Sensitivity of plant-pollinator-herbivore communities to changes in phenology

For many species in seasonal environments, warmer springs associated with anthropogenic climate change are causing phenological changes. Within ecological communities, the timing of interactions among species may be altered if the species experience asymmetrical phenological shifts. We present a model that examines the consequences of changes in the relative timing of herbivory and pollination in a community of herbivores and pollinators that share a host plant. Our model suggests that phenological shifts can alter the abundances of these species and, in some cases, their population dynamics. If historical patterns of interactions in a community change and herbivores become active before pollinators, the community could see a reduction in pollinators and an increase in herbivores, while if pollinators become active before herbivores, there could be a loss of stable coexistence. Previous studies have warned of the potential for climate change to cause large phenological mismatches whereby species that depend on one another become so separated in time that they can no longer interact. Our results suggest that climate change-induced phenological shifts can have a major impact on communities even in cases where complete phenological mismatches do not occur.     

Rapid,recurring, structured survey versus bioblitz for generating biodiversity data and analysis with a multispecies abundance model

A bioblitz inexpensively and quickly generates biodiversity data, but bioblitzes are often conducted with haphazard, unreplicated sampling. Results tend to be taxonomically, geographically, or temporally biased, lack metadata, and consist of lists of observed taxa that do not enable further analyses or correction for imperfect detection. A rapid, recurring, structured survey (RRSS) uses a structured sampling design and temporal and spatial replication to survey randomly selected sites on a conservation property. We participated in a loosely structured bioblitz and a subsequent RRSS at Big Canoe Creek Nature Preserve in Springville (St. Clair County), Alabama (USA) to compare observed richness derived from the 2 survey approaches. The RRSS data structure enabled us to fit models that accounted for imperfect detection to estimate abundances, occupancy probabilities, and habitat associations. The loosely structured bioblitz data could not be used in such models. We present a new integrated multispecies abundance model that we applied to avian RRSS data. Our model extension enables estimation for the community, employs data augmentation to estimate the number of undetected species, and incorporates covariates. The RRSS generated a more comprehensive and less biased list of observed taxonomic richness than the loosely structured bioblitz (e.g., 73 vs. 45 bird species and 104 vs. 63 insect families from the RRSS vs. loosely structured bioblitz, respectively). Models fit to the RRSS data identified seasonal patterns in avian community composition and allowed for estimation of habitat–occupancy relationships for insect taxa. The RRSS protocol has potential for broad transferability as a standardized, quick, and inexpensive way to inventory biodiversity and estimate ecological parameters while providing an outreach opportunity.     

Effects of sampling completeness on the structure of plant-pollinator networks

Plant-animal interaction networks provide important information on community organization. One of the most critical assumptions of network analysis is that the observed interaction patterns constitute an adequate sample of the set of interactions present in plant-animal communities. In spite of its importance, few studies have evaluated this assumption, and in consequence, there is no consensus on the sensitivity of network metrics to sampling methodological shortcomings. In this study we examined how variation in sampling completeness influences the estimation of six network metrics frequently used in the literature (connectance, nestedness, modularity, robustness to species loss, path length, and centralization). We analyzed data of 186 flowering plants and 336 pollinator species in 10 networks from a forest-fragmented system in central Chile. Using species-based accumulation curves, we estimated the deviation of network metrics in undersampled communities with respect to exhaustively sampled communities and the effect of network size and sampling evenness on network metrics. Our results indicate that: (1) most metrics were affected by sampling completeness but differed in their sensitivity to sampling effort; (2) nestedness, modularity, and robustness to species loss were less influenced by insufficient sampling than connectance, path length, and centralization; (3) robustness was mildly influenced by sampling evenness. These results caution studies that summarize information from databases with high, or unknown, heterogeneity in sampling effort per species and should stimulate researchers to report sampling intensity to standardize its effects in the search for broad patterns in plant-pollinator networks.     

Do plant–pollinator interaction networks result from stochastic processes?

