Improving the Use of Migration Counts for Wildlife Population Monitoring

Counts of migrating animals are used to monitor populations, particularly for species that are not well sampled by breeding and wintering surveys. The use of migration counts for population monitoring relies on the assumptions that new individuals are detected each day, and that probability of detecting those individuals remains constant over time. The impact of violating these assumptions on our ability to estimate reliable population trends is not well understood. Further, on a broad spatial scale, our ability to combine data across sites to estimate regional or national trends has been limited by the possibility that trends vary regionally in an unknown way. Using simulated migration count data with known trend, I tested whether sampling effort (daily vs. non-daily sampling) and a temporal change in stopover duration (and thus detection probability) influenced our ability to estimate the known trend. I also tested whether analyzing data as hourly, daily or annual counts, or accounting for random error using analytical techniques, could improve accuracy and precision of estimated trends by reducing or better modeling variation in counts, respectively. Further, using model selection analytical techniques, I tested whether we could detect when trends vary regionally using current or increased number of sampling sites in a region. My findings show that trends can be improved for species with highly variable daily counts by sampling less frequently than daily or by aggregating hourly counts to annual totals. Commonly and rarely detected species were better analyzed as daily counts, collected daily throughout the migration. A linear increase in stopover duration over time biased trends and lead to a high probability of detecting an incorrect trend, which is only improved by both reducing sampling effort and including a covariate for stopover duration in regression analyses. Regional variation in trends can be detected, and increasing the length of the time series was more efficient for improving accuracy and precision of regional trends than increasing the number of sites sampled. Continued advancement of our knowledge of breeding origins and stopover duration of migrants are priorities for the further refinement of trends estimated using migration counts

Phenology of the avian spring migratory passage in Europe and North America : Asymmetric advancement in time and increase in duration

Climate change has been shown to shift the seasonal timing (i.e. phenology) and distribution of species. The phenological effects of climate change on living organisms have often been tested using first occurrence dates, which may be uninformative and biased. More rarely investigated is how different phases of a phenological sequence (e.g. beginning, central tendency and end) or its duration have changed over time. This type of analysis requires continuous observation throughout the phenological event over multiple years, and such data sets are rare. In this study we examined the impact of temperature on long-term change of passage timing and duration of the spring migration period in birds, and which species' traits explain species-specific variation. Data used covered 195 species from 21 European and Canadian bird observatories from which systematic daily sampling protocols were available. Migration dates were negatively associated with early spring temperature and timings had in general advanced in 57 years. Short-distance migrants advanced the beginning of their migration more than long-distance migrants when corrected for phylogenic relatedness, but such a difference was not found in other phases of migration. The advancement of migration has generally been greater for the beginning and median phases of migration relative to the end, leading to extended spring migration seasons. Duration of the migration season increased with increasing temperature. Phenological changes have also been less noticeable in Canada even when corrected for rate of change in temperature. To visualize long-term changes in phenology, we constructed the first multi-species spring migration phenology indicator to describe general changes in median migration dates in the northern hemisphere. The indicator showed an average advancement of one week during five decades across the continents (period 1959-2015). The indicator is easy to update with new data and we therefore encourage future research to investigate whether the trend towards longer periods of occurrence or emergence in spring is also evident in other migratory populations. Such phenological changes may influence detectability in monitoring schemes, and may have broader implications on population and community dynamics.Peer reviewe

Temporal aggregation of migration counts can improve accuracy and precision of trends

Temporal replicate counts are often aggregated to improve model fit by reducing zero-inflation and count variability, and in the case of migration counts collected hourly throughout a migration, allows one to ignore nonindependence. However, aggregation can represent a loss of potentially useful information on the hourly or seasonal distribution of counts, which might impact our ability to estimate reliable trends. We simulated 20-year hourly raptor migration count datasets with known rate of change to test the effect of aggregating hourly counts to daily or annual totals on our ability to recover known trend. We simulated data for three types of species, to test whether results varied with species abundance or migration strategy: a commonly detected species, e.g., Northern Harrier, Circus cyaneus; a rarely detected species, e.g., Peregrine Falcon, Falco peregrinus; and a species typically counted in large aggregations with overdispersed counts, e.g., Broad-winged Hawk, Buteo platypterus. We compared accuracy and precision of estimated trends across species and count types (hourly/daily/annual) using hierarchical models that assumed a Poisson, negative binomial (NB) or zero-inflated negative binomial (ZINB) count distribution. We found little benefit of modeling zero-inflation or of modeling the hourly distribution of migration counts. For the rare species, trends analyzed using daily totals and an NB or ZINB data distribution resulted in a higher probability of detecting an accurate and precise trend. In contrast, trends of the common and overdispersed species benefited from aggregation to annual totals, and for the overdispersed species in particular, trends estimating using annual totals were more precise, and resulted in lower probabilities of estimating a trend (1) in the wrong direction, or (2) with credible intervals that excluded the true trend, as compared with hourly and daily counts

Motivations and fears driving participation in collaborative research infrastructure for animal tracking.

