Long-term Trends in the Number of Monarch Butterflies (Lepidoptera: Nymphalidae) Counted on Fall Migration at Long Point, Ontario, Canada (1995–2014)

Crewe, Tara L.; McCracken, Jon D.

doi:10.1093/aesa/sav041

Cited by 21 publications

(26 citation statements)

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“…Our simulations were modeled to represent counts of raptors migrating past a count site. However, our results are applicable to any sampling protocol that collects counts of migrating animals on an hourly or daily basis (Crewe et al 2008, Crewe andMcCracken 2015), and given comparable count distribution. We recommend how count sampling protocols might be used to optimize sampling effort and statistical power, while minimizing the probability of drawing false inference about population trends.…”

Section: Introductionmentioning

confidence: 55%

“…Alternatively, given the high variability among hourly and daily counts often observed for migrants detected in large aggregations or flocks like the Broad-winged Hawk or monarch butterfly (Crewe and McCracken 2015), aggregating counts into effort-corrected annual totals could improve model fit, and thus increase power and lower the probability of drawing false inference from the data , Crewe 2015. Incorporating weather or habitat covariates in trend analyses might also improve the precision of annual population indices Hussell 1998, Farmer et al 2007), and in the case that a long-term systematic change in environmental covariates has occurred, their inclusion could model any associated bias in trend (but see Crewe et al 2015a).…”

Section: Discussionmentioning

confidence: 99%

“…For species that are either secretive during breeding or breed and winter in remote areas, data from breeding and wintering surveys are often unavailable or provide insufficient sampling coverage to assess population trends (Dunn 2005, Blancher et al 2009, Sauer and Link 2011. Monitoring species as they migrate between wintering and breeding grounds can be used as an additional source of information on population trend for these species (Downes et al 2000, Bildstein et al 2008, Blancher et al 2009, Findlay et al 2011, Crewe and McCracken 2015.…”

Section: Introductionmentioning

confidence: 99%

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The Condor

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Section: Introductionmentioning

confidence: 55%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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2016

The Condor

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“…Because the number of raptors and songbirds migrating and detected by counts can be largely influenced by weather (Hall et al 1992, Allen et al 1996, Barriocanal et al 2002, Berthiaume et al 2009) and other factors including observer differences or local habitat conditions (Harrison et al 2000), modeling environmental or other relevant covariates to account for some of the observed variation in counts (Francis and Hussell 1998) might also improve precision, and therefore our ability to detect trends. The effect of including covariates for weather or other factors on model fit using hourly or daily count data should be explored, particularly for overdispersed species, which may then benefit from being analyzed as daily as opposed to annual totals (e.g., Crewe and McCracken 2015).…”

Section: Discussionmentioning

confidence: 99%

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

Crewe¹,

Taylor²,

Lepage³

2016

ACE

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ABSTRACT. 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.Le regroupement à différentes échelles temporelles de dénombrements d'oiseaux en migration peut améliorer l'exactitude et la précision des tendances RÉSUMÉ. Le regroupement des dénombrements répétés dans le temps est une technique souvent utilisée pour réduire la surreprésentation de zéros et la variabilité des dénombrements, et ultimement améliorer l'ajustement des modèles. Dans les cas de dénombrements d'oiseaux en migration faits à chaque heure durant la période de migration, ce regroupement des données permet aussi d'ignorer la non-indépendance. Toutefois, ce regroupement peut mener à une perte d'informations potentiellement utiles sur la distribution horaire ou saisonnière des dénombrements, pouvant en retour affecter notre capacité à obtenir des estimations fiables des tendances. Nous avons simulé des données de dénombrements de rapaces en migration réalisés à chaque heure pendant 20 ans et dont le taux de changement était connu afin de tester l'effet du regroupement des dénombrements faits à l'heure en totaux quotidiens ou annuels sur notre capacité de détecter la tendance connue. Nous avons simulé des don...

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“…This has the potential to result in region‐ and species‐specific patterns of population change. Partially as a result of these uncertainties, population trends estimated using migration counts in North America have been site‐specific to date (Bildstein et al , Crewe et al , Crewe and McCracken ).…”

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confidence: 99%

Quantifying regional variation in population trends using migrating counts

Crewe¹,

Taylor²,

Lepage

et al. 2015

J Wildl Manag

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Quantification of regional or national variation in population trends is an integral component of assessing species conservation status, and ideally uses spatial and temporal replicate surveys across the breeding or wintering ranges. However, populations of boreal-breeding birds are often monitored using sitespecific trends in the number of individuals counted migrating through more populated regions en route to or from their breeding grounds. Combining data across sites to quantify regional variation in a population trend has not occurred, and would rely on the success of model selection to choose the model that best describes the underlying pattern of regional population change. Using simulated daily counts of migrating birds with known regional rate(s) of population trend, we tested the utility of Akaike's Information Criterion (AIC) for detecting regional variation in population trend by comparing a series of hierarchical models that estimated nationally or regionally varying trends. When trends varied regionally, AIC successfully ranked the model that best described the regional pattern of population trend as the top model 100% of the time using 40-year time series, and with as few as 3 monitoring sites in each region. When trends did not vary among regions, the more highly parameterized (incorrect) model with regionally varying trends was ranked as the top model 20% of the time, regardless of the length of time series or number of sites in a region. In this case, the incorrect regional model resulted in less precise trends but also comparable or higher power and comparable or lower probability of drawing false inference from the data than did the correct model. Compared with sampling 10 sites/region over a 20-year time period, sampling as few as 3 sites/region over a 40-year time period can result in a lower probability of detecting a false trend. Promoting long-term monitoring at fewer sites can also minimize the financial and logistical resources required to maintain migration monitoring programs, and allow more recent population change to be interpreted within the context of long-term population fluctuations. Ó 2015 The Wildlife Society.

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Long-term Trends in the Number of Monarch Butterflies (Lepidoptera: Nymphalidae) Counted on Fall Migration at Long Point, Ontario, Canada (1995–2014)

Cited by 21 publications

References 25 publications

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

Quantifying regional variation in population trends using migrating counts

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