Plant–pollinator interaction networks are characterized by several features that cannot be obtained from a totally random network (e.g. nestedness, power law distribution of degree specialization, temporal turnover). One reason is that both plants and pollinators are active for only a part of the year, and so a plant species flowering in spring cannot interact with a pollinator species that is active only in autumn. In this paper we build a stochastic model to simulate the plant–pollinator interaction network, taking into account the duration of activity of each species. To build the model we used an empirical plant–pollinator network from a Mediterranean scrub community surveyed over four years. In our simulated annual cycle we know which plant and pollinator species are active, and thus available to interact. We can obtain simulated plant–pollinator interaction networks with properties similar to the real ones in two different ways: (i) by assuming that the frequency distribution of both plant and pollinator duration of activity follow an exponential function, and that interaction among temporally coexisting species are totally random, and (ii) by assuming more realistic frequency distributions (exponential for pollinators, lognormal for plants) and that the interaction among coexisting species is occurring on a per capita basis. In the latter case we assume that there is a positive relationship between abundance and duration of activity. In our model the starting date of the species activity had little influence on the network structure. We conclude that the observed plant–pollinator network properties can be produced stochastically, and the mechanism shaping the network is not necessarily related to size constraints. Under such conditions co-evolutionary explanations should be given with caution.     

The strength of plant-pollinator interactions

Recent studies of plant-animal mutualistic networks have assumed that interaction frequency between mutualists predicts species impacts (population-level effects), and that field estimates of interaction strength (per-interaction effects) are unnecessary. Although existing evidence supports this assumption for the effect of animals on plants, no studies have evaluated it for the reciprocal effect of plants on animals. We evaluate this assumption using data on the reproductive effects of pollinators on plants and the reciprocal reproductive effects of plants on pollinators. The magnitude of species impacts of plants on pollinators, the reciprocal impacts of pollinators on plants, and their asymmetry were well predicted by interaction frequency. However, interaction strength was a key determinant of the sign of species impacts. These results underscore the importance of quantifying interaction strength in studies of mutualistic networks. We also show that the distributions of interaction strengths and species impacts are highly skewed, with few strong and many weak interactions. This skewed distribution matches the pattern observed in food webs, suggesting that the community-wide organization of species interactions is fundamentally similar between mutualistic and antagonistic interactions. Our results have profound ecological implications, given the key role of interaction strength for community stability.     

Spatial and temporal modeling of community non-timber forest extraction

This paper examines the interaction of spatial and dynamic aspects of resource extraction from forests by local people. Highly cyclical and varied across space and time, the patterns of resource extraction resulting from the spatial–temporal model bear little resemblance to the patterns drawn from focusing either on spatial or temporal aspects of extraction alone. Ignoring this variability inaccurately depicts villagers’ dependence on different parts of the forest and could result in inappropriate policies. Similarly, the spatial links in extraction decisions imply that policies imposed in one area can have unintended consequences in other areas. Combining the spatial–temporal model with a measure of success in community forest management—the ability to avoid open-access resource degradation—characterizes the impact of incomplete property rights on patterns of resource extraction and stocks.     

Determining association networks in social animals: choosing spatial–temporal criteria and sampling rates

Social Network Analysis has become an important methodological tool for advancing our understanding of human and animal group behaviour. However, researchers tend to rely on arbitrary distance and time measures when defining ‘contacts’ or ‘associations’ between individuals based on preliminary observation. Otherwise, criteria are chosen on the basis of the communication range of sensor devices (e.g. bluetooth communication ranges) or the sampling frequencies of collection devices (e.g. Global Positioning System devices). Thus, researchers lack an established protocol for determining both relevant association distances and minimum sampling rates required to accurately represent the network structure under investigation. In this paper, we demonstrate how researchers can use experimental and statistical methods to establish spatial and temporal association patterns and thus correctly characterise social networks in both time and space. To do this, we first perform a mixing experiment with Merino sheep (Ovis aries) and use a community detection algorithm that allows us to identify the spatial and temporal distance at which we can best identify clusters of previously familiar sheep. This turns out to be within 2–3 m of each other for at least 3 min. We then calculate the network graph entropy rate—a measure of ease of spreading of information (e.g. a disease) in a network—to determine the minimum sampling rate required to capture the variability observed in our sheep networks during distinct activity phases. Our results indicate the need for sampling intervals of less than a minute apart. The tools that we employ are versatile and could be applied to a wide range of species and social network datasets, thus allowing an increase in both the accuracy and efficiency of data collection when exploring spatial association patterns in gregarious species.     

