Description:

Overview
We focused our empirical measurements in Sabah, Borneo. We measured a microclimate thermal proxy sensitive to radiative, convective, and conductive heat fluxes. This thermal proxy is relevant to a range of organismal processes including plant regeneration, animal behavior, and soil nutrient fluxes. Measurements were made across space and time within three 1-ha plots comprising a gradient from old growth to heavy selective logging. We then combined these data with nearby weather station air temperature data, as well as measurements of topography and canopy structure derived from detailed ground surveys and airborne LIDAR.
Sampling design
Study location
We identified three sites along a logging intensity gradient in the Malaysian state of Sabah in Borneo. Sites each contain a 1-ha plot in lowland mixed dipterocarp forest. The plots are chosen to contrast an unlogged old growth forest with a moderately logged forest and a heavily logged forest
Microclimate
To map the microclimate thermal proxy in each plot, we installed dataloggers on a semi-regular grid pattern varying in minimum distance from 1 to 14 meters. Each datalogger was a Thermochron iButton (DS1921G, Maxim), capable of logging up to 2048 temperature values between -30°C and 70°. Each datalogger was waterproofed by wrapping in plastic paraffin film (Parafilm, Bemis) and then in light yellow duct tape. Dataloggers were attached using plastic zip-ties onto PVC stakes at a height of 1-3 cm immediately above the forest floor. The tape color was chosen to approximate the albedo of vegetation/soil, and the size of the sensor package was chosen to have a similar boundary layer to many small organisms (e.g. tree seedlings, fallen branches, large insects). The sensor packages intentionally did not include a radiation shield, as the intent was not to measure air temperature. Temperatures recorded by the loggers therefore reflect a combination of conductive, convective, and radiative heat fluxes, and can be considered a rough proxy for those experienced by small organisms.
Each plot contains 25 20 × 20 m2 subplots, demarcated by 1 m-high PVC stakes embedded in the soil. Each subplot also contains at its center a mesh litter trap suspended on PVC stakes at 1-m height. Exact locations for all subplot corner and center stakes were determined using ground-based Field-Map software (IFER, Jílové u Prahy, Czech Republic). Spatial positions were recorded in three-dimensional space (local x, y, z-coordinates) using an Impulse 200 Standard laser rangefinder, MapStar Module II electronic compass (Laser Technology, Colorado, USA).
We installed a datalogger on these stakes at the corner of each subplot and the center of each subplot. We also chose at random three focal subplots in each plot for higher-resolution sampling. Within these subplots we established a cross-type design, with six additional dataloggers deployed at 1 to 5 m distance on additional PVC stakes located near each litter trap. A total of 239 dataloggers were installed.
Dataloggers were deployed during the end of the dry season in late 2015. Each recorded 28 days of data at 20-minute intervals. Start times were synchronized among dataloggers within plots. The exact date of deployment was 1 November for the heavily logged plot and 9 November for the moderately logged and old growth plot. Weather conditions during November-December 2015 were consistently dry and hot, so we do not anticipate any biases from the differing start dates. In nearly all cases dataloggers were recovered in their original location, except for a small number that were transported 1-2 meters down slopes. We treated data from these as though they were in their original position. A small number of dataloggers also failed due to being lost or punctured by animal bites. 90% of dataloggers (214/239) were successfully recovered and downloaded.
Air temperature data
To compare the microclimate thermal proxy to other temperature metrics, we obtained off-plot (open site) and on-plot (below canopy) weather station data. To represent off-plot data for both the moderately and heavily logged plots, a weather station was located in a cleared area at the SAFE base camp (4.724341°N, 117.601449°E), at a distance of 2.0 km from the heavily logged plot and 3.9 km from the moderately logged plot. Data were logged continuously (Datahog, Skye Instruments, UK). Measurements included air temperature (°C) and photosynthetically active radiation (W m-2). Data were available for all of the study period. To represent off-plot data for the old growth plot, another weather station was located in a cleared area at the Maliau Basin Studies Center (4.736263°N, 116.97662°E), at a distance of 1.4 km from the plot. Available data only included photosynthetically active radiation (W m-2). Data were available for approximately 25% of the study period. We predicted air temperature values at this plot for these dates by calibrating a LOESS regression model of air temperature based on time of day (seconds after midnight) and photosynthetically active radiation, calibrated with weather station data from an open clearing at the SAFE base camp (78 km distance, 184 m lower elevation). Because of the small elevation change we did not include a further lapse rate correction for temperature. The fitted model, which had a residual standard error of 0.9°C, was used to predict off-plot air temperature at the old growth plot.
To represent on-plot air temperature, we located air temperature sensors (HOBO, U23-002) within radiation shields at 1.5 m height in a subplot within each plot (corresponding to a focal subplot with a higher density of microclimate dataloggers: old growth, subplot 18; moderately logged, subplot 24; heavily logged, subplot 25). Temperature was measured hourly.
LiDAR data
Discrete airborne LiDAR data were acquired by NERC's Airborne Research Facility (ARF) in November of 2014 using a Leica ALS50-II LiDAR sensor flown on a Dornier 228-20 at 41 points m-2 density, with up to four returns recorded per pulse. Georeferencing of the point cloud was ensured by incorporating data from a Leica base station in the study area. LiDAR point clouds were classified into ground and non-ground points, and used to produce a 1 m resolution canopy height model by averaging the first returns. Gaps in the canopy height model were filled by averaging neighboring cells.
Topography
The ground-mapped coordinates of the subplot corners, subplot centers, and all stems were used to construct a digital elevation model (DEM) for the plot. Elevation was interpolated onto a 1 m grid using ordinary kriging with a minimum of 4 points and search radius of 30 meters. This grid was then aligned to the LIDAR-determined location and elevation of the plot corners. The DEM was then used to estimate slope (in degrees) and cosine of aspect (with higher values indicating more southerly exposures) for each location.
Forest structure
Forest structure was determined from field surveys and from airborne laser scanning. For the field survey, all trees ≥10 cm diameter at 1.3 m height were censused in each plot in 2016. Diameter was measured at 1.3 m with a tape measure, height with laser rangefinders, and x-y position of each stem were determined using the same system as the subplot corners. The horizontal crown projection of every tree was mapped by measuring spatial positions (x and y-coordinates) of 5 to 30 points (depending on the size of the crown) at the boundary of a crown projected to the horizontal plane and then smoothed using Field-Map software.
Field stem maps were then converted into raster grids of stem basal area density (smoothed with 2-meter Gaussian kernel, and then rasterized to 1 m resolution), canopy density (number of overlying canopies per unit area) (1 m resolution), and plant area index (PAI) (10 m native resolution, interpolated to 1 m resolution). Spatial variation in PAI was mapped from the LiDAR point cloud using the MacArthur-Horn method. The method assumes that the leaves are randomly distributed within the laterally homogeneous canopy layers, so the PAI is proportional to the logarithm of the fraction of LiDAR pulses, β, penetrating through the canopy: PAI = -1/κ ln(β), where κ is a correction factor that accounts for canopy features, such as clumping and the distribution of leaf angles. We assumed a constant value of κ=0.7. Only the first returns, representing the first interaction of each LiDAR pulse with the canopy, are considered. We employed a lower cutoff of 2 m to avoid confusing ground returns with low-lying vegetation. PAI was estimated for point locations along a 1 m regular grid using circular sampling neighborhood of 10 m. This sampling window size is used to capture a sufficient number of LiDAR returns to avoid saturation effects in the more densely vegetated parts of the plots. This approach for calculating canopy closure may be biased, as clumping of vegetation, variation in leaf angle, and canopy edges (i.e. at gaps) should lead to spatial variation in the κ coefficient. It was not possible with our data to constrain κ using hemispherical photos due to saturation effects.

