To date, 28 species have been confirmed as breeding within the consus area. This considerable highter than the average breeding bird diversity for other confiferous forest sites at temmperate latitudes. It is likely that both the varied habitat and structural complexity of plant cover within the Catamount census area are responsible for the high bird species diversity reported here. However, one resource in particular - an abundance of mature aspen trees - contributes significantly to the availability of nest sites within the study area. All but two of the cavity-nesting pairs observed to date occupied holes in living aspen trees (both vireo pairs also nested in aspen). One 0.4-hectare aspen stand typically harbors 10-12 active nests, extrapolating to a record density of 27 breeding pairs per hectare. While the potential importance of dead tree snags to wildlife has long been recognized by forest managers, our survey shows that living aspen trees may far outnumber all other tree species - living or dead - in contributing to the breeding success of hole-nesting birds.

Understanding patterns of species diversity is crucial for ecological research and conservation, and this understanding may be improved by studying patterns in the two components of species diversity, species richness and evenness of abundance of species. Variation in species richness and evenness has previously been linked to variation in total abundance of communities as well as productivity gradients. Exploring both components of species diversity is essential because these components could be unrelated or driven by different mechanisms. The aim of this study was to investigate the relationship between species richness and evenness in European bird communities along an extensive latitudinal gradient. We examined their relationships with latitude and Net Primary Productivity, which determines energy and matter availability for heterotrophs, as well as their responses to territory densities (i.e., the number of territories per area) and community biomass (i.e., the bird biomass per area). We applied a multivariate Poisson log-normal distribution to unique long-term, high-quality time-series data, allowing us to estimate species richness of the community as well as the variance of this distribution, which acts as an inverse measure of evenness. Evenness in the distribution of abundance of species in the community was independent of species richness. Species richness increased with increasing community biomass, as well as with increasing density. Since both measures of abundance were explained by NPP, species richness was partially explained by energy-diversity theory (i.e., the more energy, the more species sustained by the ecosystem). However, species richness did not increase linearly with NPP but rather showed a unimodal relationship. Evenness was not explained either by productivity nor by any of the aspects of community abundance. This study highlights the importance of considering both richness and evenness to gain a better understanding of variation in species diversity. We encourage the study of both components of species diversity in future studies, as well as use of simulation studies to verify observed patterns between richness and evenness. Methods The following information is also given in the main manuscript. The individual contributors should be contacted for more information or permissions to work with their respective data sets.

276 of the plots come from the British Trust for Ornithology’s (BTO) Common Bird Census (CBC) and the BTO/Joint Nature Conservation Committee/Royal Society for the Protection of Birds, Breeding Bird Survey (BBS) (Marchant, 1990; Freeman et al., 2007). Two additional plots from the UK were obtained from Williamson (1975) and Gaston and Blackburn (2008). 78 plots from Germany were provided by Bowler and Schwarz (pers. comm.), see Schwarz and Flade (1989) and Kamp et al. (2021). 2 plots from Estonia were provided by Leivits (pers. comm.). 7 plots from Poland were obtained from Tomiałojć and Wesołowski (1996) and Wesołowski et al. (2002). 6 plots from Sweden were obtained from Enemar et al. (2004), Svensson (2006 & 2009). 2 plots from Finland were obtained from Palmgren (1987) and Lehikoinen et al. (2016) 3 plots from Norway were included (own data, as well as Moksnes (1978) and Hogstad (1993)).

References: Enemar, A., Sjöstrand, B., Andersson, G. & Von Proschwitz, T. (2004) The 37-year dynamics of a subalpine passerine bird community, with special emphasis on the influence of environmental temperature and Epirrita autumnata cycles. Ornis Svecica, 14, 63-106. Freeman, S.N., Noble, D.G., Newson, S.E. & Baillie, S.R. (2007) Modelling population changes using data from different surveys: the Common Birds Census and the Breeding Bird Survey. Bird Study, 54, 61-72. Gaston, K. & Blackburn, T. (2008) Pattern and process in macroecology. Hoboken. NJ, John Wiley & Sons. Hogstad, O. (1993) Structure and dynamics of a passerine bird community in a spruce-dominated boreal forest. A 12-year study. Annales Zoologici Fennici, pp. 43-54. JSTOR. Kamp, J., Frank, C., Trautmann, S., Busch, M., Dröschmeister, R., Flade, M., Gerlach, B., Karthäuser, J., Kunz, F. & Mitschke, A. (2021) Population trends of common breeding birds in Germany 1990–2018. Journal of Ornithology, 162, 1-15. Lehikoinen, A., Fraixedas, S., Burgas Riera, D., Eriksson, H., Henttonen, H., Laakkonen, H., Lehikoinen, P., Lehtomäki, J., Leppänen, J. & Mäkeläinen, S. (2016) The impact of weather and the phase of the rodent cycle on breeding populations of waterbirds in Finnish Lapland. Ornis Fennica, 93. Marchant, J.H. (1990) Population trends in British breeding birds. British Trust for Ornithology. Moksnes, A. (1978) Vurdering av ornitologiske verneverdier og skadevirkninger i forbindelse med planene om tilleggsreguleringer i Neavassdraget, Tydal kommune. Palmgren, P. (1987) On the constancy of annually repeated bird censuses. Ornis Fennica, 64, 85-89. Schwarz, J. & Flade, M. (1989) Ergebnisse des DDA-Monitoringprogramms. Teil I: Bestandsänderungen von Vogelarten der Siedlungen seit, 87-106. Svensson, S. (2006) Species composition and population fluctuations of alpine bird communities during 38 years in the Scandinavian mountain range. Ornis Svecica, 16, 183-210. Svensson, S. (2009) A stable bird community during 27 years (1980—2006) in the nemoral broadleaf wood Dalby Söderskog National Park. Ornis Svecica, 19, 237-243. Tomiałojć, L. & Wesołowski, T. (1996) Structure of primaeval forest bird community during 1970s and 1990s [Bialowieza National Park, Poland]. Acta Ornithologica, 31, 133-154. Wesołowski, T., Tomiałojć, L., Mitrus, C. & Rowiński, P. (2002) The breeding bird community of a primaeval temperate forest (Białowieża National Park, Poland) at the end of the 20th century. Acta Ornithologica, 37, 27-45. Williamson, K. (1975) The breeding bird community of chalk grassland scrub in the Chiltern Hills. Bird Study, 22, 59-70.