The social network structure of a wild meerkat population: 1. Inter-group interactions

Groups of individuals frequently interact with each other, but typically analysis of such interactions is restricted to isolated dyads. Social network analysis (SNA) provides a method of analysing polyadic interactions and is used to analyse interactions between individuals. We use a population of 12 groups (ca. 250 animals) of wild meerkats (Suricata suricatta) to test whether SNA can also be used to describe and elucidate patterns of inter-group interactions. Using data collected over 24 months, we constructed two sets of networks, based on direct encounters between groups and instances of roving males visiting other groups. We analysed replicated networks of each type of interaction to investigate similarities between networks of different social interactions as well as testing their stability over time. The two network types were similar to each other when derived from long-term data, but showed significant differences in structure over shorter timescales where they varied according to seasonal and ecological conditions. Networks for both types of inter-group interaction constructed from data collected over 3 months reliably described long-term (12- and 24-month) patterns of interactions between groups, indicating a stable social structure despite variation in group sizes and sex ratios over time. The centrality of each meerkat group in roving interactions networks was unaffected by the sex ratio of its members, indicating that male meerkats preferentially visit geographically close groups rather than those containing most females. Indeed, the strongest predictors of network structure were spatial factors, suggesting that, in contrast to analyses of intra-group interactions, analyses of inter-group interactions using SNA must take spatial factors into account.     

Network-based exploration and visualisation of ecological data

Networks – structured graphs consisting of sets of nodes connected by edges – provide a rich framework for data visualisation and exploratory analyses. Although rarely used for the visualisation of ecological data, networks are well suited to this purpose, including data that one might not normally think of as a network. We present a simple method for transforming a data matrix into network format, and show how this can be used as the basis for interactive exploratory analyses of ecological data.The method is demonstrated using a database of marine zooplankton samples acquired in the Southern Ocean. The network analyses revealed zooplankton community structures that are in good agreement with previously published results. Variations in community structure were observed to be related to the temporal and spatial pattern of sampling, as well as to physical environmental factors such as sea ice cover. The analyses also revealed a number of errors in the data, including taxon identification errors and instrument failures.The method allows the analyst to generate networks from different combinations of variables in the data set, and to examine the effects of varying parameters such as the scales of spatial, temporal, and taxonomic aggregation. This flexibility allows the analyst to rapidly gain a number of perspectives on the data and provides a powerful mechanism for exploration.     

Separating selection by diurnal and nocturnal pollinators on floral display and spur length in Gymnadenia conopsea

Most plants attract multiple flower visitors that may vary widely in their effectiveness as pollinators. Floral evolution is expected to reflect interactions with the most important pollinators, but few studies have quantified the contribution of different pollinators to current selection on floral traits. To compare selection mediated by diurnal and nocturnal pollinators on floral display and spur length in the rewarding orchid Gymnadenia conopsea, we manipulated the environment by conducting supplemental hand-pollinations and selective pollinator exclusions in two populations in central Norway. In both populations, the exclusion of diurnal pollinators significantly reduced seed production compared to open pollination, whereas the exclusion of nocturnal pollinators did not. There was significant selection on traits expected to influence pollinator attraction and pollination efficiency in both the diurnal and nocturnal pollination treatment. The relative strength of selection among plants exposed to diurnal and nocturnal visitors varied among traits and populations, but the direction of selection was consistent. The results suggest that diurnal pollinators are more important than nocturnal pollinators for seed production in the study populations, but that both categories contribute to selection on floral morphology. The study illustrates how experimental manipulations can link specific categories of pollinators to observed selection on floral traits, and thus improve our understanding of how species interactions shape patterns of selection.     