Project: This dataset was collected as part of the following SAFE research project: Drivers of microclimate variation in disturbed forests

Funding: These data were collected as part of research funded by:

• NERC independent research fellowship (Standard grant, NE/M019160/1)

This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.

Permits: These data were collected under permit from the following

Temperatures recorded 5cm above the forest floor in a gridded design (1 to 13m distance) within three, 1 hectare forest plots in Sabah, Borneo. The dataset also includes air temperature data from a nearby weather station at the same temporal resolution, and spatially-interpolated measurements of topography and canopy structure in each forest plot at a 1m resolution. iButton temperature measurement 5cm above the forest floor in gridded design (1-13m distance) within three 1-ha forest plots in Sabah, Borneo. Measurements were taken at 20 minute intervals over one continuous month (November 2015). Dataset also includes nearby weather station air temperature data at identical temporal resolution, as well as spatially-interpolated (1-m) measurements of topography and canopy structure in each forest plot. Output of BALI project (NERC funded Human-modified Tropical Forest Programme).

The model explained 17.3% of variance in the Euclidean distance of spectra (F2,53 = 6.742, p = 0.002).

1. Metacommunity research relies largely on proxies for inferring the effect of dispersal on local community structure. Overland and watercourse distances have been typically used as such proxies. A good proxy for dispersal should, however, take into account more complex landscape features that can affect an organism's movement and dispersal. The cost distance approach does just that, allowing determining the path of least resistance across a landscape. 2. Here, we examined the distance decay of assemblage similarity within a subarctic stream insect metacommunity. We tested whether overland, watercourse and cumulative cost distances performed differently as correlates of dissimilarity in assemblage composition between sites. We also investigated the effect of body size and dispersal mode on metacommunity organization. 3. We found that dissimilarities in assemblage composition correlated more strongly with environmental than physical distances between sites. Overland and watercourse dist...

The long-distance migrations by marine fishes are difficult to track by field observation. Here, we propose a new method to track such migrations using stable nitrogen isotopic composition at the base of the food web (δ15NBase), which allows for direct comparison of isotope ratios between proxy organisms of the isoscape and the target migratory animal. We initially constructed a δ15NBase isoscape in the North Pacific by bulk and compound-specific isotope analyses of copepods (n = 360 and 24, respectively). We then determined retrospective δ15NBase values of spawning chum salmon (Oncorhynchus keta) from their vertebral centra (10 sections from each of two salmon), and estimated their migration routes by using a state-space model. Our isotope tracking method successfully reproduced a known chum salmon migration route between the Okhotsk and Bering seas, and our findings suggest the presence of a migration route to the Bering Sea Shelf during a later growth stage.

The F10.7 radio flux measured at Earth is used to estimate the solar EUV flux from 2-100 nm at Earth. These values are then scaled to Mars' orbital distance from the Sun using a 1/r2 scaling, and time-shifted to account for the solar longitude difference between the Earth and Sun. A 26-day solar rotation rate is assumed.

Many fish form schools and maintain visual contact with their neighbors in a three-dimensional environment. In this study, we assessed whether zebrafish modified their spacing and interaction time in an additive or multiplicative way relative to multiple sources of social information using computer animations. We simultaneously manipulated: (a) the size of the virtual conspecific (as a proxy of social cue magnitude), (b) the position of the virtual conspecific in the water column (as a proxy of the level of perceived risk), and (c) the absence/presence of the visual horizon (as a proxy of depth perception). We found that the size of the virtual conspecific independently affected spacing behavior (zebrafish increased their separation distance as conspecific size increased). However, some of these factors interacted significantly, such that their effects on social behavior depended on each other. For instance, zebrafish increased their separation distance under high risk conditions when the virtual conspecific was larger, but this risk effect disappeared when the conspecific was the same size or smaller, likely to avoid aggression. Also, zebrafish increased their separation distance when depth perception was enhanced under low risk conditions, but the effect of depth perception disappeared under high risk conditions. Overall, we found that certain dimensions of the visual social environment affected zebrafish spacing behavior in different ways, but they did not affect social interaction time. We discuss the implications of these findings for the spatial organization of fish schools.

Summary of advantages and weaknesses of each predation risk proxy, and suggested cases of when to use each depending on the predator’s preferences.

Although the permanently to seasonally ice-covered Arctic Ocean is a unique and sensitive component in the Earth's climate system, the knowledge of its long-term climate history remains very limited due to the restricted number of pre-Quaternary sedimentary records. During Polarstern Expedition PS87/2014, we discovered multiple submarine landslides over a distance of >350 km along Lomonosov Ridge. Removal of younger sediments from steep headwalls has led to exhumation of Miocene to early Quaternary sediments close to the seafloor, allowing the retrieval of such old sediments with gravity cores. Multi-proxy biomarker analyses of these gravity cores reveal for the first time that the late Miocene central Arctic Ocean was relatively warm (4-7°C) and ice-free during summer, whereas sea ice occurred during spring and autumn/winter. A comparison of our proxy data with Miocene climate simulations seems to favour relatively high late Miocene atmospheric CO2 concentrations. These new findings from the Arctic region provide new benchmarks for groundtruthing global climate reconstructions and modeling.

Aim: Dispersal limitation, environmental selection and drift are known to influence both taxonomic similarity between communities and genetic similarity between populations. However, disentangling the relative roles of these processes on spatial patterns of differentiation - whether regarding taxonomic differentiation between communities or genetic differentiation between populations - is challenging. Investigating whether spatial patterns of taxonomic differentiation and genetic differentiation are correlated (β-SGDCs) is a promising approach to address this issue. Here, we investigated β-SGDCs over broad spatial scales and 22 freshwater fish species to elucidate the processes shaping taxonomic and genetic differentiations between drainage basins.

Location: Global scope, data mainly from Europe and North America.

Taxon: Actinopterygii and Petromyzontiformes (freshwater fishes).

