how integrated models can be adapted to the data we can collect. We summarise the modelling issues that arise and the approaches that could be taken to solve them

Dealing with varying detection probability, unequal sample sizes and clumped distributions in count data

Peer reviewe

Is more data always better? A simulation study of benefits and limitations of integrated distribution models

Species distribution models are popular and widely applied ecological tools. Recent increases in data availability have led to opportunities and challenges for species distribution modelling. Each data source has different qualities, determined by how it was collected. As several data sources can inform on a single species, ecologists have often analysed just one of the data sources, but this loses information, as some data sources are discarded. Integrated distribution models (IDMs) were developed to enable inclusion of multiple datasets in a single model, whilst accounting for different data collection protocols. This is advantageous because it allows efficient use of all data available, can improve estimation and account for biases in data collection. What is not yet known is when integrating different data sources does not bring advantages. Here, for the first time, we explore the potential limits of IDMs using a simulation study integrating a spatially biased, opportunistic, presence‐only dataset with a structured, presence–absence dataset. We explore four scenarios based on real ecological problems; small sample sizes, low levels of detection probability, correlations between covariates and a lack of knowledge of the drivers of bias in data collection. For each scenario we ask; do we see improvements in parameter estimation or the accuracy of spatial pattern prediction in the IDM versus modelling either data source alone? We found integration alone was unable to correct for spatial bias in presence‐only data. Including a covariate to explain bias or adding a flexible spatial term improved IDM performance beyond single dataset models, with the models including a flexible spatial term producing the most accurate and robust estimates. Increasing the sample size of presence–absence data and having no correlated covariates also improved estimation. These results demonstrate under which conditions integrated models provide benefits over modelling single data sources

Heritability of Asymmetry and Lateral Plate Number in the Threespine Stickleback

Peer reviewe

Integrating data from different taxonomic resolutions to better estimate community alpha diversity

Integrated distribution models (IDMs), in which datasets with different properties are analysed together, are becoming widely used to model species distributions and abundance in space and time. To date, the IDM literature has focused on technical and statistical issues, such as the precision of parameter estimates and mitigation of biases arising from unstructured data sources. However, IDMs have an unrealised potential to estimate ecological properties that could not be properly derived from the source datasets if analysed separately. We present a model that estimates community alpha diversity metrics by integrating one species-level dataset of presence–absence records with a co-located dataset of group-level counts (i.e. lacking information about species identity). We illustrate the ability of community IDMs to capture the true alpha diversity through simulation studies and apply the model to data from the UK Pollinator Monitoring Scheme, to describe spatial variation in the diversity of solitary bees, bumblebees and hoverflies. The simulation and case studies showed that the proposed IDM produced more precise estimates of the community diversity than the single models, and the analysis of the real dataset further showed that the alpha diversity estimates from the IDM were averages of the single models. Our findings also revealed that IDMs had a higher prediction accuracy for all the insect groups in most cases, with this performance linked to the information provided by a data source into the IDM

Integrated species distribution models fitted in INLA are sensitive to mesh parameterisation

The ever-growing popularity of citizen science, as well as recent technological and digital developments, have allowed the collection of data on species' distributions at an extraordinary rate. In order to take advantage of these data, information of varying quantity and quality needs to be integrated. Point process models have been proposed as an elegant way to achieve this for estimates of species distributions. These models can be fitted efficiently using Bayesian methods based on integrated nested Laplace approximations (INLA) with stochastic partial differential equations (SPDEs). This approach uses an efficient way to model spatial autocorrelation using a Gaussian random field and a triangular mesh over the spatial domain. The mesh is constructed by user-defined variables, so effectively represents a free parameter in the model. However, there is a lack of understanding about how to set these mesh parameters, and their effect on model performance. Here, we assess how mesh parameters affect predictions and model fit to estimate the distribution of the serotine bat, Eptesicus serotinus, in Great Britain. A Bayesian INLA model was fitted using five meshes of varying densities to a dataset comprising both structured observations from a national monitoring programme and opportunistic records. We demonstrate that mesh density impacted spatial predictions with a general loss of accuracy with increasing mesh coarseness. However, we also show that the finest mesh was unable to overcome spatial biases in the data. In addition, the magnitude of the covariate effects differed markedly between meshes. This confirms that mesh parameterisation is an important and delicate process with implications for model inference. We discuss how species distribution modellers might adapt their use of INLA in the light of these findings

Variable strength of forest stand attributes and weather conditions on the questing activity of Ixodes ricinus ticks over years in managed forests

Given the ever-increasing human impact through land use and climate change on the environment, we crucially need to achieve a better understanding of those factors that influence the questing activity of ixodid ticks, a major disease-transmitting vector in temperate forests. We investigated variation in the relative questing nymph densities of Ixodes ricinus in differently managed forest types for three years (2008–2010) in SW Germany by drag sampling. We used a hierarchical Bayesian modeling approach to examine the relative effects of habitat and weather and to consider possible nested structures of habitat and climate forces. The questing activity of nymphs was considerably larger in young forest successional stages of thicket compared with pole wood and timber stages. Questing nymph density increased markedly with milder winter temperatures. Generally, the relative strength of the various environmental forces on questing nymph density differed across years. In particular, winter temperature had a negative effect on tick activity across sites in 2008 in contrast to the overall effect of temperature across years. Our results suggest that forest management practices have important impacts on questing nymph density. Variable weather conditions, however, might override the effects of forest management practices on the fluctuations and dynamics of tick populations and activity over years, in particular, the preceding winter temperatures. Therefore, robust predictions and the detection of possible interactions and nested structures of habitat and climate forces can only be quantified through the collection of long-term data. Such data are particularly important with regard to future scenarios of forest management and climate warming

Position measurements for the isotope production facility and the switchyard kicker upgrade projects

The Los Alamos Neutron Science Center (LANSCE) is installing two beam lines to both improve operational tuning and provide new capabilities within the facility. The Isotope Production Facility (IPF) will provide isotopes for medical purposes by using the H' beam spur at 100 MeV and the Switchyard Kicker Upgrade (SYK) will allow the LANSCE 800-MeV H beam to be rapidly switched between various beam lines within the facility. The beam position measurements for both of these beam lines uses a standard micro-stripline beam position monitor (BPM) with both a 50-mm and 75-mm radius. The cable plant is unique in that it unambiguously provides a method of verifying the operation of the complete position measurement. The processing electronics module uses a log ratio technique with error corrections such that it has a dynamic range of -12 dBm to -85 dBm with errors less than 0.15 dB within this range. This paper will describe the primary components of these measurement systems and provide initial data of their operation