Differential Species Richness and Ecological Success of Epiphytes and Hemiepiphytes of Neotropical Araceae and Cyclanthaceae.

Numerous plant functional traits of ecophysiology and morphology associated with an epiphytic life history have promoted relatively high rates of evolutionary diversification and ecological success in tropical families such as the Orchidaeae, Polypodiaceae, Bromeliaceae, and Cactaceae. Epiphytic life histories are relatively uncommon in the Araceae and rare in the Cyclanthaceae which lack key functional traits for epiphytism. Only two lineages of Neotropical Araceae, Anthurium and Philodendron, include examples of epiphyte life histories. The evolution of a hemiepiphytic life history represented an important development for tropical Araceae by providing functional traits that have greatly expanded opportunities for adaptive radiation and ecological success as indicated by species richness and frequency of occurrence. The key adaptive trait allowing the diversification of hemiepiphytic Araceae was the development of heteroblastic growth of leaves and stems. Although hemiepiphytic life histories are present in the Cyclanthaceae, the family has undergone only modest speciation and limited ecological success in both its epiphytes and hemiepiphytes. Extensive sampling of more than 4600 trees from primary forest on four soil groups in northeastern Costa Rica have found a modest diversity of 15 species of epiphytic Araceae but only two species of epiphytic Cyclanthaceae. In contrast, 38 species of hemiepiphytic Araceae and 5 species of hemiepiphytic Cyclanthaceae were sampled, indicating relatively limited adaptive radiation of hemiepiphytic Cyclanthaceae and lower ecological success. Using summed values of frequency of occurrence as a measure of ecological success, epiphytic Araceae were 18 to 42 times more frequent than epiphytic Cyclanthaceae in swamp, alluvial, and residual soil forests. Summed frequencies of occurrence of hemiepiphytic Araceae were 7 to 13 times higher than those of hemiepiphytic Cyclanthaceae. The four soil groups were similar in their floristic composition of epiphytic and hemiepiphytic Araceae and Cyclanthaceae, but the frequencies of occurrence of both epiphytes and hemiepiphytes were, with few exceptions, highest on swamp soil plots, with alluvial soil plots slightly less favorable.

Functional traits of broad-leaved monocot herbs in the understory and forest edges of a Costa Rican rainforest.

Broad-leaved monocot herbs are widespread and dominant components of the shaded understories of wet neotropical forests. These understory habitats are characterized by light limitation and a constant threat of falling branches. Many shaded understory herb species have close relatives that occupy forest edges and gaps, where light availability is higher and defoliation threat is lower, creating an opportunity for comparative analysis of functional traits in order to better understand the evolutionary adaptations associated with this habitat transition. We documented ecological, morphological and ecophysiological traits of multiple herb species in six monocot families from each of these two habitats in the wet tropical rainforest at the La Selva Biological Station, Costa Rica. We found that a mixture of phylogenetic canalization and ecological selection for specific habitats helped explain patterns of functional traits. Understory herbs were significantly shorter and had smaller leaves than forest edge species. Although the mean number of leaves per plant and specific leaf area did not differ between the two groups, the larger leaf size of forest edge species gave them more than three times the mean plant leaf area. Measures of leaf water content and nitrogen content varied within both groups and mean values were not significantly different. Despite the high leaf nitrogen contents, the maximum photosynthetic rates of understory herbs were quite low. Measures of δ 13C as an analog of water use efficiency found significantly lower (more negative) values in understory herbs compared to forest edge species. Clonality was strongly developed in several species but did not show strong phylogenetic patterns. This study highlights many functional traits that differ between broad-leaved monocot species characteristic of understory and forest edge habitats, as well as traits that vary primarily by phylogenetic relatedness. Overall, plant functional traits do not provide a simple explanation for the relative differences in abundance for individual understory and forest edge species with some occurring in great abundance while others are relatively rare.

How well do the spring indices predict phenological activity across plant species?

