Climate change impacts bird migration phenology,causing changes in departure and arrival dates,leading to potential mismatches between migration and other key seasonal constraints.While the impacts of climate change o...Climate change impacts bird migration phenology,causing changes in departure and arrival dates,leading to potential mismatches between migration and other key seasonal constraints.While the impacts of climate change on arrival at breeding grounds have been relatively well documented,little is known about the impacts of climate change on post-breeding migration,especially at stopover sites.Here we use long-term(11 years)banding data(11,118 captures)from 7 species at Mai Po Marshes Nature Reserve in Hong Kong,a key stopover site for migratory birds along the East Asian–Australasian Flyway,to describe long-term changes in migration phenology and to compare observed changes to annual weather variation.We also examine changes in wing length over a longer time period(1985–2020)as wing length often correlates positively with migration distance.We found that observed changes in migratory phenology vary by species;three species had later estimated arrival(by 1.8 days per year),peak(by 2.6 days per year)or departure(by 2.5 days per year),one showed an earlier peak date(by 1.8days per year)and two showed longer duration of passage(2.7 days longer and 3.2 days longer per year).Three species exhibited no long-term change in migration phenology.For two of the four species with shifting phenology,temperature was an important predictor of changing peak date,departure dates and duration of passage.Wing length was shorter in three species and longer in two species,but these changes did not correlate with observed phenological changes.The complex changes observed here are indicative of the challenges concerning the detection of climate change in migratory stopover sites.Continued monitoring and a better understanding of the dynamics of all sites in the migratory pathway will aid conservation of these species under global change.展开更多
Accurate understanding of global photosynthetic capacity(i.e.maximum RuBisCO carboxylation rate,Vc,max)variability is critical for improved simulations of terrestrial ecosystem photosynthesis metabolisms and carbon cy...Accurate understanding of global photosynthetic capacity(i.e.maximum RuBisCO carboxylation rate,Vc,max)variability is critical for improved simulations of terrestrial ecosystem photosynthesis metabolisms and carbon cycles with climate change,but a holistic understanding and assessment remains lacking.Here we hypothesized that V_(c,max)was dictated by both factors of temperature-associated enzyme kinetics(capturing instantaneous ecophysiological responses)and the amount of activated RuBisCO(indexed by V_(c,max)standardized at 25℃,V_(c,max25)),and compiled a comprehensive global dataset(n=7339 observations from 428 sites)for hypothesis testing.The photosynthesis data were derived from leaf gas exchange measurements using portable gas exchange systems.We found that a semi-empirical statistical model considering both factors explained 78%of global V_(c,max)variability,followed by 55%explained by enzyme kinetics alone.This statistical model outperformed the current theoretical optimality model for predicting global V_(c,max)variability(67%),primarily due to its poor characterization on global V_(c,max25)variability(3%).Further,we demonstrated that,in addition to climatic variables,belowground resource constraint on photosynthetic machinery built-up that directly structures the biogeography of V_(c,max25)was a key missing mechanism for improving the theoretical modelling of global V_(c,max)variability.These findings improve the mechanistic understanding of global V_(c,max)variability and provide an important basis to benchmark process-based models of terrestrial photosynthesis and carbon cycling under climate change.展开更多
基金Funding was provided by an RAE Improvement Grant to(TCB)from the Faculty of Science at The University of Hong Kong。
文摘Climate change impacts bird migration phenology,causing changes in departure and arrival dates,leading to potential mismatches between migration and other key seasonal constraints.While the impacts of climate change on arrival at breeding grounds have been relatively well documented,little is known about the impacts of climate change on post-breeding migration,especially at stopover sites.Here we use long-term(11 years)banding data(11,118 captures)from 7 species at Mai Po Marshes Nature Reserve in Hong Kong,a key stopover site for migratory birds along the East Asian–Australasian Flyway,to describe long-term changes in migration phenology and to compare observed changes to annual weather variation.We also examine changes in wing length over a longer time period(1985–2020)as wing length often correlates positively with migration distance.We found that observed changes in migratory phenology vary by species;three species had later estimated arrival(by 1.8 days per year),peak(by 2.6 days per year)or departure(by 2.5 days per year),one showed an earlier peak date(by 1.8days per year)and two showed longer duration of passage(2.7 days longer and 3.2 days longer per year).Three species exhibited no long-term change in migration phenology.For two of the four species with shifting phenology,temperature was an important predictor of changing peak date,departure dates and duration of passage.Wing length was shorter in three species and longer in two species,but these changes did not correlate with observed phenological changes.The complex changes observed here are indicative of the challenges concerning the detection of climate change in migratory stopover sites.Continued monitoring and a better understanding of the dynamics of all sites in the migratory pathway will aid conservation of these species under global change.
基金supported by National Natural Science Foundation of China(31922090 and 31901086)Hong Kong Research Grant Council Early Career Scheme(27306020)+4 种基金HKU Seed Fund for Basic Research(201905159005 and 202011159154)supported by the Innovation and Technology Fund(funding support to State Key Laboratories in Hong Kong of Agorobiotechnology)of the HKSAR,Chinasupported by the Carbon Mitigation Initiative of the Princeton Universitysupport from the National Science Foundation(DEB-2045968)Texas Tech University.
文摘Accurate understanding of global photosynthetic capacity(i.e.maximum RuBisCO carboxylation rate,Vc,max)variability is critical for improved simulations of terrestrial ecosystem photosynthesis metabolisms and carbon cycles with climate change,but a holistic understanding and assessment remains lacking.Here we hypothesized that V_(c,max)was dictated by both factors of temperature-associated enzyme kinetics(capturing instantaneous ecophysiological responses)and the amount of activated RuBisCO(indexed by V_(c,max)standardized at 25℃,V_(c,max25)),and compiled a comprehensive global dataset(n=7339 observations from 428 sites)for hypothesis testing.The photosynthesis data were derived from leaf gas exchange measurements using portable gas exchange systems.We found that a semi-empirical statistical model considering both factors explained 78%of global V_(c,max)variability,followed by 55%explained by enzyme kinetics alone.This statistical model outperformed the current theoretical optimality model for predicting global V_(c,max)variability(67%),primarily due to its poor characterization on global V_(c,max25)variability(3%).Further,we demonstrated that,in addition to climatic variables,belowground resource constraint on photosynthetic machinery built-up that directly structures the biogeography of V_(c,max25)was a key missing mechanism for improving the theoretical modelling of global V_(c,max)variability.These findings improve the mechanistic understanding of global V_(c,max)variability and provide an important basis to benchmark process-based models of terrestrial photosynthesis and carbon cycling under climate change.