Methods: We used Mantel tests to investigate the raw correlation between taxonomic and genetic differentiations. We carried out multiple regressions to characterise the effects of geographic distance (proxy of dispersal limitation), environmental distance (proxy of environmental selection) and pairwise harmonic mean area between basins (proxy of drift) on taxonomic differentiation and genetic differentiation taken separately. We then analysed the correlation between the residuals of these two regressions with Mantel tests. Finally, we investigated whether the variation in β-SGDC between species was related to species traits.

Results: We detected an overall positive β-SGDC, both for the raw and the residual correlations. This implies that, unmeasured effects, other than geographic distance, environmental distance and harmonic mean area, influence the β-SGDC observed. Values of β-SGDCs greatly varied between species, but this was not explained by any species traits.

Main conclusions: Independently of the effects of geographic distance, environmental distance and harmonic mean area, the β-SGDC we observed suggests that historical processes strongly shaped the patterns of taxonomic and genetic differentiations between basins. Consequently, taxonomic differentiation may be an appropriate proxy to explain the influence of historical processes on genetic differentiation.

Yearly citation counts for the publication titled "Emotional vocabulary in immigrants’ L2 written discourse: is linguistic distance a proxy for L2 emotionality?".

Proxied dataset of inshore lobster commercial fishing for 2012 - 2021 in the Newfoundland and Labrador region. Only lobster harvested from the Newfoundland and Labrador region are included, based on species sought. Commercial data for the inshore lobster fishery does not require a set of coordinates be provided for catch records. With zero georeferenced inshore lobster records, the inshore lobster fishery leaves a major data gap in one of Newfoundland and Labradors largest fisheries. The Gulf region created a lobster proxy mapping tool, which associated each commercial lobster record with the most likely 10km2 hexagon grid cell based on a number of weighted variables. The tool was adopted by the Newfoundland and Labrador region and altered to work with its own variables which include human use, habitat, accessibility, area/location, home port distance, traditional ecological knowledge and depth. Each hexagon represents the summed total weight of all records associated with a particular hexagon. The best available commercial data used in this model is derived from landings data and may not include catches that have resulted in cash/wharf sales. As a result, there are some areas of Newfoundland and Labrador that may be under represented in this dataset where wharf sales may be high. Therefore, this dataset should be viewed as a general estimation on lobster harvesting patterns within Newfoundland and Labrador.

Flight initiation distance is repeatable and geographically flexible in greylag geese (Anser anser)

https://doi.org/10.5061/dryad.pvmcvdnw5

Description of the data and file structure

Full details for this dataset are given in Katsis et al., (2024, Journal of Avian Biology).

Study sites and species

This study was conducted in a free-roaming population of greylag geese in Grünau im Almtal, Upper Austria. All members of the flock are individually identifiable; almost all geese are marked with a unique combination of aluminum and colored leg bands and any unbanded individuals can be distinguished from their facial markings. Some flock members (n = 11) were hand-raised by a human foster parent until fledging, as part of an ongoing long-term research program. We collected data over two testing periods outside the greylag goose breeding season and approximately 13 months apart: 8–14 September 2022 and 10–29 October 2023. During the fi...

AICc = Akaike Information Criteria adjusted for small sample sizes; ΔAICc = (AICc)–(AICc)min; w = Akaike weight. Candidate models with ΔAICc < 2 (bold face) were selected as final models.a Combined nyala, impala and warthog biomass density estimates derived from aerial count data in Phinda GR, 2002–2012.A priori linear models exploring the effect of sex, time as a proxy for population density and prey biomass density on dispersal distance in subadult leopards in Phinda Game Reserve, South Africa, 2002–2012.

Two loess-paleosol sequences from the Heilbronn Basin in southwestern Germany, Frankenbach and Talheim, have been investigated to gain information about environmental conditions during the last interglacial-glacial cycle. Since paleoenvironmental research on terrestrial archives is crucial for understanding past climatic and environmental conditions, the profiles were studied using a multi-proxy approach. Applied methods include the analyses of grain-size distribution, element concentrations, color data, and rock magnetism. The data reveal that the two profiles show large variations in conservation due to their different geomorphologic positions. Moreover, the results imply a subarctic climate with stable conditions during the Middle Pleniglacial (MIS 3) in-situ soil formation phase. At the same time, the Lower Pleniglacial (MIS 4) was characterized by erosion and reworking of Eemian and Early Glacial (MIS 5) material but also by the accumulation of aeolian material during the late Lower Pleniglacial (MIS 4) in Frankenbach. The results further suggest that the Rhine River floodplain has been a major source for the accumulated material in the two profiles, which indicates westerly wind directions during the time of accumulation. The low GSI and U-Ratio of Upper Pleniglacial (MIS 2) sediments, supported by the overall high content of fine material, imply a larger distance to the source area. We give an overview of loess in the Heilbronn Basin and place it in a wider context of loess in Western Europe.

