Simultaneous-count models to estimate abundance from counts of unmarked individuals with imperfect detection

We developed a method to estimate population abundance from simultaneous counts of unmarked individuals over multiple sites. We considered that at each sampling occasion, individuals in a population could be detected at 1 of the survey sites or remain undetected and used either multinomial or binomial simultaneous-count models to estimate abundance, the latter being equivalent to an N-mixture model with one site. We tested model performance with simulations over a range of detection probabilities, population sizes, growth rates, number of years, sampling occasions, and sites. We then applied our method to 3 critically endangered vulture species in Cambodia to demonstrate the real-world applicability of the model and to provide the first abundance estimates for these species in Cambodia. Our new approach works best when existing methods are expected to perform poorly (i.e., few sites and large variation in abundance among sites) and if individuals may move among sites between sampling occasions. The approach performed better when there were >8 sampling occasions and net probability of detection was high (>0.5). We believe our approach will be useful in particular for simultaneous surveys at aggregation sites, such as roosts. The method complements existing approaches for estimating abundance of unmarked individuals and is the first method designed specifically for simultaneous counts.     

Site-selection bias and apparent population declines in long-term studies

Detecting population declines is a critical task for conservation biology. Logistical difficulties and the spatiotemporal variability of populations make estimation of population declines difficult. For statistical reasons, estimates of population decline may be biased when study sites are chosen based on abundance of the focal species. In this situation, apparent population declines are likely to be detected even if there is no decline. This site-selection bias is mentioned in the literature but is not well known. We used simulations and real population data to examine the effects of site-selection biases on inferences about population trends. We used a left-censoring method to detect population-size patterns consistent with site-selection bias. The site-selection bias is an important consideration for conservation biologists, and we offer suggestions for minimizing or mitigating it in study design and analysis. Article impact statement: Estimates of population declines are biased if studies begin in large populations, and time-series data show a signature of such an effect.     

A multispecies test of source–sink indicators to prioritize habitat for declining populations

For species at risk of decline or extinction in source–sink systems, sources are an obvious target for habitat protection actions. However, the way in which source habitats are identified and prioritized can reduce the effectiveness of conservation actions. Although sources and sinks are conceptually defined using both demographic and movement criteria, simplifications are often required in systems with limited data. To assess the conservation outcomes of alternative source metrics and resulting prioritizations, we simulated population dynamics and extinction risk for 3 endangered species. Using empirically based habitat population models, we linked habitat maps with measured site‐ or habitat‐specific demographic conditions, movement abilities, and behaviors. We calculated source–sink metrics over a range of periods of data collection and prioritized consistently high‐output sources for conservation. We then tested whether prioritized patches identified the habitats that most affected persistence by removing them and measuring the population response. Conservation decisions based on different source–sink metrics and durations of data collection affected species persistence. Shorter time series obscured the ability of metrics to identify influential habitats, particularly in temporally variable and slowly declining populations. Data‐rich source–sink metrics that included both demography and movement information did not always identify the habitats with the greatest influence on extinction risk. In some declining populations, patch abundance better predicted influential habitats for short‐term regional persistence. Because source–sink metrics (i.e., births minus deaths; births and immigrations minus deaths and emigration) describe net population conditions and cancel out gross population counts, they may not adequately identify influential habitats in declining populations. For many nonequilibrium populations, new metrics that maintain the counts of individual births, deaths, and movement may provide additional insight into habitats that most influence persistence.     

Community dynamics of ammonia oxidizing bacteria in a full-scale wastewater treatment system with nitrification stability

To determine whether the functional stability of nitrification was correlated to a stable community structure of ammonia oxidizing bacteria (AOB) in a full-scale wastewater treatment plant, the AOB community dynamics in a wastewater treatment system was monitored over one year. The community dynamics were investigated using specific PCR followed by terminal restriction fragment length polymorphism (T-RFLP) analysis of the amoA gene. The T-RFLP results indicated that during the period of nitrification stability, the AOB community structure in the full-scale wastewater treatment system was relatively stable, and the average change rate every 15 d of the system was 6.6%±5.8%. The phylogenetic analysis of the cloned amoA gene showed clearly that the dominant AOB in the system was Nitrosomonas spp. The results of this study indicated that throughout the study period, the AOB community structure was relatively stable in the full-scale wastewater treatment system with functional stability of nitrification.     