1. The Excel spreadsheet titled "1_Cape Petrel Population adjusted Estimates_Table1.xlsx is population survey count data and estimates of Cape petrels in the Vestfold islands, East Antarctica in 1974 and 2017. Numbers present the number of occupied nests in each year. Adjusted data as per ICESCAPE modelling and provides a value based on attendance of Cape petrels relative to phenology, values in brackets are the lower and upper confidence intervals based on 95% confidence. No data is where there was no survey data available; however a 0 indicates the island was searched, however no breeding birds recorded at that site.
   
   Four surveys of Cape petrel breeding populations have been conducted in the Vestfold Islands: 1972-73 (Johnstone et al 1973), 1974-75 (AAD unpublished data), 2016-17 (Louise Emmerson and Anna Lashko) and 2017-18 austral summers (Kimberley Kliska and Marcus Salton). Here we refer to breeding seasons as the year eggs were laid, which was also when surveys were conducted. For example, 1972-73 breeding season spans from October 1972 until April 1973 and is referred to as 1972; 1974/75 is referred to as 1974 and 2017/18 as 2017. In 1972, numbers of occupied nests and distribution were assessed from ground surveys across the Vestfold Islands region and Cape petrels were found only in the southern half of the Vestfold Islands. In 1974, all accessible islands in this southern region were again surveyed from the ground or sea ice for Cape petrels from Bluff Island south to the Sørsdal Glacier. In addition, the ‘Northern Islands’ (Figure 1) were opportunistically searched during seal surveys conducted from 1-8th November 1974, and no sign of breeding Cape petrels were recorded (Williams, pers. comm. 2020). The 2016 survey focussed on identifying islands with cape petrels present in the south from ground-based activities, and in the north from aerial surveys. The 2017 survey focused search effort on all the islands where breeding Cape petrels were observed in 1972 and 1974. Similar to the 1974 survey, the Northern Islands were opportunistically searched for Cape petrels during seal surveys between the 5-13th December 2017, and no Cape petrels were observed. To our knowledge, no Cape petrels have been observed in the Northern Islands. We are therefore confident that this study encompasses the entire Vestfold Islands population.
   To assess the status and temporal change in population numbers of Cape petrels in the Vestfold Islands, datasets from the three breeding seasons were analysed, with two complete datasets, one a combination of both the 1972 and 1974 surveys and one from the 2017 survey were used in the final analysis. Three islands surveyed in the 1972 survey were not surveyed in 1974, therefore to complete the dataset for the 1974, the counts from these three islands in 1972 (Magnetic, Turner and Gardner Islands) were used to fill data gaps in 1974. The complete dataset is referred to as the 1974 dataset. Historical count data from 1972 and 1974 seasons were obtained from Johnstone et al 1973 and the Australian Antarctic Division Davis Biology species log 1974, respectively. In the 1972 survey, breeding pairs were estimated at various locations by island name and symbol shape on hand drawn maps. These symbols indicated which side of an island Cape petrels were located. In the 1974 survey breeding pairs of Cape petrels were recorded, as counted from the sea ice or by ground searching on the 17th of November and the 17th of December 1974. Locations of breeding Cape petrels were recorded with cross marks on hand drawn maps, indicating which gully or slope on an island Cape petrels were located. To ensure consistency of survey dates, both the Davis Station log book 1974 and the personal journal of Richard Williams (the biologist who undertook the survey work in 1974) were cross checked for survey dates.
   In the 2017 season, the survey was conducted over three days (18th, 20th and 30th of November) at all known Cape petrel breeding colonies. At each breeding colony a combination of ground searches and/or binocular counts were conducted from a vantage point on the sea ice tens of meters perpendicular away from Cape petrel breeding areas with the aim of counting all occupied nests. Occupied nests were classified as Confirmed if a bird was present at the nest and Unconfirmed if a nest was suspected but no bird observed (i.e. bowls of small pebbles and/or large amounts of guano on rocks were indicative of nests). Counts of confirmed nests were used to represent the number of occupied nests in 2017, and were considered consistent with breeding pair estimates in historic surveys. Birds observed on ledges without guano were considered loafing rather than breeding and not included in counts. The locations of breeding colonies were recorded using a combination of geographical positioning system (GPS) locations, hand-drawn maps and photographs of breeding colonies from the vantage point where counts were conducted. To compare changes between surveys, the Vestfold Island region was divided into two sections: Northern Islands and Southern Islands. The Southern Islands were further classified into three areas labelled A, B, and C. Area A is the northern part of the Southern Islands and includes Bluff, Turner, Magnetic and Gardner Islands and the Davis Station, and has the most persistent fast ice. Area B includes Hawker and Mule Islands and is further south, with intermediate fast ice duration, and Area C includes Zolotov and Kazak Islands and is furthest south, just north of the Sørsdal Glacier, and has the earliest loss of fast ice (Figure 1).To account for potential uncertainty in the population counts, we assumed the counts were within ±10% (with 95 % confidence) of the true number present. We refer to this as ‘count repeatability’.
   