Combining culturomic sources to uncover trends in popularity and seasonal interest in plants

Culturomic tools enable the exploration of trends in human–nature interactions, although they entail inherent biases and necessitate careful validation. Furthermore, people may engage with nature across different culturomic data sets differently. We evaluated people's digital interest and engagement with plant species based on Wikipedia and Google data and explored the conservation implications of these temporal interest patterns. As a case study, we explored the digital footprints of the most popular plant species in Israel. We analyzed 4 years of daily page views from Hebrew Wikipedia and 10 years of daily Google search volume in Israel. We modeled popularity of plant species in these 2 data sets based on a suite of plant attributes. We further explored the seasonal trends of people's interest in each species. We found differences in how people interacted digitally with plants in Wikipedia and Google. Overall, in Google, searches for species that have utility to humans were more common, whereas in Wikipedia, plants that serve as cultural emblems received more attention. Furthermore, in Google, popular species attracted more attention over time, opposite to the trend in Wikipedia. In Google, interest in species with short bloom duration exhibited more pronounced seasonal patterns, whereas in Wikipedia, seasonality of interest increased as bloom duration increased. Together, our results suggest that people's digital interactions with nature may be inherently different depending on the sources explored, which may affect use of this information for conservation. Although culturomics holds much promise, better understanding of its underpinnings is important when translating insights into conservation actions.     

Long-term variation of organochlorine residues and assemblages of epibenthic organisms in a shallow North Florida (USA) estuary

A 4-year study (1972–1976) determined long-term trends of organochlorine residues (DDT, DDE, DDD, PCB's, mirex) and trawl-susceptible organisms in a shallow, river-dominated estuary in North Florida (Apalachicola Bay, USA). Moderate levels of such compounds were found in various species prior to the restricted use of DDT in 1972. A subsequent precipitous decline in organochlorine besidues was attributed to decreased upland usage, major flushing of the river basin in early 1973, and various factors associated with estuarine function. No mirex was found in sediments or aquatic organisms. Apparently, the half-life of organochlorines is relatively short in this bay system. Various statistical methods were used to test the relationships of different physico-chemical and biological parameters. During the 4-year study period, seasonal river flow fluctuations dominated water color, turbidity, salinity, nutrients (NO₃), chlorophyll a, and the temporal succession of fishes in the bay. Certain long-term trends of fish associations were noted; relative dominance of key fish species declined and stabilized while bay-wide species richness and diversity increased with time. Qualitative changes in species representation determined the long-term pattern of community variability. This was consonant with a distinctive fish fauna during the first year of sampling. The bay anchovy Anchoa mitchilli was dominant during 9 of the first 12 monts of the project; this influenced the time-related changes in community indices. Temporally clustered fish associations reflected the importance of river flow in the estuarine environment. Direct correlation of fish distribution with the rapid disappearance of organochlorine compounds was complicated by aperiodic natural phenomena such as storms and river fluctuations. Population and community trends appeared consistent with other studies showing similar patterns of dominance of stress-resistant fish populations and related changes in community parameters. In this case, the relatively predictable annual succession of fish associations allowed an appraisal of key forcing functions. Due to the high level of seasonal and annual biological variability in this estuary, there were some problems in the application of linear statistical models to the data base. Although the long-term trend of relative species representation is useful as an index of stress, new techniques are needed to analyze extensive field data so that functions such as trophic interactions are included in the estimation of causal relationships. There are indications that such effects could be related to the impact of organochlorine compounds on estuarine systems.     

