Carl F. Salk

High‐dimensional coexistence based on individual variation: a synthesis of evidence

High biodiversity of forests is not predicted by traditional models, and evidence for trade-offs those models require is limited. High-dimensional regulation (e.g., N factors to regulate N species) has long been recognized as a possible alternative explanation, but it has not be been seriously pursued, because only a few limiting resources are evident for trees, and analysis of multiple interactions is challenging. We develop a hierarchical model that allows us to synthesize data from long-term, experimental, data sets with processes that control growth, maturation, fecundity, and survival. We allow for uncertainty at all stages and variation among 26 000 individuals and over time, including 268 000 tree years, for dozens of tree species. We estimate population-level parameters that apply at the species level and the interactions among latent states, i.e., the demographic rates for each individual, every year. The former show that the traditional trade-offs used to explain diversity are not present. Demographic rates overlap among species, and they do not show trends consistent with maintenance of diversity by simple mechanisms (negative correlations and limiting similarity). However, estimates of latent states at the level of individuals and years demonstrate that species partition environmental variation. Correlations between responses to variation in time are high for individuals of the same species, but not for individuals of different species. We demonstrate that these relationships are pervasive, providing strong evidence that high- dimensional regulation is critical for biodiversity regulation.

Comparing the Quality of Crowdsourced Data Contributed by Expert and Non-Experts

There is currently a lack of in-situ environmental data for the calibration and validation of remotely sensed products and for the development and verification of models. Crowdsourcing is increasingly being seen as one potentially powerful way of increasing the supply of in-situ data but there are a number of concerns over the subsequent use of the data, in particular over data quality. This paper examined crowdsourced data from the Geo-Wiki crowdsourcing tool for land cover validation to determine whether there were significant differences in quality between the answers provided by experts and non-experts in the domain of remote sensing and therefore the extent to which crowdsourced data describing human impact and land cover can be used in further scientific research. The results showed that there was little difference between experts and non-experts in identifying human impact although results varied by land cover while experts were better than non-experts in identifying the land cover type. This suggests the need to create training materials with more examples in those areas where difficulties in identification were encountered, and to offer some method for contributors to reflect on the information they contribute, perhaps by feeding back the evaluations of their contributed data or by making additional training materials available. Accuracies were also found to be higher when the volunteers were more consistent in their responses at a given location and when they indicated higher confidence, which suggests that these additional pieces of information could be used in the development of robust measures of quality in the future.

Individual‐scale variation, species‐scale differences: inference needed to understand diversity

As ecological data are usually analysed at a scale different from the one at which the process of interest operates, interpretations can be confusing and controversial. For example, hypothesised differences between species do not operate at the species level, but concern individuals responding to environmental variation, including competition with neighbours. Aggregated data from many individuals subject to spatio-temporal variation are used to produce species-level averages, which marginalise away the relevant (process-level) scale. Paradoxically, the higher the dimensionality, the more ways there are to differ, yet the more species appear the same. The aggregate becomes increasingly irrelevant and misleading. Standard analyses can make species look the same, reverse species rankings along niche axes, make the surprising prediction that a species decreases in abundance when a competitor is removed from a model, or simply preclude parameter estimation. Aggregation explains why niche differences hidden at the species level become apparent upon disaggregation to the individual level, why models suggest that individual-level variation has a minor impact on diversity when disaggregation shows it to be important, and why literature-based synthesis can be unfruitful. We show how to identify when aggregation is the problem, where it has caused controversy, and propose three ways to address it.