Network of 38 papers and 55 citation links related to "Emotional vocabulary in immigrants’ L2 written discourse: is linguistic distance a proxy for L2 emotionality?".

An overview of the model parameters and their relationships with age and social setting. Each parameter’s source is also provided.

Flight plays crucial role in the fitness of insect pollinators, such as bumble bees. Despite their relatively large body size compared to their wings, bumble bees can fly under difficult ambient conditions, such as cooler temperatures. While their body size is often positively linked to their foraging range and flight ability, the influence of age remains less explored. Here, we studied the flight performance (distance, duration, and speed) of aging bumble bee workers using tethered flight mills. Additionally, we measured their intertegular distance (ITD) and dry mass as proxies for their body size. We found that flight distance and duration was predominantly influenced by age, challenging assumptions that age does not play a key role in foraging and task allocation. From the age of 7 to 14 days, flight distance and duration increased six-fold and five-fold, respectively. Conversely, body size primarily impacted the maximum and average flight speed of workers. Our findings indicate that age substantially influences flight distance and duration in bumble bee workers, affecting foraging performance and potentially altering task allocation strategies. This underscores the importance of considering individual age and physiological changes alongside body size/mass in experiments involving bumble bee workers. Methods Experimental overview The experiment was designed based a pilot study, in which the flight performance of differently aged workers from two colonies had been examined in the previous year. In this study, we repeatedly measured the individual flight performance of 61 workers from four colonies (n = 14, 18, 8 and 21 workers per colony) at the age of 7, 14 and 21 days. Due to occasional difficulties in locating every marked worker on each day of measurement or potential mortality (n = 13/77), or instances where a few workers had lost their tags during flight measurement (n = 5/77), these individuals were excluded from data analysis. We selected workers with a wet body mass ranging from 100 mg to 320 mg, primarily during the colony’s exponential growth phase. Bumble bee husbandry We used four commercial, queenright Bombus terrestris colonies (Natupol Research Hives, Koppert B.V., Netherlands) with approximately 20-30 workers. They were housed in their standard plastic nest boxes (27 [l] x 24 [w] x 14 [h] cm). Each colony was connected to a small foraging arena (60 x 40 x 28 cm), where bees were required to perform foraging flights to access ad libitum 40% w/v sucrose solution provided through 50 mL gravity feeders. Additionally, colonies were provided with 3-6 g of pollen candy (2:1 honey bee collected organic pollen: 75% sucrose syrup) daily, depending on their consumption. Colonies were kept under laboratory conditions with a relative humidity of about 40 % RH and an average room temperature of 23 ± 1 °C. Foraging areas were illuminated by two flicker free daylight-like LEDs (each 2400 lm, CRI98, 5500 K, True-Light International GmbH, Germany) under 14:10 h light:dark regime, but nest boxes were kept dark by covering them with cardboard. The foraging arenas served to allow aging bees to ‘practice’ flying and become accustomed to the same lighting conditions used during tests (described below). Tagging and marking Newly emerged workers (< 1 d old), identified by their silvery appearance and unfurled wings [43], were collected from their colonies. Their sex was determined by counting antennal segments (females have 12, males 13), under a stereo microscope. Then bees were immobilized with metal pins without harming them to carefully shave off the thorax hairs between the tegula, where a circular stainless-steel tag (Æ 2 mm, thickness = 0.1 mm, weight (mean ± sd) = 2.43 ± 0.02 mg, n = 20, Sartorius Micro SC2, Sartorius AG, Germany) was attached using superglue (Supergel, UHU GmbH & Co. KG, Germany). The weight of the tag accounted for only 1% of the mean weight of all tested bees (231.84 ± 52.62 mg, analytic balance A210P-OD1, Sartorius AG, Germany), significantly lighter than typical pollen and nectar loads carried by foragers (up to 90% of body weight [28]). Therefore, we assume that this tagging method is unlikely to significantly affect flight performance as measured here. These metal tags were colour coded to differentiate cohorts. For reliable identification of each individual bee, the middle legs were colour coded using water-based permanent paint markers (5M Uni-Posca, Mitsubishi pencil, Japan). Afterwards, tagged bees were placed in a separate plastic cup for 30-60 min before returning them to their natal colony. Although precautions were taken to guarantee that attached tags would not interfere with wing movements, tag positions were measured using a digital microscope (figure S1a, CHX-500F, Keyence GmbH, Germany) on frozen specimens at the