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Phenotypic plasticity allows organisms to adjust the timing of life‐history events in response to environmental and demographic conditions. Shifts by individuals in the timing of breeding with respect to variation in age and temperature are well documented in nature, and these changes are known to scale to affect population dynamics. However, relatively little is known about how organisms alter phenology in response to other demographic and environmental factors. We investigated how pre‐breeding temperature, breeding population density, age, and rainfall in the first month of life influenced the timing and plasticity of lay date in a population of Savannah Sparrows (Passerculus sandwichensis) monitored over 33 yr (1987–2019). Females that experienced warmer pre‐breeding temperatures tended to lay eggs earlier, as did older females, but breeding population density had no effect on lay date. Natal precipitation interacted with age to influence lay date plasticity, with females that experienced high precipitation levels as nestlings advancing lay dates more strongly over the course of their lives. We also found evidence for varied pace of life; females that experienced high natal precipitation had shorter lifespans and reduced fecundity, but more nesting attempts over their lifetimes. Rainfall during the nestling period increased through time, while population density and fecundity declined, suggesting that increased precipitation on the breeding grounds may be detrimental to breeding females and ultimately the viability of the population as a whole. Our results suggest that females adjust their laying date in response to pre‐breeding temperature, and as they age, while presenting new evidence that environmental conditions during the natal period can affect phenological plasticity and generate downstream, population‐level effects.

 

Abstract. Solute concentrations in stream water vary with discharge in patterns that record complex feedbacks between hydrologic and biogeochemical processes. In a comparison of three shale-underlain headwater catchments located in Pennsylvania, USA (the forested Shale Hills Critical Zone Observatory), and Wales, UK (the peatland-dominated Upper Hafren and forest-dominated Upper Hore catchments in the Plynlimon forest), dissimilar concentration–discharge (C–Q) behaviors are best explained by contrasting landscape distributions of soil solution chemistry – especially dissolved organic carbon (DOC) – that have been established by patterns of vegetation and soil organic matter (SOM). Specifically, elements that are concentrated in organic-rich soils due to biotic cycling (Mn, Ca, K) or that form strong complexes with DOC (Fe, Al) are spatially heterogeneous in pore waters because organic matter is heterogeneously distributed across the catchments. These solutes exhibit non-chemostatic behavior in the streams, and solute concentrations either decrease (Shale Hills) or increase (Plynlimon) with increasing discharge. In contrast, solutes that are concentrated in soil minerals and form only weak complexes with DOC (Na, Mg, Si) are spatially homogeneous in pore waters across each catchment. These solutes are chemostatic in that their stream concentrations vary little with stream discharge, likely because these solutes are released quickly from exchange sites in the soils during rainfall events. Furthermore, concentration–discharge relationships of non-chemostatic solutes changed following tree harvest in the Upper Hore catchment in Plynlimon, while no changes were observed for chemostatic solutes, underscoring the role of vegetation in regulating the concentrations of certain elements in the stream. These results indicate that differences in the hydrologic connectivity of organic-rich soils to the stream drive differences in concentration behavior between catchments. As such, in catchments where SOM is dominantly in lowlands (e.g., Shale Hills), we infer that non-chemostatic elements associated with organic matter are released to the stream early during rainfall events, whereas in catchments where SOM is dominantly in uplands (e.g., Plynlimon), these non-chemostatic elements are released later during rainfall events. The distribution of SOM across the landscape is thus a key component for predictive models of solute transport in headwater catchments.

