Geology topics

Peter Solymos

Publications and source records attributed to Peter Solymos.

5 recordsLinked to original sources

USGS2020

At the end of the road: Lessons learned from comparing model- and design-based approaches to estimate population sizes of boreal birds in Alberta, Canada

Estimating population abundance is a challenging task complicated by the amount, type, and quality of available data. Conservationists have relied on design-based estimates from Partners in Flight (PIF), which primarily uses roadside data from the North American Breeding Bird Survey (BBS) to estimate populations sizes. However, the BBS was not designed to estimate population sizes. We developed models incorporating land cover and climate variables based on roadside and off-road point-count surveys. We calculated spatially explicit, model-based population estimates for 81 landbird species in Bird Conservation Region 6 in Alberta, Canada, and compared these to PIF estimates. We also developed a framework to evaluate how the differences between the detection distance, time-of-day, roadside count, and habitat representation adjustments explain discrepancies between the two estimators. We showed that the key assumptions of the PIF population size estimator were commonly violated in this region, and the two approaches provided very different population size estimates for most species. The average differences between estimators were explained by differences in the detection distance and time-of-day components, but these adjustments left much unexplained variation among species. Differences in the roadside count and habitat representation components explained most of the among-species variation. The variation caused by these factors was large enough to change the population size ranking of the species. The roadside count bias needs serious attention when roadside surveys are used to extrapolate over off-road areas. Habitat representation bias is likely prevalent in regions sparsely and non-representatively sampled by roadside surveys, such as the boreal region of North America, and thus population size estimates for these regions need to be treated with caution for certain species. Model-based integration of available data sources and additional sampling can contribute towards more accurate population size estimates for conservation in remote areas of North America.

Alberta↗

USGS2019

Monitoring boreal avian populations: How can we estimate trends and trajectories from noisy data?

Substantial effort has been dedicated to developing reliable monitoring schemes for North American bird populations, but our ability to monitor bird populations in the boreal forest remains limited because of the sparsity of long-term data sets, particularly in northerly regions. Given the importance of the boreal forest for many migratory birds, we set out to (1) summarize the main challenges associated with monitoring avian populations, (2) describe the available statistical tools for population monitoring and their applications, and (3) identify future directions to overcome current challenges in monitoring bird populations in the boreal forest. Defining and delineating populations of interest and identifying the drivers that affect those populations present the greatest current challenges. This is because migratory birds may be affected by many population-limiting processes at different stages of their annual life cycles. These factors are often hierarchically structured and can influence populations at the local, regional, or continental scales. Some of the challenges associated with delineating populations and identifying population drivers can be addressed via the plethora of sampling and analytic methods available to examine population change over time. Choosing the proper analytic methods depends on the goals of the study and the nature of the data such as single or multiple populations, repeated occurrence or count-based surveys, or demographic rates. Recent advances in hierarchical and integrated population models make these analytic approaches some of the most promising avenues for the development of future methods. However, these tools require large data sets, and acquiring sufficient data on bird populations and potential explanatory variables is difficult in the boreal forest. If the current challenges to monitoring birds in the boreal forest are to be overcome, serious effort should be dedicated to integrating existing data and making them accessible. Enhancing survey effort through multispecies surveys will also play an important role. Implementing spatially balanced sampling plans with a rotating panel design could balance the trade-offs between spatial versus temporal replication at an affordable cost. Improving the accessibility of environmental covariates that are spatially and temporally explicit would also enable development of mechanistic population models that improve our understanding of migratory bird population dynamics. Finally, given that long-term monitoring programs can take many decades before delivering reliable population trends and that organizational priorities often change over time, we suggest that collaborative efforts will help ensure the long-term survival of new monitoring programs.

Avian Conservation and Ecology↗

USGS2018

Evaluating time-removal models for estimating availability of boreal birds during point count surveys: Sample size requirements and model complexity

We used conventional and finite mixture removal models with and without time-varying covariates to evaluate availability given presence for 152 bird species using data from point counts in boreal North America. We found that the choice of model had an impact on the estimability of unknown model parameters and affected the bias and variance of corrected counts. Finite mixture models provided better fit than conventional removal models and better adjusted for count duration. However, reliably estimating parameters and minimizing variance using mixture models required at least 200–1,000 detections. Mixture models with time-varying proportions of infrequent singers were best supported across species, indicating that accounting for date- and time-related heterogeneity is important when combining data across studies over large spatial scales, multiple sampling time frames, or variable survey protocols. Our flexible and continuous time-removal modeling framework can be used to account for such heterogeneity through the incorporation of easily obtainable covariates, such as methods, date, time, and location. Accounting for availability bias in bird surveys allows for better integration of disparate studies at large spatial scales and better adjustment of local, regional, and continental population size estimates.

Condor↗

USGS2018

Phylogeny and species traits predict bird detectability

Avian acoustic communication has resulted from evolutionary pressures and ecological constraints. We therefore expect that auditory detectability in birds might be predictable by species traits and phylogenetic relatedness. We evaluated the relationship between phylogeny, species traits, and field‐based estimates of the two processes that determine species detectability (singing rate and detection distance) for 141 bird species breeding in boreal North America. We used phylogenetic mixed models and cross‐validation to compare the relative merits of using trait data only, phylogeny only, or the combination of both to predict detectability. We found a strong phylogenetic signal in both singing rates and detection distances; however the strength of phylogenetic effects was less than expected under Brownian motion evolution. The evolution of behavioural traits that determine singing rates was found to be more labile, leaving more room for species to evolve independently, whereas detection distance was mostly determined by anatomy (i.e. body size) and thus the laws of physics. Our findings can help in disentangling how complex ecological and evolutionary mechanisms have shaped different aspects of detectability in boreal birds. Such information can greatly inform single‐ and multi‐species models but more work is required to better understand how to best correct possible biases in phylogenetic diversity and other community metrics.

Ecography↗

USGS2014

Reviving common standards in point-count surveys for broad inference across studies

We revisit the common standards recommended by Ralph et al. (1993, 1995a) for conducting point-count surveys to assess the relative abundance of landbirds breeding in North America. The standards originated from discussions among ornithologists in 1991 and were developed so that point-count survey data could be broadly compared and jointly analyzed by national data centers with the goals of monitoring populations and managing habitat. Twenty years later, we revisit these standards because (1) they have not been universally followed and (2) new methods allow estimation of absolute abundance from point counts, but these methods generally require data beyond the original standards to account for imperfect detection. Lack of standardization and the complications it introduces for analysis become apparent from aggregated data. For example, only 3% of 196,000 point counts conducted during the period 1992-2011 across Alaska and Canada followed the standards recommended for the count period and count radius. Ten-minute, unlimited-count-radius surveys increased the number of birds detected by >300% over 3-minute, 50-m-radius surveys. This effect size, which could be eliminated by standardized sampling, was ≥10 times the published effect sizes of observers, time of day, and date of the surveys. We suggest that the recommendations by Ralph et al. (1995a) continue to form the common standards when conducting point counts. This protocol is inexpensive and easy to follow but still allows the surveys to be adjusted for detection probabilities. Investigators might optionally collect additional information so that they can analyze their data with more flexible forms of removal and time-of-detection models, distance sampling, multiple-observer methods, repeated counts, or combinations of these methods. Maintaining the common standards as a base protocol, even as these study-specific modifications are added, will maximize the value of point-count data, allowing compilation and analysis by regional and national data centers.

Condor↗