end of the experiment. The tag deviation from the centre between the tegulae was calculated, but tag position did not significantly affect their flight performance (figure S1b,c). Flight mill setup We used four tethered air flight mills (figure S2a), similar to previously described setups and methods [25, 41, 42]. The core of each flight mill is a lightweight and counter-weighted arm (length 32 cm) that floats by magnetic levitation and a needle that is inserted into a low-friction Teflon bearings at the centre of the arm. Individual bees were attached to a magnet (Æ 2 mm, 4 mm long) on one end of the arm and counter-balanced on the other arm, enabling tethered flights with their own power. An optical sensor transmitted a voltage pulse every half rotation (flight distance of 50 cm), recorded to a PC using the software guiBee [44]. Data extraction and calculations of flight distance, duration, and speeds were executed using the RScript FlightMillDataExtraction [45] in R (version 4.3.0). Each flight mill was positioned at the centre of a plastic cylinder (Æ 46 cm), keeping about 7 cm distance between the bee and the cylinder wall. The inside was decorated with 2.5 cm wide black and white vertical stripes continuously printed on paper to provide consistent visual feedback [46]. The walls also prevented interference from neighbouring flight mills and reduced any potential impacts of air currents [47]. The flight mills were illuminated by four flicker free daylight-like LEDs (each 2400 lm, CRI98, 5500 K, True-Light International GmbH, Germany) from a height of 70 cm. Flight trials Marked workers were gently collected from each colony in the morning using tweezers at the age 7, 14 and 21 days (i.e. each bee was repeatedly measured). They were kept separately per colony in a metal cage (9.5 x 8.5 x 5 cm) with ad libitum access to 40% w/v sucrose solution. After collection, workers were individually separated into flat-bottom glass vials (10 mL, 50 x 22 mm) with mesh lids, containing a 45 x 15 mm piece of cardboard to absorb any faeces. For 20 min each bee was individually fed with 40% w/v sucrose solution to satiation through the mesh of the lid. Subsequently, individual bees were weighed (d = 0.1 mg, analytic balance A210P-OD1, Sartorius AG, Germany) and attached to the magnet on the one side of the flight mill arm and kept in place on a launch platform. A counterweight (to ± 10 mg) was attached on the other side of the flight mill arm. The bees were then allowed to calm down and rest in the dark for 20 min by covering each flight mill with thick cardboard. Each bee was then positioned in the direction of flight and the launch platform was quickly removed to initiate flight (figure S2b,c; video S1). When bees stop flying, they would raise their wings and extend their legs, which resulted in a slowing of flight mill rotation. In addition to direct observations, stops can be detected in the raw data using our R script described above [45]. When a bee stopped flying (or did not initiate flight), it was allowed to rest on a handheld plastic Petri dish (Æ 46 cm) for approximately 20 s. Each bee was allowed 4 stops, i.e. 5 flight starts. We decided to allow multiple stops and pool individual flight data based on field observations showing that foragers periodically pause [48], and previous flight mill studies indicating that flight durations tend to be short after three stops [25, 49]. Our pilot study was consistent with those findings. To avoid biases in our dataset, we refrained from setting minimum or maximum flight durations or distances, even if a bee would not fly (e.g. one bee aged 21 days flew 0 m; see data in electronic supplementary material). The temperature during all flight was recorded at 5 min intervals (RC-5 temperature data logger, Elitech Ldt., UK) to calculate the average flight temperature and account for slight room temperature differences. At the end of each flight trial, bees returned to their natal colonies. After the last flight trial at age of 21 d, bees were frozen and stored at -20°C until further analysis. Measuring intertegular distance and dry mass In addition to the evaluation of the tag position, the intertegular distance (ITD) for each bee was measured using a digital microscope (figure S1a, CHX-500F, Keyence GmbH, Germany). The ITD serves as a proxy for workers body size, along with their dry weight (figure S1b) [50]. Prior to measuring their dry weight, the sternites (ventral abdominal segments) of each individual bee were cut open from the stinger to the fourth sternite without damaging their guts. The bees were then individually dried at 60°C for 3 d in drying cabinet (U40, Memmert GmbH & Co. KG, Germany) and subsequently weighed (d = 0.1 mg, analytic balance M-Pact AX224, Sartorius GmbH, Germany). Statistical analyses All statistical analyses and data visualizations were performed using R version 4.3.2 [51]. The complete code and output are provided in the electronic supplementary material. Briefly, to analyse the effects of the fixed factors age and body size (using dry mass as a proxy, figure S1b) as fixed factors