   2. Attendance data titled "2_Attendance_CapePetrels_BluffIsland_2019-2020.csv." The attendance data is derived from images taken with a remotely deployed camera at the Bluff Island Cape petrel colony near Davis station, East Antarctica. This phenology of cape petrel at this colony was used to adjust historical and contemporary population estimates of the Cape Petrel population. The .csv file includes latitude and longitude, season, calendar time and date, and an occupied nest count from the 6th of November 2019 until the 8th of March 2020. The camera data were counted by Kimberley Kliska in June 2020 as part of a project investigating the phenology of Cape petrels in this region.
   
   3. The dataset in folders titled "1970s polygons" and "2017 polygons revised" contains boundaries of Cape petrel nesting areas at numerous breeding sites on islands off the Vestfold Hills, Antarctica, for the purpose of assessing change in the bird’s distribution between the early 1970s and 2017 (Kliska et al. 2021 manuscript in review). Nest areas were identified in the early 1970s during three surveys over three years 1972, 73 and 74, and in 2017 during one survey that year. Details of the surveys in 1970s were presented in the ANARE SCIENTIFIC REPORTS publication N. 123 ‘The Biology of the Vestfold Hills, Antarctica’ 1972-73 summer, and in the Davis Biology Species Log 1974 (included 1973-74 summer and 1974-75 summer) (the latter by Richard Williams). Details of the survey in 2017 were presented in the Seabirds Research end-of-season field report Davis 2017-18 summer (by Kim Kliska and Marcus Salton). Polygons created from the 2017 survey are published with the AADC (Emmerson and Southwell 2020).
   In both periods the islands were surveyed either by ground searching an area on foot or by visualising the birds from a distance with or without binoculars, and then transcribing the area with nests onto hand drawn maps. These hand drawn maps were transcribed on to spatially projected electronic maps by Marcus Salton to represent the maximal perimeter of the cape petrel nest areas. In the 1970’s surveys, the depicted nesting areas represented locations where birds were observed sitting on or next to nests (or extensive guano deposits that were indicative of a nest). Birds that were on rocks and not associated with a nest or extensive guano deposits were considered non-breeding, and areas with extensive guano deposits without birds considered inactive nests, which were both omitted from the nesting area. The polygons that had already been created from the 2017 survey (Emmerson and Southwell 2020) were modified to match this representation of nesting area, by excluding areas within inactive nests (based on recollections of Kim Kliska and Marcus Salton).
   Polygons were created using R computing software version 4.0.2 (2020-06-22). The spatially projected electronic maps were derived from two shapefiles from the AADC: a coastline file (‘all_coast_poly_2003.shp’ DOI) and a contour file (‘vestfold_contours.shp’ DOI). These shapefiles were projected using Azimuthal equidistant, with the centre of the study area at latitude = -68.5785 and longitude = 77.8709 for visualisation purposes. Polygons are grouped by island. Not all islands have formal names. Therefore the number system created by Southwell (2016 a, b) for a project on Adelie penguins was adopted.

For each atlas, the total number of reports (Nrep) and the number of atlas cells surveyed (Cells) are given. A species' national range size was estimated as the number of atlas cells occupied (not corrected for differences in the coverage). ‘Latitude’ is the weighted mean latitude of a species' presence in each atlas, where weighting was based on the proportion of surveyed atlas cells per latitude which were occupied. Latitude is given in Finnish uniform grid coordinates, equivalent to kilometres north of the equator. Means are presented with their standard error.