Null model analysis of species associations using abundance data

The influence of negative species interactions has dominated much of the literature on community assembly rules. Patterns of negative covariation among species are typically documented through null model analyses of binary presence/absence matrices in which rows designate species, columns designate sites, and the matrix entries indicate the presence (1) or absence (0) of a particular species in a particular site. However, the outcome of species interactions ultimately depends on population-level processes. Therefore, patterns of species segregation and aggregation might be more clearly expressed in abundance matrices, in which the matrix entries indicate the abundance or density of a species in a particular site. We conducted a series of benchmark tests to evaluate the performance of 14 candidate null model algorithms and six covariation metrics that can be used with abundance matrices. We first created a series of random test matrices by sampling a metacommunity from a lognormal species abundance distribution. We also created a series of structured matrices by altering the random matrices to incorporate patterns of pairwise species segregation and aggregation. We next screened each algorithm-index combination with the random and structured matrices to determine which tests had low Type I error rates and good power for detecting segregated and aggregated species distributions. In our benchmark tests, the best-performing null model does not constrain species richness, but assigns individuals to matrix cells proportional to the observed row and column marginal distributions until, for each row and column, total abundances are reached. Using this null model algorithm with a set of four covariance metrics, we tested for patterns of species segregation and aggregation in a collection of 149 empirical abundance matrices and 36 interaction matrices collated from published papers and posted data sets. More than 80% of the matrices were significantly segregated, which reinforces a previous meta-analysis of presence/absence matrices. However, using two of the metrics we detected a significant pattern of aggregation for plants and for the interaction matrices (which include plant-pollinator data sets). These results suggest that abundance matrices, analyzed with an appropriate null model, may be a powerful tool for quantifying patterns of species segregation and aggregation.     

Specialization and interaction strength in a tropical plant-frugivore network differ among forest strata

The degree of interdependence and potential for shared coevolutionary history of frugivorous animals and fleshy-fruited plants are contentious topics. Recently, network analyses revealed that mutualistic relationships between fleshy-fruited plants and frugivores are mostly built upon generalized associations. However, little is known about the determinants of network structure, especially from tropical forests where plants' dependence on animal seed dispersal is particularly high. Here, we present an in-depth analysis of specialization and interaction strength in a plant-frugivore network from a Kenyan rain forest. We recorded fruit removal from 33 plant species in different forest strata (canopy, midstory, understory) and habitats (primary and secondary forest) with a standardized sampling design (3447 interactions in 924 observation hours). We classified the 88 frugivore species into guilds according to dietary specialization (14 obligate, 28 partial, 46 opportunistic frugivores) and forest dependence (50 forest species, 38 visitors). Overall, complementary specialization was similar to that in other plant-frugivore networks. However, the plant-frugivore interactions in the canopy stratum were less specialized than in the mid- and understory, whereas primary and secondary forest did not differ. Plant specialization on frugivores decreased with plant height, and obligate and partial frugivores were less specialized than opportunistic frugivores. The overall impact of a frugivore increased with the number of visits and the specialization on specific plants. Moreover, interaction strength of frugivores differed among forest strata. Obligate frugivores foraged in the canopy where fruit resources were abundant, whereas partial and opportunistic frugivores were more common on mid- and understory plants, respectively. We conclude that the vertical stratification of the frugivore community into obligate and opportunistic feeding guilds structures this plant-frugivore network. The canopy stratum comprises stronger links and generalized associations, whereas the lower strata are composed of weaker links and more specialized interactions. Our results suggest that seed-dispersal relationships of plants in lower forest strata are more prone to disruption than those of canopy trees.     

Digital data sources and methods for conservation culturomics

Ongoing loss of biological diversity is primarily the result of unsustainable human behavior. Thus, the long-term success of biodiversity conservation depends on a thorough understanding of human–nature interactions. Such interactions are ubiquitous but vary greatly in time and space and are difficult to monitor efficiently at large spatial scales. However, the Information Age also provides new opportunities to better understand human–nature interactions because many aspects of daily life are recorded in a variety of digital formats. The emerging field of conservation culturomics aims to take advantage of digital data sources and methods to study human–nature interactions and thus to provide new tools for studying conservation at relevant temporal and spatial scales. Nevertheless, technical challenges associated with the identification, access, and analysis of relevant data hamper the wider adoption of culturomics methods. To help overcome these barriers, we propose a conservation culturomics research framework that addresses data acquisition, analysis, and inherent biases. The main sources of culturomic data include web pages, social media, and other digital platforms from which metrics of content and engagement can be obtained. Obtaining raw data from these platforms is usually desirable but requires careful consideration of how to access, store, and prepare the data for analysis. Methods for data analysis include network approaches to explore connections between topics, time-series analysis for temporal data, and spatial modeling to highlight spatial patterns. Outstanding challenges associated with culturomics research include issues of interdisciplinarity, ethics, data biases, and validation. The practical guidance we offer will help conservation researchers and practitioners identify and obtain the necessary data and carry out appropriate analyses for their specific questions, thus facilitating the wider adoption of culturomics approaches for conservation applications.     