Anthropogenic derived environmental change is challenging earth's biodiversity. To implement effective management, it is imperative to understand how organisms are responding over broad spatiotemporal scales. Collection of these data is generally beyond the budget of individual researchers and the integration and sharing of ecological data and associated infrastructure is becoming more common. However, user groups differ in their expectations, standards of performance, and desired outputs from research investment, and accommodating the motivations and fears of potential users from the outset may lead to higher levels of participation. Here we report upon a study of the Australian ornithology community, which was instigated to better understand perceptions around participation in nationally coordinated research infrastructure for detecting and tracking the movement of birds. The community was surveyed through a questionnaire and individuals were asked to score their motivations and fears around participation. Principal Components Analysis was used to reduce the dimensionality of the data and identify groups of questions where respondents behaved similarly. Linear regressions and model selection were then applied to the principal components to determine how career stage, employment role, and years of biotelemetry experience affected the respondent's motivations and fears for participation. The analysis showed that across all sectors (academic, government, NGO) there was strong motivation to participate and belief that national shared biotelemetry infrastructure would facilitate bird management and conservation. However, results did show that a cross-sector cohort of the Australian ornithology community were keen and ready to progress collaborative infrastructure for tracking birds, and measures including data-sharing agreements could increase participation. It also informed that securing initial funding would be a significant challenge, and a better option to proceed may be for independent groups to coordinate through existing database infrastructure to form the foundation from which a national network could grow

Modeling Systematic Change in Stopover Duration Does Not Improve Bias in Trends Estimated from Migration Counts.

The use of counts of unmarked migrating animals to monitor long term population trends assumes independence of daily counts and a constant rate of detection. However, migratory stopovers often last days or weeks, violating the assumption of count independence. Further, a systematic change in stopover duration will result in a change in the probability of detecting individuals once, but also in the probability of detecting individuals on more than one sampling occasion. We tested how variation in stopover duration influenced accuracy and precision of population trends by simulating migration count data with known constant rate of population change and by allowing daily probability of survival (an index of stopover duration) to remain constant, or to vary randomly, cyclically, or increase linearly over time by various levels. Using simulated datasets with a systematic increase in stopover duration, we also tested whether any resulting bias in population trend could be reduced by modeling the underlying source of variation in detection, or by subsampling data to every three or five days to reduce the incidence of recounting. Mean bias in population trend did not differ significantly from zero when stopover duration remained constant or varied randomly over time, but bias and the detection of false trends increased significantly with a systematic increase in stopover duration. Importantly, an increase in stopover duration over time resulted in a compounding effect on counts due to the increased probability of detection and of recounting on subsequent sampling occasions. Under this scenario, bias in population trend could not be modeled using a covariate for stopover duration alone. Rather, to improve inference drawn about long term population change using counts of unmarked migrants, analyses must include a covariate for stopover duration, as well as incorporate sampling modifications (e.g., subsampling) to reduce the probability that individuals will be detected on more than one occasion

Influence of simulated direction of trend in annual counts and magnitude and pattern of change in stopover duration (indexed by daily survival probability <i>phi</i>_<i>i</i>) on bias of estimated trends.

<p>Reference categories for the independent variables in the linear regression model were no trend (Trend = 0%year^-1), random variation in stopover duration, and a range in <i>phi</i>_<i>i</i> values between 0.40–0.50.</p><p>Influence of simulated direction of trend in annual counts and magnitude and pattern of change in stopover duration (indexed by daily survival probability <i>phi</i>_<i>i</i>) on bias of estimated trends.</p

Effect of a covariate for detection and subsampling on bias of population trends.

<p>Bias (%year^-1) in estimated trend in migration counts (estimated—simulated trend), when trend was estimated with and without a covariate for detection, and with and without subsampling to every third or fifth observation day. All datasets were simulated to have a declining population trend of 1.2%year^-1 and a systematic increase in stopover duration (indexed by a linear increase in daily survival probability between 0.40–0.50, 0.35–0.55, 0.30–0.60, 0.25–0.65 or 0.20–0.70) over a 20-year time series. Lines of the boxplots represent the 25^th percentile, median and 75^th percentile of bias estimates across 100 simulated datasets. The horizontal dashed line depicts no bias in estimated trend.</p

Influence of including a covariate for detection and subsampling on bias of estimated trends, when stopover duration (indexed by daily survival probability <i>phi</i>_<i>i</i>) increased systematically over time.

<p>Reference categories for the independent variables in the linear regression model were an increase in <i>phi</i>_<i>i</i> between 0.40–0.50, no subset, and no covariate.</p><p>Influence of including a covariate for detection and subsampling on bias of estimated trends, when stopover duration (indexed by daily survival probability <i>phi</i>_<i>i</i>) increased systematically over time.</p

Bias in estimated population trend when stopover duration was constant or varied temporally.

<p>Bias (%year^-1) in estimated trend in migration counts (estimated—simulated trend), when trend was estimated using datasets simulated to have either an increasing trend (0.96%year^-1), no long term trend (0%year^-1) or a declining trend (-1.2%year^-1) in the count population, and where stopover duration <b>a)</b> remained constant across years (indexed by daily survival probability, <i>phi</i>_<i>i</i> = 0, 0.20, 0.50, or 0.70), or <b>b)</b> varied randomly, cyclically or increased systematically over time by various magnitudes (where <i>phi</i>_<i>i</i> varied between 0.40–0.50, 0.35–0.55, 0.30–0.60, 0.25–0.65 or 0.20–0.70). Lines of the boxplots represent the 25^th percentile, median and 75^th percentile of bias estimates across 100 simulated datasets. The horizontal dashed line depicts no bias in trend.</p