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  1. Abstract

    Rapid advances in the field of movement ecology have led to increasing insight into both the population‐level abundance patterns and individual‐level behaviour of migratory species. Despite this progress, research questions that require scaling individual‐level understanding of the behaviour of migrating organisms to the population level remain difficult to investigate.

    To bridge this gap, we introduce a generalizable framework for training full‐annual cycle individual‐based models of migratory movements by combining information from tracking studies and species occurrence records. Focusing on migratory birds, we call this method: Models of Individual Movement of Avian Species (MIMAS). We implement MIMAS to design individual‐based models of avian migration that are trained using previously published weekly occurrence maps and fit via Approximate Bayesian Computation.

    MIMAS models leverage individual‐ and population‐level information to faithfully represent continental‐scale migration patterns. Models can be trained successfully for species even when little existing individual‐level data is available for parameterization by relying on population‐level information. In contrast to existing mathematical models of migration, MIMAS explicitly represents and estimates behavioural attributes of migrants. MIMAS can additionally be used to simulate movement over consecutive migration seasons, and models can be easily updated or validated as new empirical data on migratory behaviours becomes available.

    MIMAS can be applied to a variety of research questions that require representing individual movement at large scales. We demonstrate three applied uses for MIMAS: estimating population‐specific migratory phenology, predicting the spatial patterns and magnitude of ectoparasite dispersal by migrants, and simulating the spread of a pathogen across the annual cycle of a migrant species. Currently, MIMAS can easily be used to build models for hundreds of migratory landbird species but can also be adapted in the future to build models of other types of migratory animals.

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  2. Abstract

    Rare birds known as “accidentals” or “vagrants” have long captivated birdwatchers and puzzled biologists, but the drivers of these rare occurrences remain elusive. Errors in orientation or navigation are considered one potential driver: migratory birds use the Earth’s magnetic field—sensed using specialized magnetoreceptor structures—to traverse long distances over often unfamiliar terrain. Disruption to these magnetoreceptors or to the magnetic field itself could potentially cause errors leading to vagrancy. Using data from 2 million captures of 152 landbird species in North America over 60 years, we demonstrate a strong association between disruption to the Earth’s magnetic field and avian vagrancy during fall migration. Furthermore, we find that increased solar activity—a disruptor of the avian magnetoreceptor—generally counteracts this effect, potentially mitigating misorientation by disabling the ability for birds to use the magnetic field to orient. Our results link a hypothesized cause of misorientation to the phenomenon of avian vagrancy, further demonstrating the importance of magnetoreception among the orientation mechanisms of migratory birds. Geomagnetic disturbance may have important downstream ecological consequences, as vagrants may experience increased mortality rates or facilitate range expansions of avian populations and the organisms they disperse.

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  3. Abstract

    Data availability limits phenological research at broad temporal and spatial extents. Butterflies are among the few taxa with broad-scale occurrence data, from both incidental reports and formal surveys. Incidental reports have biases that are challenging to address, but structured surveys are often limited seasonally and may not span full flight phenologies. Thus, how these data source compare in phenological analyses is unclear. We modeled butterfly phenology in relation to traits and climate using parallel analyses of incidental and survey data, to explore their shared utility and potential for analytical integration. One workflow aggregated “Pollard” surveys, where sites are visited multiple times per year; the other aggregated incidental data from online portals: iNaturalist and eButterfly. For 40 species, we estimated early (10%) and mid (50%) flight period metrics, and compared the spatiotemporal patterns and drivers of phenology across species and between datasets. For both datasets, inter-annual variability was best explained by temperature, and seasonal emergence was earlier for resident species overwintering at more advanced stages. Other traits related to habitat, feeding, dispersal, and voltinism had mixed or no impacts. Our results suggest that data integration can improve phenological research, and leveraging traits may predict phenology in poorly studied species.

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  4. Abstract

    Insect phenological lability is key for determining which species will adapt under environmental change. However, little is known about when adult insect activity terminates and overall activity duration. We used community‐science and museum specimen data to investigate the effects of climate and urbanisation on timing of adult insect activity for 101 species varying in life history traits. We found detritivores and species with aquatic larval stages extend activity periods most rapidly in response to increasing regional temperature. Conversely, species with subterranean larval stages have relatively constant durations regardless of regional temperature. Species extended their period of adult activity similarly in warmer conditions regardless of voltinism classification. Longer adult durations may represent a general response to warming, but voltinism data in subtropical environments are likely underreported. This effort provides a framework to address the drivers of adult insect phenology at continental scales and a basis for predicting species response to environmental change.

