Combined Influences of Model Choice, Data Quality, and Data Quantity When Estimating Population Trends

The influence of COVID-19 on attention-deficit/hyperactivity disorder diagnosis and treatment rates across age, gender, and socioeconomic status: A 20-year national cohort study

Infection and lockdowns resulting from COVID-19 have been suggested to increase the prevalence and treatment rates of Attention Deficit/Hyperactivity Disorder (ADHD). To accurately estimate the pandemic's effects, pre-pandemic data can be used to estimate diagnosis and treatment rates during the COVID-19 years as if the COVID-19 pandemic did not occur. However, accurate predictions require a broad dataset, both in terms of the number of cases and the pre-pandemic timeframe. In the current study, we modeled monthly ADHD diagnosis and treatment rates over the 18 years preceding the COVID-19 pandemic. The dataset included ∼3 million cases for individuals aged 6 to 18 from the Clalit Health Services' electronic database. Using a trained model, we projected monthly rates for post-lockdown and post-infection periods, enabling us to estimate the expected diagnosis and treatment rates without the COVID-19 pandemic. We then compared these predictions to observed data, stratified by age groups, gender, and socioeconomic status. Our findings suggest no influence of the COVID-19 pandemic on ADHD diagnosis or treatment rates. We show that a narrower timeframe for pre-COVID-19 data points can lead to incorrect conclusions that COVID-19 affected ADHD diagnosis rates. Findings are discussed, given the assumed impact of the COVID-19 pandemic on ADHD.

Lost reproductive value reveals a high burden of juvenile road mortality in a long-lived species

Adult mortality is often the most sensitive vital rate affecting at-risk wildlife populations. Therefore, road ecology studies often focus on adult mortality despite the possibility for roads to be hazardous to juvenile individuals during natal dispersal. Failure to quantify concurrent variation in mortality risk and population sensitivity across demographic states can mislead the efforts to understand and mitigate the effects of population threats. To compare relative population impacts from road mortality among demographic classes, we weighted mortality observations by applying reproductive value analysis to quantify expected stage-specific contributions to population growth. We demonstrate this approach for snapping turtles (Chelydra serpentina) observed on roads at two focal sites in Ontario, Canada, where we collected data for both live and dead individuals observed on roads. We estimated reproductive values using stage-classified matrix models to compare relative population-level impacts of adult and juvenile mortality. Reproductive value analysis is a tractable approach to assessing demographically variable effects for applications covering large spatial scales, nondiscrete populations, or where abundance data are lacking. For one site with long-term life-history data, we compared demographic frequency on roads to expected general population frequencies predicted by the matrix model. Our application of reproductive value is sex specific but, as juvenile snapping turtles lack external secondary sex characters, we estimated the sex ratio of road-crossing juveniles after dissecting and sexing carcasses collected on roads at five sites across central Ontario, Canada. Juveniles were more abundant on roads than expected, suggesting a substantial dispersal contribution, and the road-killed juvenile sex ratio approached 1:1. A higher proportion of juveniles were also found dead compared with adults, and cumulative juvenile mortality had similar population-level importance as adult mortality. This suggests that the impact of roads needs to be considered across all life stages, even in wildlife species with slow life histories, such as snapping turtles, that are particularly sensitive to adult mortality.

Logistic-growth models measuring density feedback are sensitive to population declines, but not fluctuating carrying capacity