Modelling coral reef ecosystems with limited observational data

Ecosystem models represent potentially powerful tools for coral reef ecosystem managers. They can provide insight into ecosystem dynamics not achievable through alternative means allowing coral reef managers to assess the potential outcome of any given management decision. One of the main limitations in the applicability of ecosystem models is that they often require detailed empirical data and this can restrict their applicability to ecosystems that are either currently well studied or have the resources available to collect the required data. This study describes the development of a coral reef ecosystem model that can be calibrated to an ecosystem with limited empirical data. Based on the assumption that coral reef ecological structure is generic across all tropical coral reefs and that the magnitude of the interactions between ecological components is reef specific, the dynamics of the ecosystem can be replicated based on limited empirical data. The model successfully replicated the dynamics of three individual reef systems including an inshore and oceanic reef within the Great Barrier Reef and a Caribbean reef system. It highlighted the importance of understanding the specific dynamics of a given reef and that a positive management intervention in one system may result in a negative outcome for another. The model was also used to assess the importance of various interactions within coral reef ecosystems. It identified the interactions between hard corals and other non-algal benthic components as being an important (but currently understudied) facet of coral reef ecology. The development of this modelling approach provides access to ecosystem modelling tools for coral reef managers previously excluded due to a lack of resources or technical expertise.     

Comparing measured and modelled forest carbon stocks in high-boreal forests of harvest and natural-disturbance origin in Labrador, Canada

Understanding the effects of disturbance regimes on carbon (C) stocks and stock changes is a prerequisite to estimating forest C stocks and fluxes. Live-tree, dead-tree, woody debris (WD), stump, buried wood, organic layer, and mineral soil C stock data were collected from high-boreal black spruce (Picea mariana (Mill.) B.S.P.) stands of harvest and fire origin and compared to values predicted by the Carbon Budget Model of the Canadian Forest Sector (CBM-CFS3); the core model of Canada's National Forest Carbon Monitoring, Accounting and Reporting System. Data comparing the effect of natural and anthropogenic disturbance history on forest C stocks are limited, but needed to evaluate models such as the CBM-CFS3. Results showed that adjustments to the CBM-CFS3 volume-to-biomass conversion and partitioning parameters were required for the non-merchantable and branch C pools to accurately capture live-tree C stocks in the studied black spruce ecosystems. Accuracy of the CBM-CFS3 modelled estimates of dead organic matter and soil C pools was improved relative to regional default parameters by increased snag fall and >10 cm WD base decay rates. The model evaluation process also highlighted the importance of developing a bryophyte module to account for bryophyte C dynamics and the physical burial of woody debris by bryophytes. Modelled mineral soil C estimates were improved by applying a preliminary belowground slow C pool base decay rate optimized for the soil type of the studied sites, Humo-Ferric Podzols.     

Sensitivity of plant-pollinator-herbivore communities to changes in phenology

For many species in seasonal environments, warmer springs associated with anthropogenic climate change are causing phenological changes. Within ecological communities, the timing of interactions among species may be altered if the species experience asymmetrical phenological shifts. We present a model that examines the consequences of changes in the relative timing of herbivory and pollination in a community of herbivores and pollinators that share a host plant. Our model suggests that phenological shifts can alter the abundances of these species and, in some cases, their population dynamics. If historical patterns of interactions in a community change and herbivores become active before pollinators, the community could see a reduction in pollinators and an increase in herbivores, while if pollinators become active before herbivores, there could be a loss of stable coexistence. Previous studies have warned of the potential for climate change to cause large phenological mismatches whereby species that depend on one another become so separated in time that they can no longer interact. Our results suggest that climate change-induced phenological shifts can have a major impact on communities even in cases where complete phenological mismatches do not occur.     