Influenza A viruses (IAV) circulate endemically among many wild aquatic bird populations that seasonally migrate between wintering grounds in southern latitudes to breeding ranges along the perimeter of the circumpolar arctic. Arctic and subarctic zones are hypothesized to serve as ecologic drivers of the intercontinental movement and reassortment of IAVs due to high densities of disparate populations of long distance migratory and native bird species present during breeding seasons. Iceland is a staging ground that connects the East Atlantic and North Atlantic American flyways, providing a unique study system for characterizing viral flow between eastern and western hemispheres. Using Bayesian phylodynamic analyses, we sought to evaluate the viral connectivity of Iceland to proximal regions and how inter-species transmission and reassortment dynamics in this region influence the geographic spread of low and highly pathogenic IAVs. Findings demonstrate that IAV movement in the arctic and subarctic follows seabird migration around the perimeter of the circumpolar north, favoring short-distance flights between proximal regions rather than long distance flights over the polar interior. Iceland connects virus movement between mainland Europe and North America, particularly due to the westward migration of wild birds from mainland Europe to Northeastern Canada and Greenland. Though virus diffusion rates were similar among avian taxonomic groups in Iceland, gulls act as recipients and not sources of IAVs to other avian hosts prior to onward migration. These data identify patterns of virus movement in northern latitudes and inform future surveillance strategies related to seasonal and emergent IAVs with pandemic potential. Methods Field sample collection From May 2010 through February 2018, we obtained IAV isolates from various species of seabirds, shorebirds, and waterfowl as well as environmental sampling of avian fecal material from locations throughout Iceland (capture and swab data can be found here: https://doi.org/10.5066/XX (Dusek et al. 202X)). Live sampled birds were captured using a 18m x 12m cannon-propelled capture net, noose pole, or hand capture. Birds found dead or moribund were also sampled. Hunter-harvested waterfowl and fisheries-bycatch seabirds were sampled as available. All birds were identified to species and, for live birds, individually marked with metal bands. Age characteristics were determined and age was documented for each bird according to the following schemes adapted from U.S. Geological Survey year classification codes: hatched in same calendar year as sampling (1CY), hatched previous calendar year (2CY), hatched previous calendar year or older, exact age unknown (2CY+), hatched three calendar years prior to sampling (3CY), hatched four calendar years prior to sampling (4CY), hatched more than four calendar years prior to sampling (4CY+), or unknown if age could not be determined (U) (Olsen KM, 2004; Prater, Marchant, & Vuorinen, 1977; USGS, 2020). Due to species specific differences, not all aging categories could be applied to all species sampled. All live birds were immediately released following completion of sampling. To sample for IAV, a single polyester-tipped swab was used to swab the cloaca only (2010-2013) or to first swab the oral cavity then the cloaca (2014-2017). Opportunistic environmental sampling of fecal material was also conducted using a direct swabbing method (2018). Each swab sample was immediately placed in individual cryovials containing 1.25 ml viral transport media (Docherty & Slota, 1988). Vials were held on ice for up to 5 hours prior to being stored in liquid nitrogen or liquid nitrogen vapor. Samples were shipped on dry ice from Iceland to Madison, Wisconsin, USA by private courier with dry ice replenishment during shipping. Once received in the laboratory, samples were stored at -80o C until analysis. Virus extraction, RT-PCR, virus isolation Viral RNA was extracted from swab samples using the MagMAXTM-96 AI/ND Viral RNA Isolation Kit (Ambion, Austin, TX) following the manufacturer’s procedures. Real-time RT-PCR was performed using previously published procedures, primers, and probes (Spackman et al., 2002) designed to detect the IAV matrix gene. RT-PCR assays utilized reagents provided in the Qiagen OneStep® RT-PCR kit. Virus isolation was performed in embryonating chicken egg culture on all swab samples exhibiting positive Ct values from RT-PCR analysis (Woolcock, 2008), with a primary cut off value of 45 on primary screen and 22 on secondary screen. All virus isolates were screened for the presence of H5 and H7 IAV subtypes using primers and probes specific for those subtypes (Spackman et al., 2002). Egg-grown virus isolates were sequenced using multiple standard methods including Sanger, Roche 454, and Illumina (HiSeq 2000 and MiSeq) sequencing (Dusek et al., 2014; Guan et al., 2019; Hall et al., 2014). Datasets for phylodynamic analyses Global dataset: The PB2 segment was selected as the basis for phylodynamic analysis. Advantages of focusing on PB2 - the largest internal segment of the IAV genome - include maximizing the number of nucleotides in the analysis (>2000 nts) and investigating transmission dynamics without targeting a specific subtype. All available avian and marine mammal IAV PB2 genes sequenced between 2009 and 2019 globally were downloaded from the National Center for Biotechnology Information Influenza Virus Resource database (NCBI IVR) (http://www.ncbi.nlm.nih.gov/genomes/FLU/FLU.html) on February 12, 2020, resulting in 13,469 sequences. Duplicate sequences (based on collection date, location, and nucleotide content) and sequences with less than 75% unambiguous bases were removed, and all vaccine derivative and laboratory-synthesized recombinant sequences were excluded. Sequences in the dataset were only included if isolation dates, location, and host species were available, resulting in 7,245 remaining sequences. Downsampling of taxa: The downsampling strategy aimed to reduce the number of sequence taxa for computation and mitigate sampling bias while maintaining the genetic diversity in the dataset. Four variables were considered important for explaining genetic diversity in the IAV sequence dataset: geographic region, host taxa, sampling year, and hemagglutinin (HA) subtype. Geographic regions included North America, Europe, Iceland, Asia, Africa, and South America (Australia and Antarctica were removed due to insufficient sequence counts). HA subtypes included H1, H2, H3, H4, H6, H8, H10, H11, H12, H13, H14, H15, H16, and pooled H5/7/9. H5, H7, and H9 were combined, as these were over-represented in the global dataset. Host categories included Anseriformes, Charadriiformes, Galliformes, and Other, which comprised all other avian taxa and marine mammals. To inform the downsampling strategy and evaluate if any of the four variables were correlated, a multiple correspondence analysis (MCA) was performed (JMP Pro v.14.0.0 (JMP Version 14.0.0, 1989-2019)). The MCA uses categorical data as input, which for this study included the sampling metadata associated with each sequence (region, host taxa, year, and HA subtype). Through representation of the variables in two-dimensional Euclidean space, significant clustering of HA subtypes with host taxa was detected (Supplementary Fig. 1), indicative of host-specific subtypes that are a well-known feature of influenza. These findings confirmed by previously published data on species-specificity of HA subtypes (Byrd-Leotis, Cummings, & Steinhauer, 2017; Long, Mistry, Haslam, & Barclay, 2019; Verhagen et al., 2015) led us to downsample the dataset stratifying taxa by two non-overlapping variables: geographic region and HA subtype. Data were downsampled to maintain 21-75 taxa per geographic region category and 6-30 per HA subtype category, resulting in a total of 301 sequences (outgroup). This step was performed to mitigate sampling bias resulting in over-representation of species or viral strains, while accounting for genetic diversity in the dataset. Next, to ensure relative evenness of geographic state groupings for discrete trait analyses, virus sequences from Iceland (n=93) were downsampled by stratifying taxa by HA subtype and maintaining 1-15 sequences per category, resulting in 63 sequences (ingroup). These 63 sequences were used for global and local discrete trait analyses and reflected the composition of diverse subtypes by host for the full Iceland sequence dataset. The resulting dataset reflected the underlying composition of host-specific subtypes present in this localized system. To assist with rooting and time-calibration of the tree, historical avian sequences from NCBI IVR were downloaded for the years 1979-2008. These were downsampled by year to ensure one sequence per year, resulting in 30 historic sequences. The total downsampled dataset, including the outgroup (n=301), ingroup (n=63), and historic sequences (n=30) resulted in a total of 394 sequences. Europe-Iceland-North America Datasets: To elucidate viral dynamics between significant source regions and Iceland and within-Iceland phylodynamics, a second analysis was performed at a restricted scale to Europe, Iceland, and North America. The cleaned global dataset described above (n=7245) was downsampled to include significant source regions of North America (n=3222) and Europe (n=407), totaling 3629 sequences. To identify at lower spatial resolution the source/sink locations relevant to Iceland, a K-means cluster analysis was performed (JMP Pro v.14.0.0 (JMP Version 14.0.0, 1989-2019)) using latitude/longitude coordinates for each of the 3629 sequences (obtained by extracting sampling location from the strain name of each sequence and searching in www.geonames.org). A total of 20 intraregional clusters resulted in highest support. Identified clusters with <50 sequences were combined with geographically proximal