The spring indices, models that represent the onset of spring season biological activity, were developed using a long-term observational record from the mid-to-late twentieth century of three species of lilacs and honeysuckles contributed by volunteer observers across the nation. The USA National Phenology Network (USA-NPN) produces and freely delivers maps of spring index onset dates at fine spatial scale for the USA. These maps are used widely in natural resource planning and management applications. The extent to which the models represent activity in a broad suite of plant species is not well documented. In this study, we used a rich record of observational plant phenology data (37,819 onset records) collected in recent years (1981-2017) to evaluate how well gridded maps of the spring index models predict leaf and flowering onset dates in (a) 19 species of ecologically important, broadly distributed deciduous trees and shrubs, and (b) the lilac and honeysuckle species used to construct the models. The extent to which the spring indices predicted vegetative and reproductive phenology varied by species and with latitude, with stronger relationships revealed for shrubs than trees and with the Bloom Index compared to the Leaf Index, and reduced concordance between the indices at higher latitudes. These results allow us to use the indices as indicators of when to expect activity across widely distributed species and can serve as a yardstick to assess how future changes in the timing of spring will impact a broad array of trees and shrubs across the USA.

Creating the Urban Farmer's Almanac with Citizen Science Data.

Agriculture has long been a part of the urban landscape, from gardens to small scale farms. In recent decades, interest in producing food in cities has grown dramatically, with an estimated 30% of the global urban population engaged in some form of food production. Identifying and managing the insect biodiversity found on city farms is a complex task often requiring years of study and specialization, especially in urban landscapes which have a complicated tapestry of fragmentation, diversity, pollution, and introduced species. Supporting urban growers with relevant data informs insect management decision-making for both growers and their neighbors, yet this information can be difficult to come by. In this study, we introduced several web-based citizen science programs that can connect growers with useful data products and people to help with the who, what, where, and when of urban insects. Combining the power of citizen science volunteers with the efforts of urban farmers can result in a clearer picture of the diversity and ecosystem services in play, limited insecticide use, and enhanced non-chemical alternatives. Connecting urban farming practices with citizen science programs also demonstrates the ecosystem value of urban agriculture and engages more citizens with the topics of food production, security, and justice in their communities.

A decade of flowering phenology of the keystone saguaro cactus (Carnegiea gigantea).

PREMISE OF THE STUDY: Phenology is the study of biological life cycle events, such as flowering and migration. Climate patterns can alter these life history events, having ecosystem-wide ramifications. For example, warmer springs are associated with earlier leaf-out for many species, impacting species interactions and growing-season carbon dynamics. While phenological research has been conducted extensively in temperate regions, relatively little is known about the phenological responses in arid and semi-arid regions. METHODS: In this study we looked at the flowering phenology of a keystone species in the Sonoran Desert, the saguaro cactus (Carnegiea gigantea). The timing and abundance of flowering was observed on 151 individuals for 10 years at a site near Tucson, Arizona, USA. Using six phenological traits, we explored the relationship between saguaro size and flowering and the climatic drivers of flowering. KEY RESULTS: Our analyses demonstrated how the calculation of phenological traits at the individual versus the population level can yield differing responses to climate variability, suggesting that not all studies examining the same trait (e.g., first day of bloom) are directly comparable. We found that larger cacti began flowering earlier, flowered for longer, and produced more flowers. Warmer temperatures were correlated with advanced onset and higher bloom yields, while increased precipitation appeared to delay onset and reduce bloom yields. CONCLUSIONS: Given that climate models predict that the Southwestern USA will become increasingly warmer with more variable precipitation, saguaros may begin flowering earlier in the season and flower more intensely, which could impact pollen availability and population dynamics.

USA National Phenology Network's volunteer-contributed observations yield predictive models of phenological transitions.