Quantifying parameter uncertainty in a coral reef model using Metropolis-Coupled Markov Chain Monte Carlo

Coral reefs are threatened ecosystems, so it is important to have predictive models of their dynamics. Most current models of coral reefs fall into two categories. The first is simple heuristic models which provide an abstract understanding of the possible behaviour of reefs in general, but do not describe real reefs. The second is complex simulations whose parameters are obtained from a range of sources such as literature estimates. We cannot estimate the parameters of these models from a single data set, and we have little idea of the uncertainty in their predictions.We have developed a compromise between these two extremes, which is complex enough to describe real reef data, but simple enough that we can estimate parameters for a specific reef from a time series. In previous work, we fitted this model to a long-term data set from Heron Island, Australia, using maximum likelihood methods. To evaluate predictions from this model, we need estimates of the uncertainty in our parameters. Here, we obtain such estimates using Bayesian Metropolis-Coupled Markov Chain Monte Carlo. We do this for versions of the model in which corals are aggregated into a single state variable (the three-state model), and in which corals are separated into four state variables (the six-state model), in order to determine the appropriate level of aggregation. We also estimate the posterior distribution of predicted trajectories in each case.In both cases, the fitted trajectories were close to the observed data, but we had doubts about the biological plausibility of some parameter estimates. We suggest that informative prior distributions incorporating expert knowledge may resolve this problem. In the six-state model, the posterior distribution of state frequencies after 40 years contained two divergent community types, one dominated by free space and soft corals, and one dominated by acroporid, pocilloporid, and massive corals. The three-state model predicts only a single community type. We conclude that the three-state model hides too much biological heterogeneity, but we need more data if we are to obtain reliable predictions from the six-state model. It is likely that there will be similarly large, but currently unevaluated, uncertainty in the predictions of other coral reef models, many of which are much more complex and harder to fit to real data.     

Influence of host migration between woodland and pasture on the population dynamics of the tick Ixodes ricinus: A modelling approach

Ticks act as vectors of pathogens that can be harmful to animals and/or humans. Epidemiological models can be useful tools to investigate the potential effects of control strategies on diseases such as tick-borne diseases. The modelling of tick population dynamics is a prerequisite to simulating tick-borne diseases and the corresponding spread of the pathogen. We have developed a dynamic model to simulate changes in tick density at different stages (egg, larva, nymph and adult) under the influence of temperature. We have focused on the tick Ixodes ricinus, which is widespread in Europe. The main processes governing the biological cycles of ticks were taken into account: egg laying, hatching, development, host (small, mainly rodents, or large, like deer and cattle, mammals) questing, feeding and mortality. This model was first applied to a homogeneous habitat, where simulations showed the ability of the model to reproduce the general patterns of tick population dynamics. We considered thereafter a multi-habitat model, where three different habitats (woodland, ecotone and meadow) were connected through host migration. Based on this second application, it appears that migration from woodland, via the ecotone, is necessary to sustain the presence of ticks in the meadow. Woodland can therefore be considered as a source of ticks for the meadow, which in turn can be regarded as a sink. The influence of woodland on surrounding tick densities increases in line with the area of this habitat before reaching a plateau. A sensitivity analysis to parameter values was carried out and demonstrated that demographic parameters (sex ratio, development, mortality during feeding and questing, host finding) played a crucial role in the determination of questing nymph densities. This type of modelling approach provides insight into the influence of spatial heterogeneity on tick population dynamics.     

A new approach to the analysis of adjacencies: Potentials for landscape insights

Spatial adjacencies are a key-issue in environmental studies. Adjacency effects have been amply observed for biotic (plants and animals) and abiotic components of ecosystems. Particularly well-documented are the effects from human manufactures onto the contiguous vegetation mosaics of natural and semi-natural areas.In this work we first propose and reformulate association rules analysis (ARA), a relatively new data mining algorithm with very limited scientific applications so far, in the form of an in-depth investigation method of the spatial pattern of landcover and vegetation maps. We applied ARA to two very different study areas in Northern Italy, the first (Ceno valley) having a substantial human footprint and mapped at 1:25,000 scale, the second (Foses valley) being almost natural and mapped at 1:5000 scale.We were able to: (a) detect the entire network of spatial adjacencies among landcover types and (b) quantify the frequency and strength of detected adjacencies. Based on our spatial analysis, we also advanced hypotheses on both natural and man-driven vegetation dynamics. In addition, ARA allowed us to propose an index of naturality based on the discovered contiguities.Results show the skill of the proposed approach to characterize landcover spatial patterns for both mid-resolution and high-resolution maps. Furthermore the proposed approach bears a general interest, since it can be applied to the analysis of any landcover map.