Eco-geographic rules describe spatial patterns in biological trait variation and shed light on the drivers of such variation. In animals, a consensus is emerging that ‘pioneering’ traits may facilitate range shifts via a set of bold, aggressive, and stress-resilient traits. Many of these same traits are associated with more northern latitudes, and most range shifts in the northern hemisphere indicate northward movement. As a consequence, it is unclear whether pioneering traits are simply corollaries of existing latitudinal variation, or whether they override other well-trodden latitudinal patterning as a unique eco-geographic rule of phenotypic variation. The tree swallow (Tachycineta bicolor) is a songbird undergoing a southward range shift in the eastern United States, in direct opposition of the poleward movement seen in most other native species’ range shifts. Because this organic range shift countervails the typical direction of movement, this case study provides for unique ecolog..., Study sites and environmental data Methods were approved by Appalachian State University IACUC #13-15 and US Master Banding Permit #23563. All animals were handled in such a way to reduce stress and avoid physical harm. All adults were released in their home territory. We conducted fieldwork during May-June 2015. Historical sites included Saukville, Wisconsin (43.382 N, 88.023 W), Long Point, Ontario (42.623 N, 80.465 W), and Wolfville, Nova Scotia (45.107 N, 64.378 W). Expansion sites included Bloomington, Indiana (39.142 N, 86.602 W); Ames, Iowa (42.073 N, 93.635 W); Davidson, North Carolina (35.438 N, 80.697 W); and Boone, North Carolina (36.196 N, 81.783 W). We recorded GPS coordinates at each nestbox (Garmin GPSmap 78s). Sites were categorized as either historical or expansion based on prior publications (Lee, 1993; Shutler et al., 2012), bolstered by personal communications with local researchers and data from the Bird Breeding Survey (BBS; 1967 to 2017)(Sauer et al., 2017). Histo..., Excel