PURPOSE: In support of science and society, the USA National Phenology Network (USA-NPN) maintains a rapidly growing, continental-scale, species-rich dataset of plant and animal phenology observations that with over 10 million records is the largest such database in the United States. The aim of this study was to explore the potential that exists in the broad and rich volunteer-collected dataset maintained by the USA-NPN for constructing models predicting the timing of phenological transition across species' ranges within the continental United States. Contributed voluntarily by professional and citizen scientists, these opportunistically collected observations are characterized by spatial clustering, inconsistent spatial and temporal sampling, and short temporal depth (2009-present). Whether data exhibiting such limitations can be used to develop predictive models appropriate for use across large geographic regions has not yet been explored. METHODS: We constructed predictive models for phenophases that are the most abundant in the database and also relevant to management applications for all species with available data, regardless of plant growth habit, location, geographic extent, or temporal depth of the observations. We implemented a very basic model formulation-thermal time models with a fixed start date. RESULTS: Sufficient data were available to construct 107 individual species × phenophase models. Remarkably, given the limited temporal depth of this dataset and the simple modeling approach used, fifteen of these models (14%) met our criteria for model fit and error. The majority of these models represented the "breaking leaf buds" and "leaves" phenophases and represented shrub or tree growth forms. Accumulated growing degree day (GDD) thresholds that emerged ranged from 454 GDDs (Amelanchier canadensis-breaking leaf buds) to 1,300 GDDs (Prunus serotina-open flowers). Such candidate thermal time thresholds can be used to produce real-time and short-term forecast maps of the timing of these phenophase transition. In addition, many of the candidate models that emerged were suitable for use across the majority of the species' geographic ranges. Real-time and forecast maps of phenophase transitions could support a wide range of natural resource management applications, including invasive plant management, issuing asthma and allergy alerts, and anticipating frost damage for crops in vulnerable states. IMPLICATIONS: Our finding that several viable thermal time threshold models that work across the majority of the species ranges could be constructed from the USA-NPN database provides clear evidence that great potential exists this dataset to develop more enhanced predictive models for additional species and phenophases. Further, the candidate models that emerged have immediate utility for supporting a wide range of management applications.

Estimating the onset of spring from a complex phenology database: trade-offs across geographic scales.

Phenology is an important indicator of ecological response to climate change. Yet, phenological responses are highly variable among species and biogeographic regions. Recent monitoring initiatives have generated large phenological datasets comprised of observations from both professionals and volunteers. Because the observation frequency is often variable, there is uncertainty associated with estimating the timing of phenological activity. "Status monitoring" is an approach that focuses on recording observations throughout the full development of life cycle stages rather than only first dates in order to quantify uncertainty in generating phenological metrics, such as onset dates or duration. However, methods for using status data and calculating phenological metrics are not standardized. To understand how data selection criteria affect onset estimates of springtime leaf-out, we used status-based monitoring data curated by the USA National Phenology Network for 11 deciduous tree species in the eastern USA between 2009 and 2013. We asked, (1) How are estimates of the date of leaf-out onset, at the site and regional levels, influenced by different data selection criteria and methods for calculating onset, and (2) at the regional level, how does the timing of leaf-out relate to springtime minimum temperatures across latitudes and species? Results indicate that, to answer research questions at site to landscape levels, data users may need to apply more restrictive data selection criteria to increase confidence in calculating phenological metrics. However, when answering questions at the regional level, such as when investigating spatiotemporal patterns across a latitudinal gradient, there is low risk of acquiring erroneous results by maximizing sample size when using status-derived phenological data.

Developing a Workflow to Identify Inconsistencies in Volunteered Geographic Information: A Phenological Case Study.

Recent improvements in online information communication and mobile location-aware technologies have led to the production of large volumes of volunteered geographic information. Widespread, large-scale efforts by volunteers to collect data can inform and drive scientific advances in diverse fields, including ecology and climatology. Traditional workflows to check the quality of such volunteered information can be costly and time consuming as they heavily rely on human interventions. However, identifying factors that can influence data quality, such as inconsistency, is crucial when these data are used in modeling and decision-making frameworks. Recently developed workflows use simple statistical approaches that assume that the majority of the information is consistent. However, this assumption is not generalizable, and ignores underlying geographic and environmental contextual variability that may explain apparent inconsistencies. Here we describe an automated workflow to check inconsistency based on the availability of contextual environmental information for sampling locations. The workflow consists of three steps: (1) dimensionality reduction to facilitate further analysis and interpretation of results, (2) model-based clustering to group observations according to their contextual conditions, and (3) identification of inconsistent observations within each cluster. The workflow was applied to volunteered observations of flowering in common and cloned lilac plants (Syringa vulgaris and Syringa x chinensis) in the United States for the period 1980 to 2013. About 97% of the observations for both common and cloned lilacs were flagged as consistent, indicating that volunteers provided reliable information for this case study. Relative to the original dataset, the exclusion of inconsistent observations changed the apparent rate of change in lilac bloom dates by two days per decade, indicating the importance of inconsistency checking as a key step in data quality assessment for volunteered geographic information. Initiatives that leverage volunteered geographic information can adapt this workflow to improve the quality of their datasets and the robustness of their scientific analyses.