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  5. Abstract

    A wave of green leaves and multi‐colored flowers advances from low to high latitudes each spring. However, little is known about how flowering offset (i.e., ending of flowering) and duration of populations of the same species vary along environmental gradients. Understanding these patterns is critical for predicting the effects of future climate and land‐use change on plants, pollinators, and herbivores. Here, we investigated potential climatic and landscape drivers of flowering onset, offset, and duration of 52 plant species with varying key traits. We generated phenology estimates using >270,000 community‐science photographs and a novel presence‐only phenometric estimation method. We found longer flowering durations in warmer areas, which is more obvious for summer‐blooming species compared to spring‐bloomers driven by their strongly differing offset dynamics. We also found that higher human population density and higher annual precipitation are associated with delayed flowering offset and extended flowering duration. Finally, offset of woody perennials was more sensitive than herbaceous species to both climate and urbanization drivers. Empirical forecast models suggested that flowering durations will be longer in 2030 and 2050 under representative concentration pathway (RCP) 8.5, especially for summer‐blooming species. Our study provides critical insight into drivers of key flowering phenophases and confirms that Hopkins’ Bioclimatic Law also applies to flowering durations for summer‐blooming species and herbaceous spring‐blooming species.

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  6. Abstract

    Phenology is one of the most immediate responses to global climate change, but data limitations have made examining phenology patterns across greater taxonomic, spatial and temporal scales challenging. One significant opportunity is leveraging rapidly increasing data resources from digitized museum specimens and community science platforms, but this assumes reliable statistical methods are available to estimate phenology using presence‐only data. Estimating the onset or offset of key events is especially difficult with incidental data, as lower data densities occur towards the tails of an abundance distribution.

    The Weibull distribution has been recognized as an appropriate distribution to estimate phenology based on presence‐only data, but Weibull‐informed estimators are only available for onset and offset. We describe the mathematical framework for a new Weibull‐parameterized estimator of phenology appropriate for any percentile of a distribution and make it available in anrpackage,phenesse. We use simulations and empirical data on open flower timing and first arrival of monarch butterflies to quantify the accuracy of our estimator and other commonly used phenological estimators for 10 phenological metrics: onset, mean and offset dates, as well as the 1st, 5th, 10th, 50th, 90th, 95th and 99th percentile dates. Root mean squared errors and mean bias of the phenological estimators were calculated for different patterns of abundance and observation processes.

    Results show a general pattern of decay in performance of estimates when moving from mean estimates towards the tails of the seasonal abundance curve, suggesting that onset and offset continue to be the most difficult phenometrics to estimate. However, with simple phenologies and enough observations, our newly developed estimator can provide useful onset and offset estimates. This is especially true for the start of the season, when incidental observations may be more common.

    Our simulation demonstrates the potential of generating accurate phenological estimates from presence‐only data and guides the best use of estimators. The estimator that we developed, phenesse, is the least biased and has the lowest estimation error for onset estimates under most simulated and empirical conditions examined, improving the robustness of these estimates for phenological research.

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  7. Abstract

    Understanding patterns of diversity is central to ecology and conservation, yet estimates of diversity are often biased by imperfect detection. In recent years, multi‐species occupancy models (MSOM) have been developed as a statistical tool to account for species‐specific heterogeneity in detection while estimating true measures of diversity. Although the power of these models has been tested in various ways, their ability to estimate gamma diversity—or true community size,Nis a largely unrecognized feature that needs rigorous evaluation.

    We use both simulations and an empirical dataset to evaluate the bias, precision, accuracy and coverage of estimates ofNfrom MSOM compared to the widely applied iChao2 non‐parametric estimator. We simulated 5,600 datasets across seven scenarios of varying average occupancy and detectability covariates, as well as varying numbers of sites, replicates and true community size. Additionally, we use a real dataset of surveys over 9 years (where species accumulation reached an asymptote, indicating trueN), to estimateNfrom each annual survey.

    Simulations showed that both MSOM and iChao2 estimators are generally accurate (i.e. unbiased and precise) except under unideal scenarios where mean species occupancy is low. In such scenarios, MSOM frequently overestimatedN. Across all scenarios, MSOM estimates were less certain than iChao2, but this led to over‐confident iChao2 estimates that showed poor coverage. Results from the real dataset largely confirmed the simulation findings, with MSOM estimates showing greater accuracy and coverage than iChao2.