The elemental composition of lake sediment cores is often the result of several independent processes. In this study we attempt to extract statistically independent climate related signals from µ-XRF multi element data of a core drilled from Lake Hazar in Eastern Anatolia, using the independent component analysis (ICA) method. In addition, we analysed ostracod shells for oxygen and carbon isotopes. The ICA method has advantages over traditional dimension reduction methods, such as principal component analysis or factor analysis, because it is based on maximal statistical independence rather than uncorrelatedness, where independence is a stronger property. The Hz11-P03 core, which represents the last 17.3 ka, was recovered from Lake Hazar which, at times, formed the headwaters of the Tigris. Applying the ICA method, we selected two out of six independent components by measuring distance correlation similarity. We propose that one of the selected components can be read as a proxy for temperature and the other for precipitation in this region. Our results indicate that the region was relatively cold and wet during the late glacial, between 17.3 and 14.8 ka BP, and wet and warm during Bølling-Allerød. The lake level dropped below today's level during the Younger Dryas stadial (12.49 and 11.76 ka BP), forming a marked hiatus in the core's stratigraphic record. During the beginning of the Holocene, while precipitation values were high, the temperature gradually increased until 8 ka BP. Between 8 and 5 ka BP, the region was warm but extremely dry. After 5 ka BP, around 3.5 ka BP temperatures suddenly fell, and three abrupt dry phases are observed around 3.5 ka and 2.8 ka and 1.8 ka BP.