Building phenological models from presence/absence data for a butterfly fauna.

Species phenology is increasingly being used to explore the effects of climate change and other environmental stressors. Long-term monitoring data sets are essential for understanding both patterns manifest by individual species and more complex patterns evident at the community level. This study used records of 78 butterfly species observed on 626 days across 27 years at a site in northern California, USA, to build quadratic logistic regression models of the observation probability of each species for each day of the year. Daily species probabilities were summed to develop a potential aggregate species richness (PASR) model, indicating expected daily species richness. Daily positive and negative contributions to PASR were calculated, which can be used to target optimum sampling time frames. Residuals to PASR indicate a rate of decline of 0.12 species per year over the course of the study. When PASR was calculated for wet and dry years, wet years were found to delay group phenology by up to 17 days and reduce the maximum annual expected species from 32.36 to 30. Three tests to determine how well the PASR model reflected the butterfly fauna dynamics were all positive: We correlated probabilities developed with species presence/absence data to observed abundance by species, tested species' predicted phenological patterns against known biological characteristics, and compared the PASR curve to a spline-fitted curve calculated from the original species richness observations. Modeling individual species' flight windows was possible from presence/absence data, an approach that could be used on other similar records for butterfly communities with seasonal phenologies, and for common species with far fewer dates than used here. It also provided a method to assess sample frequency guidelines for other butterfly monitoring programs.     

Measurement error causes scale-dependent threshold erosion of biological signals in animal movement data.

Recent advances in telemetry technology have created a wealth of tracking data available for many animal species moving over spatial scales from tens of meters to tens of thousands of kilometers. Increasingly, such data sets are being used for quantitative movement analyses aimed at extracting fundamental biological signals such as optimal searching behavior and scale-dependent foraging decisions. We show here that the location error inherent in various tracking technologies reduces the ability to detect patterns of behavior within movements. Our analyses endeavored to set out a series of initial ground rules for ecologists to help ensure that sampling noise is not misinterpreted as a real biological signal. We simulated animal movement tracks using specialized random walks known as Lévy flights at three spatial scales of investigation: 100-km, 10-km, and 1-km maximum daily step lengths. The locations generated in the simulations were then blurred using known error distributions associated with commonly applied tracking methods: the Global Positioning System (GPS), Argos polar-orbiting satellites, and light-level geolocation. Deviations from the idealized Lévy flight pattern were assessed for each track after incrementing levels of location error were applied at each spatial scale, with additional assessments of the effect of error on scale-dependent movement patterns measured using fractal mean dimension and first-passage time (FPT) analyses. The accuracy of parameter estimation (Lévy mu, fractal mean D, and variance in FPT) declined precipitously at threshold errors relative to each spatial scale. At 100-km maximum daily step lengths, error standard deviations of > or = 10 km seriously eroded the biological patterns evident in the simulated tracks, with analogous thresholds at the 10-km and 1-km scales (error SD > or = 1.3 km and 0.07 km, respectively). Temporal subsampling of the simulated tracks maintained some elements of the biological signals depending on error level and spatial scale. Failure to account for large errors relative to the scale of movement can produce substantial biases in the interpretation of movement patterns. This study provides researchers with a framework for understanding the limitations of their data and identifies how temporal subsampling can help to reduce the influence of spatial error on their conclusions.