Analysis of long-term trends in abundance of animal populations provides insights into population dynamics. Population growth rates are the emergent interplay of inter alia fertility, survival, and dispersal. However, the density feedbacks operating on some vital rates ("component feedback") can be decoupled from density feedbacks on population growth rates estimated using abundance time series ("ensemble feedback"). Many of the mechanisms responsible for this decoupling are poorly understood, thereby questioning the validity of using logistic-growth models versus vital rates to infer long-term population trends. To examine which conditions lead to decoupling, we simulated age-structured populations of long-lived vertebrates experiencing component density feedbacks on survival. We then quantified how imposed stochasticity in survival rates, density-independent mortality (catastrophes, harvest-like removal of individuals) and variation in carrying capacity modified the ensemble feedback in abundance time series simulated from age-structured populations. The statistical detection of ensemble density feedback from census data was largely unaffected by density-independent processes. Long-term population decline caused from density-independent mortality was the main mechanism decoupling the strength of component versus ensemble density feedbacks. Our study supports the use of simple logistic-growth models to capture long-term population trends, mediated by changes in population abundance, when survival rates are stochastic, carrying capacity fluctuates, and populations experience moderate catastrophic mortality over time.

The impact of cumulative stressor effects on uncertainty and ecological risk

To enable environmental management actions to be more effectively prioritized, cumulative effects between multiple stressors need to be accounted for in risk-assessment frameworks. Ecological risk and uncertainty are generally high when multiple stressors occur. In the face of high uncertainty, transparent communication is essential to inform decision-making. The impact of stressor interactions on risk and uncertainty was assessed using generalized linear models for additive and multiplicative effect of six anthropogenic stressors on the abundance of estuarine macrofauna across New Zealand. Models that accounted for multiplicative stressor interactions demonstrated that non-additive effects dominated, had increased explanatory power (6 to 73 % relative increase between models), and thereby reduced the risk of unexpected ecological responses to stress. Secondly, 3D-plots provide important insights in the direction, magnitude and gradients of change, and aid transparency and communication of complex stressor effects. Notably, small changes in a stressor can cause a disproportionally steep gradient of change for a synergistic effect where the tolerance to stressors are lost, and would invoke precautionary management. 3D-plots were able to clearly identify directional shifts where the nature of the interaction changed from antagonistic to synergistic along increasing stressor gradients. For example, increased nitrogen load and exposure caused a shift from positive to negative effect on the abundance of a deposit-feeding polychaete (Magelona). Assessments relying on model coefficient estimates, which provide one effect term, could not capture the complexities observed in 3D-plots and are at risk of mis-identifying interaction types. Finally, visualising model uncertainty demonstrated that although error terms were higher for multiplicative models, they better captured the uncertainty caused by data availability. Together, the steep gradients of change identified in 3D-plots and the higher uncertainty in model predictions in multiplicative models urges more conservative limits to be set for management that account for risk and uncertainty in multiple stressor effects.

Reliability of animal counts and implications for the interpretation of trends

Population time series analysis is an integral part of conservation biology in the current context of global changes. To quantify changes in population size, wildlife counts only provide estimates because of various sources of error. When unaccounted for, such errors can obscure important ecological patterns and reduce confidence in the derived trend. In the case of highly gregarious species, which are common in the animal kingdom, the estimation of group size is an important potential bias, which is characterized by high variance among observers. In this context, it is crucial to quantify the impact of observer changes, inherent to population monitoring, on i) the minimum length of population time series required to detect significant trends and ii) the accuracy (bias and precision) of the trend estimate.We acquired group size estimation error data by an experimental protocol where 24 experienced observers conducted counting simulation tests on group sizes. We used this empirical data to simulate observations over 25 years of a declining population distributed over 100 sites. Five scenarios of changes in observer identity over time and sites were tested for each of three simulated trends (true population size evolving according to deterministic models parameterized with declines of 1.1%, 3.9% or 7.4% per year that justify respectively a "declining," "vulnerable" or "endangered" population under IUCN criteria).We found that under realistic field conditions observers detected the accurate value of the population trend in only 1.3% of the cases. Our results also show that trend estimates are similar if many observers are spatially distributed among the different sites, or if one single observer counts all sites. However, successive changes in observer identity over time lead to a clear decrease in the ability to reliably estimate a given population trend, and an increase in the number of years of monitoring required to adequately detect the trend.Minimizing temporal changes of observers improve the quality of count data and help taking appropriate management decisions and setting conservation priorities. The same occurs when increasing the number of observers spread over 100 sites. If the population surveyed is composed of few sites, then it is preferable to perform the survey by one observer. In this context, it is important to reconsider how we use estimated population trend values and potentially to scale our decisions according to the direction and duration of estimated trends, instead of setting too precise threshold values before action.