The seasonal timing of warming that controls onset of the growing season

Forecasting how global warming will affect onset of the growing season is essential for predicting terrestrial productivity, but suffers from conflicting evidence. We show that accurate estimates require ways to connect discrete observations of changing tree status (e.g., pre- vs. post budbreak) with continuous responses to fluctuating temperatures. By coherently synthesizing discrete observations with continuous responses to temperature variation, we accurately quantify how increasing temperature variation accelerates onset of growth. Application to warming experiments at two latitudes demonstrates that maximum responses to warming are concentrated in late winter, weeks ahead of the main budbreak period. Given that warming will not occur uniformly over the year, knowledge of when temperature variation has the most impact can guide prediction. Responses are large and heterogeneous, yet predictable. The approach has immediate application to forecasting effects of warming on growing season length, requiring only information that is readily available from weather stations and generated in climate models.

Tree phenology responses to winter chilling, spring warming, at north and south range limits

Summary Increases in primary production may occur if plants respond to climate warming with prolonged growing seasons, but not if local adaptation, cued by photoperiod, limits phenological advance. It has been hypothesized that trees with diffuse-porous xylem anatomy and early successional species may respond most to warming. Within species, northern populations may respond most due to the fact that growing seasons are relatively short. Species most sensitive to spring temperature may show little overall response to warming if reduced chilling in fall/winter offsets accelerated winter/spring development. Because current thermal models consider only highly aggregated variables, for example degree-days or chilling units (temperature sums for a season or year), they may not accurately represent warming effects. We show that assumptions contained in current thermal (degree-day) models are unrealistic for climate change analysis. Critical threshold parameters are not identifiable, and they do not actually have much to do with thresholds for development. Traditional models further overlook the discrete nature of observations, observation error and the continuous response of phenological development to temperature variation. An alternative continuous development model (CDM) that addresses these problems is applied to a large experimental warming study near northern and southern boundaries of 15 species in the eastern deciduous forest of the USA, in North Carolina and Massachusetts. Results provide a detailed time course of phenological development, including vernalization during winter and warming in spring, and challenge the basic assumptions of thermal models. Where traditional models find little evidence of a chilling effect (most are insignificant or have the wrong sign), the continuous development model finds evidence of chilling effects in most species. Contrary to the hypothesis that northern populations respond most, we find southern populations are most responsive. Because northern populations already have a compressed period for spring development, they may lack flexibility to further advance development. A stronger response in the southern range could allow residents to resist northward migration of immigrants as climate warms. If potential invaders fail to exploit a prolonged growing season to the same degree as residents, then there is a resident advantage. Hypothesized effects of warming for xylem anatomy and successional status are not supported by the 15 species in this study.

Assessing quality of volunteer crowdsourcing contributions: lessons from the Cropland Capture game

Volunteered geographic information (VGI) is the assembly of spatial information based on public input. While VGI has proliferated in recent years, assessing the quality of volunteer-contributed data has proven challenging, leading some to question the efficiency of such programs. In this paper, we compare several quality metrics for individual volunteers’ contributions. The data were the product of the ‘Cropland Capture’ game, in which several thousand volunteers assessed 165,000 images for the presence of cropland over the course of 6 months. We compared agreement between volunteer ratings and an images majority classification with volunteer self-agreement on repeated images and expert evaluations. We also examined the impact of experience and learning on performance. Volunteer self-agreement was nearly always higher than agreement with majority classifications, and much greater than agreement with expert validations although these metrics were all positively correlated. Volunteer quality showed a broad trend toward improvement with experience, but the highest accuracies were achieved by a handful of moderately active contributors, not the most active volunteers. Our results emphasize the importance of a universal set of expert-validated tasks as a gold standard for evaluating VGI quality.

A global dataset of crowdsourced land cover and land use reference data

Global land cover is an essential climate variable and a key biophysical driver for earth system models. While remote sensing technology, particularly satellites, have played a key role in providing land cover datasets, large discrepancies have been noted among the available products. Global land use is typically more difficult to map and in many cases cannot be remotely sensed. In-situ or ground-based data and high resolution imagery are thus an important requirement for producing accurate land cover and land use datasets and this is precisely what is lacking. Here we describe the global land cover and land use reference data derived from the Geo-Wiki crowdsourcing platform via four campaigns. These global datasets provide information on human impact, land cover disagreement, wilderness and land cover and land use. Hence, they are relevant for the scientific community that requires reference data for global satellite-derived products, as well as those interested in monitoring global terrestrial ecosystems in general.