    Community ecologists have a wide choice of analytical methods, and both iChao2 and MSOM estimates ofNare substantially preferable to raw species counts. The simplicity of non‐parametric estimators has obvious advantages, but our results show that in many cases, MSOM may provide superior estimates that also account more accurately for uncertainty. Both methods can show strong bias when average occupancy is very low, and practitioners should show caution when using estimates derived from either method under such conditions.

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  8. Abstract

    Climate variation has been linked to historical and predicted future distributions and dynamics of wildlife populations. However, demographic mechanisms underlying these changes remain poorly understood. Here, we assessed variation and trends in climate (annual snowfall and spring temperature anomalies) and avian demographic variables from mist‐netting data (breeding phenology and productivity) at six sites along an elevation gradient spanning the montane zone of Yosemite National Park between 1993 and 2017. We implemented multi‐species hierarchical models to relate demographic responses to elevation and climate covariates. Annual variation in climate and avian demographic variables was high. Snowfall declined (10 mm/year at the highest site, 2 mm at the lowest site), while spring temperature increased (0.045°C/year) over the study period. Breeding phenology (mean first capture date of juvenile birds) advanced by 0.2 day/year (5 days); and productivity (probability of capturing a juvenile bird) increased by 0.8%/year. Breeding phenology was 12 days earlier at the lowest compared to highest site, 18 days earlier in years with lowest compared to highest snowfall anomalies, and 6 d earlier in relatively warm springs (after controlling for snowfall effects). Productivity was positively related to elevation. However, elevation–productivity responses varied among species; species with higher productivity at higher compared to lower elevations tended to be species with documented range retractions during the past century. Productivity tended to be negatively related to snowfall and was positively related to spring temperature. Overall, our results suggest that birds have tracked the variable climatic conditions in this system and have benefited from a trend toward warmer, drier springs. However, we caution that continued warming and multi‐year drought or extreme weather years may alter these relationships in the future. Multi‐species demographic modeling, such as implemented here, can provide an important tool for guiding conservation of species assemblages under global change.

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  9. Recent reports of insect declines have raised concerns about the potential for concomitant losses to ecosystem processes. However, understanding the causes and consequences of insect declines is challenging, especially given the data deficiencies for most species. Needed are approaches that can help quantify the magnitude and potential causes of declines at levels above species. Here we present an analytical framework for assessing broad‐scale plant–insect phenologies and their relationship to community‐level insect abundance patterns. We intentionally apply a species‐neutral approach to analyse trends in phenology and abundance at the macroecological scale. Because both phenology and abundance are critical to ecosystem processes, we estimate aggregate metrics using the overwintering (diapause) stage, a key species trait regulating phenology and environmental sensitivities. This approach can be used across broad spatiotemporal scales and multiple taxa, including less well‐studied groups. Using community (‘citizen’) science butterfly observations from multiple platforms across the Eastern USA, we show that the relationships between environmental drivers, phenology and abundance depend on the diapause stage. In particular, egg‐diapausing butterflies show marked changes in adult‐onset phenology in relation to plant phenology and are rapidly declining in abundance over a 20‐year span across the study region. Our results also demonstrate the negative consequences of warmer winters for the abundance of egg‐diapausing butterflies, irrespective of plant phenology. In sum, the diapause stage strongly shapes both phenological sensitivities and developmental requirements across seasons, providing a basis for predicting the impacts of environmental change across trophic levels. Utilizing a framework that ties thermal performance across life stages in relation to climate and lower‐trophic‐level phenology provides a critical step towards predicting changes in ecosystem processes provided by butterflies and other herbivorous insects into the future. 
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    Free, publicly-accessible full text available May 1, 2025
  10. Abstract Studies of long-term trends in phenology often rely on climatic averages or accumulated heat, overlooking climate variability. Here we test the hypothesis that unusual weather conditions are critical in driving adult insect phenology. First, we generate phenological estimates for Lepidoptera (moths and butterflies) across the Eastern USA, and over a 70 year period, using natural history collections data. Next, we assemble a set of predictors, including the number of unusually warm and cold days prior to, and during, the adult flight period. We then use phylogenetically informed linear mixed effects models to evaluate effects of unusual weather events, climate context, species traits, and their interactions on flight onset, offset and duration. We find increasing numbers of both warm and cold days were strong effects, dramatically increasing flight duration. This strong effect on duration is likely driven by differential onset and termination dynamics. For flight onset, impact of unusual climate conditions is dependent on climatic context, but for flight cessation, more unusually cold days always lead to later termination particularly for multivoltine species. These results show that understanding phenological responses under global change must account for unusual weather events, especially given they are predicted to increase in frequency and severity. 
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    Free, publicly-accessible full text available December 1, 2024