Many species’ distributions are shifting in response to climate change. Many distributional shifts are predictably poleward or higher in elevation, but heterogeneity in the rate and direction of shifts both within and between species appears to be common. We found high heterogeneity in the trajectory of winter range shifts for 65 species of birds across eastern North America and in the different traits and trait interactions associated with these shifts across the spatial scales we examined. We used data from the Christmas Bird Count to quantify the trajectory of winter latitudinal center of abundance range shifts over four decades (1980–2019) for 65 species of songbirds and woodpeckers in North America, both across eastern North America (ENA) as a whole and for the Atlantic (ATL) and Mississippi (MISS) flyways separately. We then used linear models and AICc model selection to test whether species traits could explain variation in range shifts or flyway discrepancies. Across ENA, most species showed northward latitudinal range shifts, but some showed no latitudinal shift while others shifted southwards. Amongst ATL and MISS, we documented both within- and between-species differences in the rate and direction of latitudinal shifts, complicating the results from across ENA. No single trait emerged as a dominant driver of range shift differences at the ENA and flyway scales. Migration strategy interacted with insectivory to explain variation at the largest spatial scale (ENA), whereas frugivory and mean winter latitude explained much of the variation in ATL and MISS, respectively. Exploring heterogeneity in range shifts within and between species, and in the associations between range shifts and life history traits, will help us better understand the mechanisms that mediate differing responses to environmental change and predict which species will be better able to adapt to those changes. Methods We requested Christmas Bird Count (CBC) data from 1980–2019 from the National Audubon Society for 65 species (Supplementary Material Table S1) that met the above criteria. We chose 1980 as a starting point based on data from Rushing et al. (2020) and Vose et al. (2017) that indicated that breeding distributional shifts of North American birds and temperatures started to change most noticeably in the mid-1980s. The CBC is an annual survey during a two-week window centered on 25 December. Surveys occur within 24.14-km diameter circles centered on the same point each year, with locations across North America (figure 1). We truncated this dataset to only those circles located in the MISS and ATL flyway states and provinces (shaded regions, figure 1). To avoid potential spatial-temporal bias that may arise when new CBC circles are initiated each year, we limited our analysis to only those circles that participated in at least 36 out of the 40 years (90%) of data collection during the study period. Thus, of the 1,961 CBC circles in the two flyways that collected data during any years of the study period, we used data from 629 circles. The spatial distribution of CBC circles is biased towards the north and east of the study area (figure 1) (Meehan et al. 2019). This sampling bias may influence the static location of a species’ Latitudinal Center of Abundance (LCA), but it affects species similarly, and temporal changes in LCA location (our principal concern) arise only from differential abundance changes across sites over time, not from the static spatial sampling distribution. Latitudinal center of abundance calculation Latitudinal Center of Abundance is a standard expression of mean geographic range location, and LCA changes over time have been used to quantify range shifts (La Sorte and Thompson 2007, Paprocki et al. 2014). To calculate the LCA of each species for each year and flyway, we calculated the geographic mean of all CBC circles with that species present that year, weighted by the relative abundance of that species in each circle, using the R package geosphere (Hijmans et al. 2019). Because survey effort varies between circles and years, we used the number of birds per party-hour as an index of relative abundance for the weighted average to account for this variation in effort (Koenig and Liebhold 2016, Curley et al. 2020). For each species, we used linear regression to determine the strength and direction of the shift in LCA. We used year as the explanatory variable and LCA as the response variable, and the resulting regression slope estimated the rate of shift over the 40-year time period (see Figure 2). For our initial descriptive summaries of range shifts across species and flyways, we used only those slopes that were significantly different from zero as evidence for a range shift. However, all slopes were used unaltered for further statistical analysis (see below). To better illustrate effect sizes, we converted the regression slope from degrees latitude yr-1 to cumulative distance (in km) during the study period using the conversion of 1 degree = 111 km multiplied by 40 years. We performed these regressions for each species in eastern North America (ENA) as a whole, and for each flyway (ATL and MISS) separately to quantify variation in the direction and magnitude of range shifts between the flyways. We quantified flyway synchrony for each species by calculating Pearson’s correlation coefficient r between the flyway-specific LCA time series. This metric provides a quantitative comparison of the relative rate and direction of range shift between flyways for each species (see Figure 2). Within- versus between-species variation To compare between-species variation in the rate and direction of range shifts to within-species variation between flyways, we created and compared two indices. Our index of within-species variation was the absolute value of the difference between flyway regression slopes, for each species: s_withini = | ATL_slopei - MISS_slopei | For a comparable index of between-species variation, for each species, we calculated the absolute value of the difference between the ENA regression slope of that species (ENA_slopei) to the mean of all other species’ slopes (ENA_slopek) in the ENA dataset: s_betweeni = | ENA_slopei - [1/n∑63k=1(ENA_slopek)] | We converted slopes to cumulative distance (km) across the four-decade study period, as above. These two indices allowed us to more directly compare within- and between-species variation in range shifts for a given species, because they are on the same scale. In particular, the between-species variation provides a meaningful benchmark against which to judge the importance of the within-species variation. Species-level traits We tested if species-level traits could explain variation in the strength and direction of winter range shifts in the study area as a whole, within flyways, and between flyways. We included the following species-level traits in our analysis: Winter latitude (continuous variable): We calculated the mean LCA for each species during the first three years of the study period (1980–1982). The LCA during the first three years provides a baseline mean latitude for each species, prior to shifts occurring over the subsequent four decades. We included this variable to test whether more northerly species were shifting at different rates than more southerly species. Migration strategy (categorical variable): We placed each species into one of four migration strategy categories: residents, short-distance migrants, moderate-distance migrants, and irruptive migrants. We removed the three irruptive species, Cedar Waxwing (Bombycilla cedrorum), Pine Siskin (Spinus pinus), and Red-breasted Nuthatch (Sitta canadensis) from the trait-based models (see below) due to small sample size for the category (three species), but we included them in the initial descriptive statistics. Winter diet (continuous variable): Using data from Billerman et al. (2020), we quantified winter diet by estimating insects, fruit, and seeds taken in the winter for each species as a percentage of the total. To simplify this analysis, we did not include carnivorous (e.g. Loggerhead Shrike, Lanius ludovicianus) or piscivorous species (e.g. Belted Kingfisher, Megaceryle alcyon) in this study. Trait-based range shift models and model selection We created generalized linear models (glms) using the cumulative magnitudes of LCA range shift (in km) in ENA, ATL, and MISS as the response variables and species traits as explanatory variables, with interaction terms. For the flyway discrepancy response variable s_within, we created glms with Gamma error distribution to account for the log-normal distribution of this variable. We used all LCA regression slopes for the response variables rather than converting non-significant slopes to zero, which would have created a non-normal response variable, violating the assumptions of the linear models. In practice, most non-significant slopes were near zero (Figure 3). We used Akaike’s Information Criterion with small-sample size correction (AICc) to evaluate models with and without interactions (Burnham and Anderson 2002, Burnham et al. 2011). We performed model selection separately for ENA, ATL, MISS, and for σ_within, examining the same set of models in each case. The model set included all combinations of the trait variables winter latitude, migration strategy, and winter diet. Models that included latitude or % fruit in diet also included a quadratic component for those variables, based on unimodal (hump-shaped) relationships with range shifts suggested in preliminary scatter plot visual assessments (Supplementary Material Figure S1). Winter diet was represented by the three continuous variables % insects, % fruit, and % seeds, but only one of these was included in any given model to avoid collinearity. Additive and interaction models were included for each variable combination, resulting in a total of 26 models including an