Phenology research for natural resource management in the United States.

Natural resource professionals in the United States recognize that climate-induced changes in phenology can substantially affect resource management. This is reflected in national climate change response plans recently released by major resource agencies. However, managers on-the-ground are often unclear about how to use phenological information to inform their management practices. Until recently, this was at least partially due to the lack of broad-based, standardized phenology data collection across taxa and geographic regions. Such efforts are now underway, albeit in very early stages. Nonetheless, a major hurdle still exists: phenology-linked climate change research has focused more on describing broad ecological changes rather than making direct connections to local to regional management concerns. To help researchers better design relevant research for use in conservation and management decision-making processes, we describe phenology-related research topics that facilitate "actionable" science. Examples include research on evolution and phenotypic plasticity related to vulnerability, the demographic consequences of trophic mismatch, the role of invasive species, and building robust ecological forecast models. Such efforts will increase phenology literacy among on-the-ground resource managers and provide information relevant for short- and long-term decision-making, particularly as related to climate response planning and implementing climate-informed monitoring in the context of adaptive management. In sum, we argue that phenological information is a crucial component of the resource management toolbox that facilitates identification and evaluation of strategies that will reduce the vulnerability of natural systems to climate change. Management-savvy researchers can play an important role in reaching this goal.

Standardized phenology monitoring methods to track plant and animal activity for science and resource management applications.

Phenology offers critical insights into the responses of species to climate change; shifts in species' phenologies can result in disruptions to the ecosystem processes and services upon which human livelihood depends. To better detect such shifts, scientists need long-term phenological records covering many taxa and across a broad geographic distribution. To date, phenological observation efforts across the USA have been geographically limited and have used different methods, making comparisons across sites and species difficult. To facilitate coordinated cross-site, cross-species, and geographically extensive phenological monitoring across the nation, the USA National Phenology Network has developed in situ monitoring protocols standardized across taxonomic groups and ecosystem types for terrestrial, freshwater, and marine plant and animal taxa. The protocols include elements that allow enhanced detection and description of phenological responses, including assessment of phenological "status", or the ability to track presence-absence of a particular phenophase, as well as standards for documenting the degree to which phenological activity is expressed in terms of intensity or abundance. Data collected by this method can be integrated with historical phenology data sets, enabling the development of databases for spatial and temporal assessment of changes in status and trends of disparate organisms. To build a common, spatially, and temporally extensive multi-taxa phenological data set available for a variety of research and science applications, we encourage scientists, resources managers, and others conducting ecological monitoring or research to consider utilization of these standardized protocols for tracking the seasonal activity of plants and animals.

Photosynthetic resource-use efficiency and demographic variability in desert winter annual plants.

We studied a guild of desert winter annual plants that differ in long-term variation in per capita reproductive success (lb, the product of per capita survival from germination to reproduction, l, times per capita reproduction of survivors, b) to relate individual function to population and community dynamics. We hypothesized that variation in lb should be related to species' positions along a trade-off between relative growth rate (RGR) and photosynthetic water-use efficiency (WUE) because lb is a species-specific function of growing-season precipitation. We found that demographically variable species have greater RGR and greater leaf carbon isotope discrimination (Delta, a proxy inversely related to WUE). We examined leaf nitrogen and photosynthetic characteristics and found that, in this system, variation in Delta is a function of photosynthetic demand rather than stomatal regulation of water loss. The physiological characteristics that result in low Delta in some species may confer greater photosynthetic performance during the reliably moist but low temperature periods that immediately follow winter rainfall in the Sonoran Desert or alternatively during cool periods of the day or early growing season. Conversely, while species with high Delta and high RGR exhibit low leaf N, they have high biomass allocation to canopy leaf area display. Such trait associations may allow for greater performance during the infrequent conditions where high soil moisture persists into warmer conditions, resulting in high demographic variance. Alternatively, high variance could arise from specialization to warm periods of the day or season. Population dynamic buffering via stress tolerance (low RGR and Delta) correlates negatively with buffering via seed banks, as predicted by bet-hedging theory. By merging analyses of population dynamics with functional trait relationships, we develop a deeper understanding of the physiological, ecological, and evolutionary mechanisms involved in population and community dynamics.