Plant species composition is changing across many landscapes, but it is unclear how these changes affect habitat quality for animals. We used functional diversity and community-weighted mean (CWM) trait values for four plant traits (litter N, P, lignin and soluble phenolics) to explore how changes in plant species composition may affect larval amphibians in a simplified aquatic ecosystem. We predicted that increased functional diversity would improve amphibian performance (survivorship, developmental rate, and size). We also predicted that increases in CWM N and P would improve amphibian performance, while increases in CWM lignin and soluble phenolics would have negative effects on amphibian performance. We did not detect an effect of functional diversity; instead, CWM litter N and soluble phenolics were useful predictors of amphibian performance. We demonstrate that quantifying the CWM of ecologically relevant traits represents a powerful approach for predicting how changes in plant species composition can affect aquatic communities.

Decreased plant diversity is expected to reduce ecosystem function. Although many studies have examined eff ects of plant species on trophic interactions, information regarding eff ects of native or non-native plant diversity on performance of individuals of higher trophic levels is limited. We reared larval American toad Anaxyrus americanus tadpoles in outdoor mesocosms containing litter of 1, 3, 6 or 12 plant species drawn randomly from a pool of 24 (15 native, 9 nonnative) species. Tadpole performance varied signifi cantly among litter types in single litter treatments and pH and litter C:N were signifi cant predictors of tadpole performance. Metamorphs were larger in mixtures than expected based on performance in single species treatments, suggesting a non-additive eff ect of diversity. Litter diversity did not aff ect probability of survival or probability of metamorphosis. Plant origin (native or non-native) had no signifi cant eff ect on amphibian performance. Our study suggests some benefi ts to tadpole development at low levels of plant diversity, but questions assumed benefi ts of increased plant diversity and assumed detrimental eff ects of nonnative plant species for a common larval amphibian. Presence of specifi c plant species with strong negative eff ects on tadpole performance may outweigh diversity benefi ts in brown food webs.

Abstract. Varved lake sediments are exceptional archives of paleoclimatic information due to their precise chronological control and annual resolution. However, quantitative paleoclimate reconstructions based on the biogeochemical composition of biochemical varves are extremely rare, mainly because the climate–proxy relationships are complex and obtaining biogeochemical proxy data at very high (annual) resolution is difficult. Recent developments in high-resolution hyperspectral imaging (HSI) of sedimentary pigment biomarkers combined with micro X-ray fluorescence (µXRF) elemental mapping make it possible to measure the structure and composition of varves at unprecedented resolution. This provides opportunities to explore seasonal climate signals preserved in biochemical varves and, thus, assess the potential for annual-resolution climate reconstruction from biochemical varves. Here, we present a geochemical dataset including HSI-inferred sedimentary pigments and µXRF-inferred elements at very high spatial resolution (60 µm, i.e. > 100 data points per varve year) in varved sediments of Lake Żabińskie, Poland, over the period 1966–2019 CE. We compare these data with local meteorological observations to explore and quantify how changing seasonal meteorological conditions influenced sediment composition and varve formation processes. Based on the dissimilarity of within-varve multivariate geochemical time series, we classified varves into four types. Multivariate analysis of variance shows that these four varve types were formed in years with significantly different seasonal meteorological conditions. Generalized additive models (GAMs) were used to infer seasonal climate conditions based on sedimentary variables. Spring and summer (MAMJJA) temperatures were predicted using Ti and total C (Radj2=0.55; cross-validated root mean square error (CV-RMSE) = 0.7 ∘C, 14.4 %). Windy days from March to December (mean daily wind speed > 7 m s−1) were predicted using mass accumulation rate (MAR) and Si (Radj2=0.48; CV-RMSE = 19.0 %). This study demonstrates that high-resolution scanning techniques are promising tools to improve our understanding of varve formation processes and climate–proxy relationships in biochemical varves. This knowledge is the basis for quantitative high-resolution paleoclimate reconstructions, and here we provide examples of calibration and validation of annual-resolution seasonal weather inference from varve biogeochemical data.