Aim: Theory suggests that increasing productivity and climate stability towards the tropics favours specialization, thus contributing to the latitudinal richness gradient. A positive relationship between species richness and specialization should therefore emerge as a fundamental biogeographical pattern. However, land‐use and climate changes disproportionally increase the local extirpation risk for specialists, potentially weakening the relationship between richness and specialization. Here, we quantify empirically the richness–specialization prediction and test how 50 years of climate and land‐use change has affected the richness–specialization relationship. Location: USA. Time period: 1966–2015. Major taxa studied: Birds. Methods: We used the North American Breeding Bird Survey to quantify bird community richness and specialization to habitat and climate. We (a) quantify temporal change in the slope of the richness–specialization relationship, using a generalized mixed model; (b) assess how this change translates spatially, using generalized additive models; and (c) attribute spatio‐temporal change in the richness–specialization relationship to land use, climate and topographic drivers. Results: We found evidence for a positive but weak richness–specialization relationship in bird communities that greatly weakened over time. Given that specialization was not the main driver of richness, this relationship did not translate spatially into a linear spatial covariation between richness and specialization. Instead, the spatial covariation in richness and specialization followed a unimodal pattern, the peak of which shifted towards less specialized communities over time. These temporal changes were associated with precipitation change, decreasing temperature stability and land use. Main conclusions: Recent climate and land‐use changes have induced two contrasting types of community responses. In human‐dominated areas, the decoupling of richness and specialization drove a general trend for biotic homogenization. In areas of low human impact experiencing increasing climate harshness, specialization increased, whereas richness decreased. Our results offer new support for specialization as a key driver of macroecological diversity patterns and show that global changes are weakening this fundamental macroecological pattern.

BackgroundClimatic warming predicts that species move their entire distribution poleward. Poleward movement of the ‘cold’ side of the distribution of species is empirically supported, but evidence of poleward movement at the ‘warm’ distributional side is relatively scarce. Methodology/Principal FindingFinland has, as the first country in the world, completed three national atlas surveys of breeding birds, which we here use to calculate the sizes and weighted mean latitudes of the national range of 114 southern and 34 northern bird species during three periods (1974–1979; 1986–1989; 2006–2010), each denoting species presence in approximately 3 800 10×10 km2 squares. We find strong evidence that southern species (breeding predominantly in central Europe) showed a latitudinal shift of 1.1–1.3 km/year poleward during all three pairwise comparisons between these atlases (covering 11, 20.5 and 31.5 years respectively). We find evidence of a latitudinal shift of 0.7–0.8 km/year poleward of northern boreal and Arctic species, but this shift was not found in all study periods and may have been influenced by increased effort put into the more recent surveys. Species showed no significant correlation in changes in range size and weighted mean latitude between the first (11 year) and second (20.5 year) period covered by consecutive atlases, suggesting weak phylogenetic signal and little scope of species characteristics in explaining latitudinal avian range changes. ConclusionsExtinction-driven avian range changes (at the ‘warm’ side) of a species' distribution occur at approximately half the rate of colonisation-driven range changes (at the ‘cold’ side), and its quantification therefore requires long-term monitoring data, possibly explaining why evidence for such changes is currently rare. A clear latitudinal shift in an assemblage of species may still harbour considerable temporal inconsistency in latitudinal movement on the species level. Understanding this inconsistency is important for predictive modelling of species composition in a changing world.

LL = Latitude and Longitude. A is model without Latitude and Longitude, B is model with Latitude and Longitude. Most-supported model within each guild bolded for emphasis.

The relationship between colony area and population density of Adelie Penguins Pygoscelis adeliae was examined to determine whether colony area, measured from aerial or satellite imagery, could be used to estimate population density, and hence detect changes in populations over time. Using maps drawn from vertical aerial photographs of Adelie Penguin colonies in the Mawson region, pair density ranged between 0.1 and 3.1 pairs/m2, with a mean of 0.63 - 0.3 pairs/m2. Colony area explained 96.4% of the variance in colony populations (range 90.4 - 99.6%) for 979 colonies at Mawson. Mean densities were not significantly different among the 19 islands in the region, but significant differences in mean pair density were observed among colonies in Mawson, Whitney Point (Casey, East Antarctica) and Cape Crozier (Ross Sea) populations.

This work was completed as part of ASAC project 1219 (ASAC_1219).