 

Solute concentrations in stream water vary with discharge in patterns that record complex feedbacks between hydrologic and biogeochemical processes. In a comparison of headwater catchments underlain by shale in Pennsylvania, USA (Shale Hills) and Wales, UK (Plynlimon), dissimilar concentration-discharge behaviors are best explained by contrasting landscape distributions of soil solution chemistry – especially dissolved organic carbon (DOC) – that have been established by patterns of vegetation. Specifically, elements that are concentrated in organic-rich soils due to biotic cycling (Mn, Ca, K) or that form strong complexes with DOC (Fe, Al) are spatially heterogeneous in pore waters because organic matter is heterogeneously distributed across the catchments. These solutes exhibit non-chemostatic "bioactive" behavior in the streams, and solute concentrations either decrease (Shale Hills) or increase (Plynlimon) with increasing discharge. In contrast, solutes that are concentrated in soil minerals and form only weak complexes with DOC (Na, Mg, Si) are spatially homogeneous in pore waters across each catchment. These solutes are chemostatic in that their stream concentrations vary little with stream discharge, likely because these solutes are released quickly from exchange sites in the soils during rainfall events. Differences in the hydrologic connectivity of organic-rich soils to the stream drive differences in concentration behavior between catchments. As such, in catchments where soil organic matter (SOM) is dominantly in lowlands (e.g., Shale Hills), bioactive elements are released to the stream early during rainfall events, whereas in catchments where SOM is dominantly in uplands (e.g., Plynlimon), bioactive elements are released later during rainfall events. The distribution of vegetation and SOM across the landscape is thus a key component for predictive models of solute transport in headwater catchments. 

Addressing population declines of migratory insects requires linking populations across different portions of the annual cycle and understanding the effects of variation in weather and climate on productivity, recruitment, and patterns of long‐distance movement. We used stable H and C isotopes and geospatial modeling to estimate the natal origin of monarch butterflies (Danaus plexippus) in eastern North America using over 1000 monarchs collected over almost four decades at Mexican overwintering colonies. Multinomial regression was used to ascertain which climate‐related factors best‐predicted temporal variation in natal origin across six breeding regions. The region producing the largest proportion of overwintering monarchs was theUSMidwest (mean annual proportion = 0.38; 95%CI: 0.36–0.41) followed by the north‐central (0.17; 0.14–0.18), northeast (0.15; 0.11–0.16), northwest (0.12; 0.12–0.16), southwest (0.11; 0.08–0.12), and southeast (0.08; 0.07–0.11) regions. There was no evidence of directional shifts in the relative contributions of different natal regions over time, which suggests these regions are comprising the same relative proportion of the overwintering population in recent years as in the mid‐1970s. Instead, interannual variation in the proportion of monarchs from each region covaried with climate, as measured by the Southern Oscillation Index and regional‐specific daily maximum temperature and precipitation, which together likely dictate larval development rates and food plant condition. Our results provide the first robust long‐term analysis of predictors of the natal origins of monarchs overwintering in Mexico. Conservation efforts on the breeding grounds focused on the Midwest region will likely have the greatest benefit to eastern North American migratory monarchs, but the population will likely remain sensitive to regional and stochastic weather patterns.

 

Variation in movement across time and space fundamentally shapes the abundance and distribution of populations. Although a variety of approaches model structured population dynamics, they are limited to specific types of spatially structured populations and lack a unifying framework. Here, we propose a unified network‐based framework sufficiently novel in its flexibility to capture a wide variety of spatiotemporal processes including metapopulations and a range of migratory patterns. It can accommodate different kinds of age structures, forms of population growth, dispersal, nomadism and migration, and alternative life‐history strategies. Our objective was to link three general elements common to all spatially structured populations (space, time and movement) under a single mathematical framework. To do this, we adopt a network modeling approach. The spatial structure of a population is represented by a weighted and directed network. Each node and each edge has a set of attributes which vary through time. The dynamics of our network‐based population is modeled with discrete time steps. Using both theoretical and real‐world examples, we show how common elements recur across species with disparate movement strategies and how they can be combined under a unified mathematical framework. We illustrate how metapopulations, various migratory patterns, and nomadism can be represented with this modeling approach. We also apply our network‐based framework to four organisms spanning a wide range of life histories, movement patterns, and carrying capacities. General computer code to implement our framework is provided, which can be applied to almost any spatially structured population. This framework contributes to our theoretical understanding of population dynamics and has practical management applications, including understanding the impact of perturbations on population size, distribution, and movement patterns. By working within a common framework, there is less chance that comparative analyses are colored by model details rather than general principles.