Limitations of Majority Agreement in Crowdsourced Image Interpretation

Crowdsourcing can efficiently complete tasks that are difficult to automate, but the quality of crowdsourced data is tricky to evaluate. Algorithms to grade volunteer work often assume that all tasks are similarly difficult, an assumption that is frequently false. We use a cropland identification game with over 2,600 participants and 165,000 unique tasks to investigate how best to evaluate the difficulty of crowdsourced tasks and to what extent this is possible based on volunteer responses alone. Inter-volunteer agreement exceeded 90% for about 80% of the images and was negatively correlated with volunteer-expressed uncertainty about image classification. A total of 343 relatively difficult images were independently classified as cropland, non-cropland or impossible by two experts. The experts disagreed weakly (one said impossible while the other rated as cropland or non-cropland) on 27% of the images, but disagreed strongly (cropland vs. non-cropland) on only 7%. Inter-volunteer disagreement increased significantly with inter-expert disagreement. While volunteers agreed with expert classifications for most images, over 20% would have been mis-categorized if only the volunteers’ majority vote was used. We end with a series of recommendations for managing the challenges posed by heterogeneous tasks in crowdsourcing campaigns.

Ecological and environmental factors constrain sprouting ability in tropical trees

Most theories of forest biodiversity focus on the role of seed dispersal and seedling establishment in forest regeneration. In many ecosystems, however, sprouting by damaged stems determines which species occupies a site. Damaged trees can quickly recover from disturbance and out-compete seedlings. Links among species’ traits, environmental conditions and sprouting could offer insight into species’ resilience to changes in climate, land use, and disturbance. Using data for 25 Neotropical tree species at two sites with contrasting rainfall and soil, we tested hypotheses on how four functional traits (seed mass, leaf mass per area, wood density and nitrogen fixation) influence species’ sprouting responses to disturbance and how these relationships are mediated by a tree’s environmental context. Most species sprouted in response to cutting, and many species’ sprouting rates differed significantly between sites. Individual traits showed no direct correlation with sprouting. However, interactions among traits and site variables did affect sprouting rates. Many species showed increased sprouting in the higher-quality site. Most nitrogen-fixing species showed the opposite trend, sprouting more frequently where resources are scarce. This study highlights the use of functional traits as a proxy for life histories, and demonstrates the importance of environmental effects on demography.

LACO-Wiki: A New Online Land Cover Validation Tool Demonstrated Using GlobeLand30 for Kenya

Accuracy assessment, also referred to as validation, is a key process in the workflow of developing a land cover map. To make this process open and transparent, we have developed a new online tool called LACO-Wiki, which encapsulates this process into a set of four simple steps including uploading a land cover map, creating a sample from the map, interpreting the sample with very high resolution satellite imagery and generating a report with accuracy measures. The aim of this paper is to present the main features of this new tool followed by an example of how it can be used for accuracy assessment of a land cover map. For the purpose of illustration, we have chosen GlobeLand30 for Kenya. Two different samples were interpreted by three individuals: one sample was provided by the GlobeLand30 team as part of their international efforts in validating GlobeLand30 with GEO (Group on Earth Observation) member states while a second sample was generated using LACO-Wiki. Using satellite imagery from Google Maps, Bing and Google Earth, the results show overall accuracies between 53% to 61%, which is lower than the global accuracy assessment of GlobeLand30 but may be reasonable given the complex landscapes found in Kenya. Statistical models were then fit to the data to determine what factors affect the agreement between the three interpreters such as the land cover class, the presence of very high resolution satellite imagery and the age of the image in relation to the baseline year for GlobeLand30 (2010). The results showed that all factors had a significant effect on the agreement.