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Abstract Lianas are key components of tropical forests, particularly at sites with more severe dry seasons. In contrast, trees are more abundant and speciose in wetter areas. The seasonal growth advantage (SGA) hypothesis postulates that such contrasting distributions are produced by higher liana growth relative to trees during seasonal droughts. The SGA has been investigated for larger size classes (e.g., ≥5 cm diameter at 1.3 m, dbh), but rarely for seedlings. Using eight annual censuses of >12,000 seedlings of 483 tree and liana species conducted at eight 1‐ha plots spanning a strong rainfall gradient in central Panama, we evaluated whether liana seedlings had higher growth and/or survival rates than tree seedlings at sites with stronger droughts. We also tested whether an extreme El Niño drought during the study period had a more negative effect on tree compared to liana seedlings. The absolute density of liana seedlings was similar across the rainfall gradient, ranging from 0.32 individuals/m²(0.20–0.49, 95% credible interval [CI]) at the driest end of the gradient and 0.27 individuals/m²(0.13–0.51 95% CI) at the wettest end of the gradient. The relative density of liana seedlings compared to tree seedlings was higher at sites with stronger dry seasons (0.27, 0.21–0.33, 95% CI), compared to wetter sites (0.12, 0.04–0.20 95% CI), due to lower tree seedling densities at drier sites. However, liana seedlings did not grow or survive better than tree seedlings in drier sites compared to wetter sites. Tree seedlings were more negatively impacted in terms of mortality by the extreme El Niño drought than liana seedlings, with an increase in annual mortality rate of 0.013 (0.003–0.025 95% CI) compared to lianas of −0.009 (−0.028 to 0.008 95% CI), but not growth. Our results indicate that lianas do not have a SGA over trees at the seedling stage. Instead, higher survival of liana versus tree seedlings during severe droughts or differences in liana versus tree fecundity or germination across the rainfall gradient likely explain why liana seedlings have higher relative densities at drier sites. 

IntroductionThere has been a growing realization that a more inclusive approach to research can provide both ethical and practical benefits. Long-term avian monitoring programs, and indeed the academic and research community as a whole, are still learning how best to implement these methodologies effectively. MethodsThis paper provides information on a twenty-plus-year effort to conduct community-engaged avian monitoring in northwest Ecuador, with a focus on how this approach has impacted the quality and scope of the project’s science and broader societal impacts. We focus on three case studies that have been proceeding for varying lengths of time to highlight various stages of project development and maturity. ResultsA community-engaged approach has improved the quality of our scientific research by adding traditional ecological knowledge (TEK), technical capacity, and intellectual contributions to our monitoring efforts. Community-engaged research has also enhanced the breadth and quality of societal impacts, in terms of education, capacity building, and conservation, particularly in the formation of an ecological reserve that protects threatened species and habitat. We also discuss systemic and local challenges, and potential strategies to overcome these challenges DiscussionWe conclude that community-engaged research can improve the intellectual merit and broader societal impacts of long-term avian monitoring, and we advocate for continued investment, efforts, and careful reflection on best practices in this space. 

A fundamental assumption of functional ecology is that functional traits are related to interspecific variation in performance. However, the relationship between functional traits and performance is often weak or uncertain, especially for plants. A potential explanation for this inconsistency is that the relationship between functional traits and vital rates (e.g., growth and mortality) is dependent on local environmental conditions, which would lead to variation in trait-rate relationships across environmental gradients. In this study, we examined trait-rate relationships for six functional traits (seed mass, wood density, maximum height, leaf mass per area, leaf area, and leaf dry matter content) using long-term data on seedling growth and survival of woody plant species from eight forest sites spanning a pronounced precipitation and soil phosphorus gradient in central Panama. For all traits considered except for leaf mass per area-mortality, leaf mass per area-growth, and leaf area-mortality relationships, we found widespread variation in the strength of trait-rate relationships across sites. For some traits, trait-rate relationships showed no overall trend but displayed wide site-to-site variation. In a small subset of cases, variation in trait-rate relationships was explained by soil phosphorus availability. Our results demonstrate that environmental gradients have the potential to influence how functional traits are related to growth and mortality rates, though much variation remains to be explained. Accounting for site-to-site variation may help resolve a fundamental issue in trait-based ecology – that traits are often weakly related to performance – and improve the utility of functional traits for explaining key ecological and evolutionary processes. 

Data on individual tree crowns from remote sensing have the potential to advance forest ecology by providing information about forest composition and structure with a continuous spatial coverage over large spatial extents. Classifying individual trees to their taxonomic species over large regions from remote sensing data is challenging. Methods to classify individual species are often accurate for common species, but perform poorly for less common species and when applied to new sites. We ran a data science competition to help identify effective methods for the task of classification of individual crowns to species identity. The competition included data from three sites to assess each methods’ ability to generalize patterns across two sites simultaneously and apply methods to an untrained site. Three different metrics were used to assess and compare model performance. Six teams participated, representing four countries and nine individuals. The highest performing method from a previous competition in 2017 was applied and used as a baseline to understand advancements and changes in successful methods. The best species classification method was based on a two-stage fully connected neural network that significantly outperformed the baseline random forest and gradient boosting ensemble methods. All methods generalized well by showing relatively strong performance on the trained sites (accuracy = 0.46–0.55, macro F1 = 0.09–0.32, cross entropy loss = 2.4–9.2), but generally failed to transfer effectively to the untrained site (accuracy = 0.07–0.32, macro F1 = 0.02–0.18, cross entropy loss = 2.8–16.3). Classification performance was influenced by the number of samples with species labels available for training, with most methods predicting common species at the training sites well (maximum F1 score of 0.86) relative to the uncommon species where none were predicted. Classification errors were most common between species in the same genus and different species that occur in the same habitat. Most methods performed better than the baseline in detecting if a species was not in the training data by predicting an untrained mixed-species class, especially in the untrained site. This work has highlighted that data science competitions can encourage advancement of methods, particularly by bringing in new people from outside the focal discipline, and by providing an open dataset and evaluation criteria from which participants can learn. 

'Panama-El-Nino-publish.zip' contains all the code and data necessary to reproduce the analyses in the manuscript. Please unzip the file and see README.md for instructions.</div>'rocker-geospatial-rstan.sif' is a Singularity container that comes with all necessary packages pre-installed. Please seed README.md in the 'Panama-El-Nino-publish.zip' file for instructions.</div></div>Abstract</b></div>As extreme climate events are predicted to become more frequent due to global climate change, understanding their impacts on natural systems is crucial. Tropical forests are vulnerable to droughts associated with extreme El Niño events. However, little is known about how tropical seedling communities respond to El Niño-related droughts, even though patterns of seedling survival shape future forest structure and diversity. Using long-term data from eight tropical moist forests spanning a rainfall gradient in central Panama, we show that community-wide seedling mortality increased by 11% during the extreme 2015-16 El Niño, with mortality increasing most in drought sensitive species and in wetter forests. These results indicate that severe El Niño-related droughts influence understory dynamics in tropical forests, with effects varying both within and across sites. Our findings suggest that predicted increases in the frequency of extreme El Niño events will alter tropical plant communities through effects on early life stages.</div></div></div>