The fields in this dataset are:

Island Latitude Longitude Date Colony area Breeding Pairs Breeding Pairs per square metre Area per nest Number of nests Number of adults

Data used in the manuscript "Climate-related range shifts in Arctic-breeding shorebirds", submitted to Diversity and Distributions, June 2022. Data was funded and collected by Environment and Climate Change Canada

Metadata:

species - 4 letter codes denoting the observed shorebird species. See https://www.birdpop.org/docs/misc/Alpha_codes_eng.pdf

binomial - Binomial nomenclature identifying the genus and species of the observed shorebird

sti - Species Temperature Index. Calculated as the mean temperature with in the species breeding range. Temperature data came from the WorldClim 2.1 data set (Fick, S.E. and R.J. Hijmans, 2017. WorldClim 2: new 1km spatial resolution climate surfaces for global land areas. International Journal of Climatology 37 (12): 4302-4315). Shorebird breeding range data came from BirdLife International (BirdLife International and Handbook of the Birds of the World. 2020. “Bird Species Distribution Maps of the World. Version 2020.1.” http://datazone.birdlife.org/species/requestdis.)

plot - Plot identification number, as part of Environment and Climate Change Canada's Program for Regional and International Shorebird Monitoring program (PRISM).

time_period - Denotes which time period the surveys took place in

region - Denotes which region the surveys took place in

plot_area_km2 - Area of the surveyed plot in km2

plot_habitat - Surveyors estimated the proportion of each plot covered by upland and lowland habitat. Plots were the categorized as upland or lowland depending on which habitat type was predominant

n_birds - The number of breeding birds of this species that were observed on plot during the survey.

presence - A species was identified as present if one or more individuals of that species were present during the survey.

latitude - Latitude of the plot corner

longitude - Longitude of the plot corner

coordinate_type - Which corner of the plot are the coordinates for?

Wetlands are a critical habitat for boreal mammals and birds that rely on them for breeding, foraging, and resting. However, wetlands in boreal regions are increasingly experiencing natural and human pressures. These impacts can lead to a reduction in the availability of wetland habitats for boreal mammals and birds that rely on wetlands for breeding, foraging, and resting. To inform management and conservation, camera traps provide an opportunity to survey mammals and birds to investigate their habitat preferences. We aimed to evaluate the effect of habitat features on the occupancy of mammals and birds in boreal wetlands. We used a multispecies occupancy model to estimate the habitat associations of 11 mammals and 45 avian species detected at 50 sampling ponds during the summers of 2018 and 2019 in Northern Quebec. Our results indicate that certain mammals, such as Red Fox and River Otters, and birds including the American Pipit, Common Raven, Hooded Merganser, and Greater Yellowlegs showed a preference for peatland ponds, whereas the Common Grackle preferred beaver ponds. We found few effects of distance to roads, and no effect of amount of forest cover on species occupancy. The occupancy of 27% of mammals and 24% of birds decreased with increasing latitude. These findings offer valuable insights for informing conservation initiatives focused on the preservation of wetlands in northern Quebec. By discerning the specific types of ponds preferred by each species, conservationists can strategically ensure the preservation and proper management of these habitats, thereby enhancing their conservation efforts. Methods Camera trap survey We conducted camera trap surveys at 50 wetlands over two summers in 2018 and 2019, using three cameras per pond in a triangular configuration. We used scent lures as attractants, and cameras were rotated and baited accordingly. Data collection occurred during two sessions of seven consecutive days per year. Each camera array consisted of three infrared Bushnell Trophy Cam HD motion-sensing digital cameras set to be active 24 hours/day. Cameras were placed at the edge of the pond and secured to a tree or a wooden stake at an average height of 30–60 cm at about 2–5 m from the water. Cameras were triggered by animal movements and programmed to take three photographs per trigger event, and a following video of 10-s, with a 1-min interval delay between detections to avoid that a single animal would be the subject of a long event of recording. We placed scent lure on a stick at 2 -5 m in front of the camera trap to increase the probability of detecting animals approaching the lure. At the end of each session, cameras were checked, photos were analyzed to identify bird and mammal species, and records from the same species at the same pond on the same day were combined into one detection event. Covariates such as pond type, forest cover, year, latitude, and distance to the nearest road were considered in analyzing bird and mammal occupancy and detection probability. Variables such as cumulative rainfall, days since snowmelt, and sampling effort were also factored into the detection analysis. Data processing and analysis To enhance species detection, we combined observations from three camera traps at a specific site on a given day, resulting in a data matrix of 100 rows and 14 columns per species. Using a multispecies occupancy framework, we analyzed bird and mammal communities, considering various factors such as pond type, forest cover, year, latitude, and distance to the nearest road. Model parameters were estimated using a Bayesian approach with Markov chain Monte Carlo in JAGS 4.3.0 within R 4.1.2. Convergence of the chains was assessed through trace plots, posterior density plots, and the Brooks-Gelman-Rubin statistic. Model fit was evaluated using posterior predictive checks and the area under the receiver operating characteristic curve.

The current distribution of Adelie penguin breeding colonies in the AAT is being mapped through a series of 'occupancy' surveys.

A GIS of potential Adelie penguin breeding habitat was developed to structure the overall search effort. Information about the GIS is given in Southwell et al. (2009) and in the related metadata record 'Sites of potential habitat for breeding Adelie penguins in East Antarctica' with Entry ID AAS_4088_Adelie_Potential_Habitats.

The AAT coastline was broken into groups and subgroups which were surveyed when logistics allowed. All sites of potential habitat in each section were searched and a record of whether breeding penguins were present or absent was made. Most surveys were undertaken during the Adelie penguin breeding season when breeding penguins would have been present; any surveys outside the breeding season made observations of the presence or absence of guano. Most surveys have been undertaken from aircraft (both helicopters and fixed wing), but some have been done from the ground.

Maps of potential breeding habitat in the groups and subgroups were produced from the GIS to use in the field surveys. The data recorded for each search campaign included the latitude and longitude of the centroid of each site that was searched, the data of search, the observer(s), and the state of occupancy (present or absent).

These data were incorporated into the occupancy surveys undertaken as part of AAS project 4088. See the metadata record for that project to access the data (at the provided URL).