Guidance for Developing Amphibian Population Models for Ecological Risk Assessment

Despite widespread acceptance of the utility of population modeling and advocacy of this approach for a more ecologically relevant perspective, it is not routinely incorporated in ecological risk assessments (ERA). A systematic framework for situation-specific model development is one of the major challenges to broadly adopting population models in ERA. As risk assessors confront the multitude of species and chemicals requiring evaluation, an adaptable stepwise guide for model parameterization would facilitate this process. Additional guidance on interpretation of model output and evaluating uncertainty would further contribute to establishing consensus on good modeling practices. We build on previous work that created a framework and decision guide for developing population models for ERA by focusing on data types, model structure, and extrinsic stressors relevant to anuran amphibians. Anurans have a unique life cycle with varying habitat requirements and high phenotypic plasticity. These species belong to the amphibian class, which is facing global population decline in large part due to anthropogenic stressors, including chemicals. We synthesize information from databases and literature relevant to amphibian risks to identify traits that influence exposure likelihood, inherent sensitivity, population vulnerability, and environmental constraints. We link these concerns with relevant population modeling methods and structure in order to evaluate pesticide effects with appropriate scale and parameterization. A standardized population modeling approach, with additional guidance for anuran ERA, offers an example method for quantifying population risks and evaluating long-term impacts of chemical stressors to populations. Integr Environ Assess Manag 2020;16:223-233. © 2019 SETAC.

A field ecologist's adventures in the virtual world: using simulations to design data collection for complex models

Field data collection can be expensive, time consuming, and difficult; insightful research requires statistical analyses supported by sufficient data. Pilot studies and power analysis provide guidance on sampling design but can be challenging to perform, as ecologists increasingly collect multiple types of data over different scales. Despite a growing simulation literature, it remains unclear how to appropriately design data collection for many complex projects. Approaches that seek to achieve realism in decision-making contexts, such as management strategy evaluation and virtual ecologist simulations, can help. For a relatively complex analysis, we develop and demonstrate a flexible simulation approach that informs what data are needed and how long those data will take to collect, under realistic fieldwork constraints. We simulated data collection and analysis under different constraint scenarios that varied in deterministic (field trip length, travel, and measurement times) and stochastic (species detection and occupancy rates and inclement weather) features. In our case study, we fit plant height data to a multispecies, three-parameter, nonlinear growth model. We tested how the simulated data sets, based on the varying constraint scenarios, affected the model fit (parameter bias, uncertainty, and capture rate). Species prevalence in the field exerted a stronger influence on the data sets and downstream model performance than deterministic aspects such as travel times. When species detection and occupancy were not considered, the field time needed to collect an adequate data set was underestimated by 40%. Simulations can assist in refining fieldwork design, estimating field costs, and incorporating uncertainties into project planning. We argue that combining data collection, analysis, and decision-making processes in a flexible virtual setting can help address many of the decisions that field ecologists face when designing field-based research.

Implications of different population model structures for management of threatened plants

Population viability analysis (PVA) is a reliable tool for ranking management options for a range of species despite parameter uncertainty. No one has yet investigated whether this holds true for model uncertainty for species with complex life histories and for responses to multiple threats. We tested whether a range of model structures yielded similar rankings of management and threat scenarios for 2 plant species with complex postfire responses. We examined 2 contrasting species from different plant functional types: an obligate seeding shrub and a facultative resprouting shrub. We exposed each to altered fire regimes and an additional, species-specific threat. Long-term demographic data sets were used to construct an individual-based model (IBM), a complex stage-based model, and a simple matrix model that subsumes all life stages into 2 or 3 stages. Agreement across models was good under some scenarios and poor under others. Results from the simple and complex matrix models were more similar to each other than to the IBM. Results were robust across models when dominant threats are considered but were less so for smaller effects. Robustness also broke down as the scenarios deviated from baseline conditions, likely the result of a number of factors related to the complexity of the species' life history and how it was represented in a model. Although PVA can be an invaluable tool for integrating data and understanding species' responses to threats and management strategies, this is best achieved in the context of decision support for adaptive management alongside multiple lines of evidence and expert critique of model construction and output.

