Canadian Migration Monitoring Network trends are displayed only for species at each site that meet the following criteria:

- The species is a regular migrant with minimal stopover (i.e. excluding partial, irruptive and non-migrants, and roosting or staging species).
- Standardized count protocols are followed consistently through time.
- At least 75% of the species’ migratory period is well-sampled in 2/3 or more of all years.
- Relatively few individuals are detected before or after clearly recognizable migratory influxes, such that trends are minimally affected by wintering or locally breeding species recorded regularly outside the migratory period.
- The species is regularly observed within and between seasons (averaging 10+ detections/season and detection on 5+ days/season).

**M = regular migrant**, 75+% of season covered by station in 2/3 or more of all years. Analysis window limited to migratory surge (see footnote), and few individuals are present before or after. Species is well-monitored at the site. Trends are of good quality, and represent change in population size for a large area of the station's catchment area.**ML = Similar to M**, except that numbers before/after migratory peak (i.e. local area birds) amount to more than a quarter of peak numbers during the migratory surge. Although the analysis window is limited to the period of migratory surge, the resulting trend is affected to an unquantifiable degree by local population level (which may or may not agree with trend for birds migrating through the site). Note that “local area” birds are those regularly observed at a station, though they may not breed or winter right at the site.**I = Irruptive and irregular migrants**: species without a regular pattern of migratory movement. Analysis window covers the period in which there is at least occasional movement, but much of movement may fall outside of station coverage. Annual indices represent annual variation in extent and timing of irruption. Over the long term population trends may be discernable despite the high degree of annual variation, but should be interpreted case by case.**L = Birds from local area**(not necessarily breeding or wintering at count site per se, but regularly observed there), lacking seasonal patterns suggesting through movement. Trends are assumed to reflect population change in local area, which may or may not agree with trends at larger scales.**S = Staging species**(usually waterfowl and shorebirds) with pattern of migratory surge of which 75% or more is covered by the station. Some ‘S’ species would be ‘M’ if it were known that movement is unidirectional and there is minimal stopover. In most cases, however, trends may represent site use rather than population change, as there may be annual variation in use of the specialized stopover sites in the region, only one of which is being monitored. Trends should be interpreted case by case.**MX = Regular migrant**, but coverage at site falls short of documenting 75% of migration period. Shifts in timing of migration are not detectable from the data (e.g. change in timing due to climate change could produce a trend that is independent of true population size.) This is a subclass of "O" species, but designated separately to draw attention to species that could become ‘M’ with more extended coverage.**O = Other**, for species that do not fit into any of the above groups. Individuals may represent one or a combination of categories: casual visitation, premigratory dispersal, departing winter populations, or others. Trends very unlikely to represent population change either at a broad scale or in a definable local population.

Analysis for each species is limited to the dates the species is moving through the site. Long-term average daily counts were plotted against date, and the analysis “window,” incorporating approximately 95% of the migratory movement, was selected as the dates between which numbers clearly begin to increase and when they return to a relatively steady level following the migratory surge. The same procedure was used for species whose entire migration was not fully covered (i.e., the window omits dates when the species is absent or at low basal levels). For all species with no clear pattern of movement into and/or out of the area (such as resident species, many “O” species, and irruptive species with irregular timing), the window was defined as the entire seasonal coverage period for that site.

No results are produced for species that do not meet a minimum level of abundance or frequency during the site’s seasonal coverage (average of 10+ detections and detected on 5+ days).

Trends for species meeting these criteria can be interpreted as representing change in population size within a large area of the stations' catchment area (the portion of breeding range sampled by that station).

Trend maps show the estimated population trend over the most recent ten-year period for sites that meet the criteria for that species.

Seasonal abundance graphs are plots of information showing phenology and abundance of all species regularly sampled at the selected location.

Migration windows show the boundaries of the spring and fall migration window used in analyses (only for species with trends displayed on website). The bounds of spring and fall migration windows were restricted to those days of the year when the station operated during at least two-thirds of total years in operation, and are in some cases restricted further (e.g. to omit likely summer residents from analysis).

• daily mean log(species count)— Percent of years species present each day

— Percent of years station in operation each day

| Spring and/or fall migration window boundaries

Long-term trends in count were estimated independently for each species, site and season using a Bayesian framework with Integrated Nested Laplace Approximation (R-INLA, Rue et al. 2014) in R (version 3.1.3; R Core Team 2014). We estimated trends using log-linear regression, which included 1) a continuous effect for year (i) to estimate log-linear change in population size over time, 2) first and second order effects for day of year (j) to model the seasonal distribution of counts, and 3) hierarchical terms to account for random variation in counts among years and among days. Number of observation days each year was included as an offset to account for variation in daily effort:

log(µ_ij )=[a+ß]_1×[year]_ij+ß_2×[day]_ij+ß_3×[day]_ij^2+γ_i+η_j,

where γ_i is a first-order autoregressive (AR1) random effect for year to account for temporal autocorrelation among years, and η_jis an independent and identically distributed (IID) hierarchical term to account for random variation in counts among days of the year. For monitoring stations with more than one site (e.g., Long Point Bird Observatory), the regression also included a fixed site effect, as well as interactions between site and the first and second-order day of year effects. While we recognize that an AR1 random effect for day of year nested within year might have been more appropriate to account for temporal autocorrelation among daily counts, we found that specifying the random day effect as IID had no noticeable effect on trend bias or on probability of estimating a precise trend (probability that the simulated trend fell within confidence limits of the estimated trend; T. L. Crewe, unpublished data). Specifying the random day effect as IID did, however, significantly increase the speed of analysis, and reduced the probability of errors using INLA.

We assumed a Poisson distribution of counts, unless the proportion of 0-observation days across years was >= 0.65. This cut-off is somewhat arbitrary, and should be examined in greater detail, but see Crewe et al. 2016. For both data distributions, year estimates and 95% credible intervals were back-transformed to annual rates of population change using 100*exp(estimate)-1. Trends were calculated using the full dataset, as well as for all 10-year subsets to estimate 10-, 20-, 30-year (etc., where appropriate) trends for comparison among years over time. Trends are presented as %/year with lower and upper 95% credible intervals, which suggest that there is a 95% probability that the true trend falls within that range. A posterior distribution was also calculated to estimate the support for an increasing or declining trend. A value near 0.5 would suggest equal probability for an increasing and declining trend (little evidence for a change in migration counts over time), whereas a posterior probability near 1 will suggest strong support for the observed change in counts. The posterior probability can be used as a pseudo p-value, such that trends with a posterior probability > 0.9 could be considered to have strong support. Annual indices of population size were estimated as the mean daily count from the posterior distribution of the above model. Plots of annual indices show 95% credible intervals (vertical lines), and the black line and grey shading display a loess fit across indices and upper and lower credible intervals.

Crewe, T. L., P. D. Taylor, and D. Lepage. 2016. Temporal aggregation of migration counts can improve accuracy and precision of trends. Avian Conservation and Ecology 11(2): 8. <http://dx.doi.org/10.5751/ACE-00907-110208>

R Core Team. 2014. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. [online] URL: http://www.r-project.org.

Rue, H., S. Martino, F. Lindgren, D. Simpson, and A. Riebler. 2014. INLA: Functions which allow to perform full Bayesian analysis of latent Gaussian models using Integrated Nested Laplace Approximation. [online] URL: http://www.r-inla.org.