Most research on urban avian ecology has focused on population- and community-level phenomena, whereas fewer studies have examined how urbanization affects individual behavioral responses to a sudden and novel stimulus, and how those translate to fitness. We measured between-individual variation in provisioning latency in two urban adapters - great tits and blue tits - in response to an infrared camera installed in the nestbox, encountered when offspring in the nest were at the peak of food demand (9–10-days old). For each nestbox, we quantified urbanization as intensity in human activity, distance to road and proportion of impervious surface area. In both species, provisioning latency increased significantly closer to roads. Moreover, increased provisioning latency when exposed to a novel object was associated with higher reproductive success in great tits whose nestboxes were surrounded by high amounts of impervious surface. In contrast, increased provisioning latency was consistently associated with lower reproductive success in blue tits. Our results suggest that provisioning latency changes in relation to the environment surrounding the nest, and may be context- and species-specific when exposed to a novel stimulus, such as a novel object in the nest. To better understand the role of initial behavioral responses towards novelty across an individual's lifetime and, ultimately, its impact on fitness in the urban mosaic, further research explicitly testing different behavioral responses across the entire breeding cycle in wild model systems is needed. Methods 2.2 Breeding data In both years, nestboxes were inspected at weekly intervals from mid-March until the end of June to determine first-egg date and clutch size. Hatching date (recorded as day 1) was determined by checking the nestboxes one day before the expected hatching (c. 12 days after the last egg of the clutch was laid), and every two days after that, until hatching occurred. When nestlings were between 9 and 10 days of age, infrared-cameras were installed inside the nestboxes as part of a project investigating avian diet across the urban mosaic (Corsini et al., in prep). Parents were caught, measured and ringed when nestlings were ca. 11-13 days old, and aged on plumage characteristics as first-year breeders (First year) or older individuals (Older). At day 15, all nestlings were measured and ringed. Ca. 25 days after hatching, nestboxes were checked to record the number of nestlings that died before fledging, and to calculate fledging success as the proportion of eggs producing fledglings. 2.3 Measuring individual neophobia Neophobia was measured as an individual’s latency time to first entrance (FE) following its first approach (FA) of the nestbox after installing the infrared camera (see Figure 2). To account for the influence of disrupting factors in the nestbox surroundings, we also recorded the number of times an individual flew off before its first entrance (Number of flights). The infrared-cameras (model SONY HK100427) were installed in the morning (between 6:26 and 9:33 AM) when nestlings were 9 or 10 days old. The time (in minutes) between each bird’s first approach (FA, Figure 2, Figure S2) towards the camera (i.e., standing or flying next to the nestbox entrance, facing the camera) and its first entrance (FE, Figure 2, Figure S2), was used as a proxy of neophobia for comparison with previous studies (see 37,50). Because of clear plumage differences between male and female great tit, we could record first entrances separately for each parent. As this was not feasible for blue tits – infrared cameras recorded black and white videos – we noted latency only for the first bird entering the nestbox. Total number of videorecorded nestboxes by species, study site and year is reported in Table S1. 2.4 Characterizing urbanization We characterized the amount of urbanization surrounding each nestbox by measuring human or canine presence within 15m, distance to the closest road and percent area covered by impervious surface (i.e., % ISA). Briefly, human presence was obtained from repeated surveys on the ground aimed at detecting all humans and dogs within a 15m radius around each nestbox during 30 seconds long counts. This value was then standardized by dividing the total amount of humans and dogs detected by the number of counts performed within each study site (detailed in 48). The distance to the closest road (i.e., for vehicular use) was recorded in meters using the distance matrix tool in QGIS (see methods in 49). Percentage of ISA was derived from opensource remote sensing imagery data (Copernicus Land Monitoring, https://land.copernicus.eu/sitemap); specifically, we used the “imperviousness” layer - which included all built up areas and was quantified using a 20m-pixel resolution raster file - averaged in a 100m radius around each nestbox in QGIS using the Zonal statistics tool, as detailed in 51. A Principal Component Analysis on these three proxies of urbanization after their standardization, revealed that PC1 explained 60% of the variance. PC1 comprehended the variables Distance to Road and ISA as main contributors (Figure S3a and b). 