Population assessment using multivariate time-series analysis: A case study of rockfishes in Puget Sound

Estimating a population's growth rate and year-to-year variance is a key component of population viability analysis (PVA). However, standard PVA methods require time series of counts obtained using consistent survey methods over many years. In addition, it can be difficult to separate observation and process variance, which is critical for PVA. Time-series analysis performed with multivariate autoregressive state-space (MARSS) models is a flexible statistical framework that allows one to address many of these limitations. MARSS models allow one to combine surveys with different gears and across different sites for estimation of PVA parameters, and to implement replication, which reduces the variance-separation problem and maximizes informational input for mean trend estimation. Even data that are fragmented with unknown error levels can be accommodated. We present a practical case study that illustrates MARSS analysis steps: data choice, model set-up, model selection, and parameter estimation. Our case study is an analysis of the long-term trends of rockfish in Puget Sound, Washington, based on citizen science scuba surveys, a fishery-independent trawl survey, and recreational fishery surveys affected by bag-limit reductions. The best-supported models indicated that the recreational and trawl surveys tracked different, temporally independent assemblages that declined at similar rates (an average of -3.8% to -3.9% per year). The scuba survey tracked a separate increasing and temporally independent assemblage (an average of 4.1% per year). Three rockfishes (bocaccio, canary, and yelloweye) are listed in Puget Sound under the US Endangered Species Act (ESA). These species are associated with deep water, which the recreational and trawl surveys sample better than the scuba survey. All three ESA-listed rockfishes declined as a proportion of recreational catch between the 1970s and 2010s, suggesting that they experienced similar or more severe reductions in abundance than the 3.8-3.9% per year declines that were estimated for rockfish populations sampled by the recreational and trawl surveys.

Population Abundance of the Endangered Galapagos Sea Lion Zalophus wollebaeki in the Southeastern Galapagos Archipelago

There is great concern regarding the population status of the endangered Galapagos sea lion (GSL) because it has drastically decreased over the last 30 years. We determined the population size and growth trend of the GSL in the Galapagos southeastern region (SER) at three population levels based on the available census data: 1) SER (2011-2015), including 13 rookeries on the four islands San Cristóbal (SC), Española, Floreana, and Santa Fe, comprising 58% of the archipelago's population; 2) SC (2011-2015), including five rookeries, comprising 52% of the SER population; and 3) El Malecón (2005-2015), the largest rookery on SC and in the SER (43% of the population on SC and 22% in the SER). We also analyzed the influence of environmental variability on pup abundance in these rookeries. The current GSL population size in the SER, after applying correction factors to the counts, is estimated at approximately 2300-4100 individuals and has declined at an average annual rate (ʎ) of 8.7% over the last five years. A similar trend was determined for SC but at ʎ = 1.4% during the same period. For El Malecón, a count-based population viability analysis using a diffusion approximation approach showed that the population increased from 2005 to 2015 at ʎ = 2%. The interannual variability in pup abundance was associated with anomalies in sea surface temperature linked to oceanographic-atmospheric events, which impact the abundance and availability of prey, and ultimately may determine the population's reproductive success. Since rookeries in the SER had different population trends, management actions should be implemented based on their specific conditions, giving priority to rookeries such as El Malecón, which, despite showing a slightly increasing population trend, still faces a high risk of extinction due to anthropogenic disturbances and environmental variability that may affect its growth and survival.