2.5 Weather data Weather data over the two-year period were provided by the Polish Institute of Meteorology and Water Management (IMGW – PIB). Daily temperature (°C) and rainfall (mm) were derived from two climatic stations: Warsaw Okecie and Legionowo (here used as references for study sites located within and outside the city, respectively). For each nest, we used the average-daily temperature and the rainfall on the date of recording. 2.6 Statistical analyses All statistics were computed in R (v. 3.6.2, 52), R-packages and functions are detailed below. Figures and plots were generated through the R-package “ggplot2” (v.3.2.1 53) and the opensource software Inkscape (v. 1.0.2 54). In the full dataset, two great tit nests (4 birds) and one blue tit nest (1 bird) were identified by ring numbers as second broods, thus, excluded from all the analyses, as birds were already exposed to the camera from the first breeding attempt that occurred in the same year. As in great tits, the second bird of the pair was often influenced by the partner while approaching the camera (i.e., the second bird entered the nestbox faster after the first one did, MC and PL personal obs., Figure S4), the behavioral dataset of both species was restricted to birds that accessed the nestbox first. Statistical analyses were performed as follows: 1) We used the Test-Retest approach to infer whether neophobia was consistent within individuals measured in two distinct breeding years. Out of all birds measured, 9 banded great tits were caught in 2018 and 2019 (N = 18 observations). 2) We used a series of One-Way ANOVAs to test whether neophobia (in min) differed between species, age-classes and sexes. We subsequently tested species-specific differences in terms of their exposure to human presence, distance to roads and % of ISA in the nestbox surroundings. We further tested whether neophobia was associated with the number of times a bird flew away from the nestbox before the first entrance. 3) To test whether neophobia is mediated by urbanization, we built two distinct Linear Mixed – Effects Models (LMMs, “lmer” function in the R-package “lme4”, v. 1.1-21) where both species were analyzed jointly, and were characterised by the same model structure except for the proxy of urbanization, which was fitted as explanatory variable as either Distance to Road or ISA. In each model, Neophobia was log-transformed to meet models’ assumption and fitted as the response variable, while the variables: Distance to Road (or ISA), Human presence, rainfall in mm (Rain), Temperature and Date of Recording (1st of April coded as 1), were fitted as continuous predictors, and the variables Species and Year as two-levels factors. Site was set as random effect to control for non-independence of nestboxes sampled within the same study area. Because the variables Temperature and Date of recording were positively correlated (rPearson (113) = 0.67, p < 0.001), Temperature was removed from the global models. We used the vif function in the R-package “car” (v. 3.0-11) 55 to check possible multicollinearity issues between continuous predictors. Despite the fact that Variance Inflation Factors (VIF) were below 2 for Distance to Road and ISA, we detected a strong negative correlation between these two terms (r = -0.6; see Pearson’s correlation reported in Table S2). Consequently, these two proxies of urbanization were always analyzed separately; in contrast, because Human presence was only weakly correlated to ISA and Distance to Road (r = 0.4 and r = -0.2, respectively, see Figure S3a and Table S2), it was fitted as continuous predictor in each global model. Models comparing the separate effects of Distance to Road and ISA on neophobia were also used because roads may have a major impact on wildlife (e.g., via habitat loss, but also due to the increased levels of sound, light and chemical pollution associated to them, which may extend far away from their location 56). Thus, analyses and results relative to each proxy are reported and discussed in parallel. We performed backward model selection in each global model (via the step function in the R-package lmerTest v. 3.1-3 57). We used the DHARMA package to check the global models fit and assumptions 58 (global model diagnostics shown in Figure S5). 4) To test whether neophobia in parents was associated with reproductive success and whether this relationship was mediated by urbanization (here, again, separately tested as Distance to Road or ISA), analyses were run for the two species separately, to account for species-specific differences in life-history. We ran Generalized Linear Mixed-Effects Models (GLMMs, using the glmer function in lme4), where the number of