Evaluating citizen science data for forecasting species responses to national forest management

The extensive spatial and temporal coverage of many citizen science datasets (CSD) makes them appealing for use in species distribution modeling and forecasting. However, a frequent limitation is the inability to validate results. Here, we aim to assess the reliability of CSD for forecasting species occurrence in response to national forest management projections (representing 160,366 km²) by comparison against forecasts from a model based on systematically collected colonization–extinction data. We fitted species distribution models using citizen science observations of an old‐forest indicator fungus Phellinus ferrugineofuscus. We applied five modeling approaches (generalized linear model, Poisson process model, Bayesian occupancy model, and two MaxEnt models). Models were used to forecast changes in occurrence in response to national forest management for 2020‐2110. Forecasts of species occurrence from models based on CSD were congruent with forecasts made using the colonization–extinction model based on systematically collected data, although different modeling methods indicated different levels of change. All models projected increased occurrence in set‐aside forest from 2020 to 2110: the projected increase varied between 125% and 195% among models based on CSD, in comparison with an increase of 129% according to the colonization–extinction model. All but one model based on CSD projected a decline in production forest, which varied between 11% and 49%, compared to a decline of 41% using the colonization–extinction model. All models thus highlighted the importance of protected old forest for P. ferrugineofuscus persistence. We conclude that models based on CSD can reproduce forecasts from models based on systematically collected colonization–extinction data and so lead to the same forest management conclusions. Our results show that the use of a suite of models allows CSD to be reliably applied to land management and conservation decision making, demonstrating that widely available CSD can be a valuable forecasting resource.     

Spatial and temporal patterns of size-at-sex-change in two exploited coastal fish

Fisheries are known to selectively remove larger and older individuals from wild populations. In sequential hermaphrodites, size-selectivity also becomes ‘sex-selectivity’, as the largest and oldest individuals of a sex-changing population primarily belong to one sex. Plasticity in size-at-sex-change is believed to circumvent the undesirable effects of gamete limitation and low effective population size. Here we present data of two commercially exploited sea bream species in different biogeographic regions, one protandrous (male-first) and one protogynous (female-first), respectively exhibiting spatial and temporal fluctuation in size-at-sex-change. By contrasting the patterns found against recent evidence of similar phenomena in other species, we discuss life-history strategies that favour reproductive success in different environmental scenarios. Size-at-sex-change may be for sequential hermaphrodites as important a life-history parameter as size-at-maturity or age-at-size. Consequently, the demographic consequences of altered size-at-sex-change should be considered explicitly in stock management and assessment.     

Estimating linear temporal trends from aggregated environmental monitoring data

Trend estimates are often used as part of environmental monitoring programs. These trends inform managers (e.g., are desired species increasing or undesired species decreasing?). Data collected from environmental monitoring programs is often aggregated (i.e., averaged), which confounds sampling and process variation. State-space models allow sampling variation and process variations to be separated. We used simulated time-series to compare linear trend estimations from three state-space models, a simple linear regression model, and an auto-regressive model. We also compared the performance of these five models to estimate trends from a long term monitoring program. We specifically estimated trends for two species of fish and four species of aquatic vegetation from the Upper Mississippi River system. We found that the simple linear regression had the best performance of all the given models because it was best able to recover parameters and had consistent numerical convergence. Conversely, the simple linear regression did the worst job estimating populations in a given year. The state-space models did not estimate trends well, but estimated population sizes best when the models converged. We found that a simple linear regression performed better than more complex autoregression and state-space models when used to analyze aggregated environmental monitoring data.     

Extinctions in competitive communities forced by coloured environmental variation

Understanding the relationships between environmental fluctuations, population dynamics and species interactions in natural communities is of vital theoretical and practical importance. This knowledge is essential in assessing extinction risks in communities that are, for example, pressed by changing environmental conditions and increasing exploitation. We developed a model of density dependent population renewal, in a Lotka–Volterra competitive community context, to explore the significance of interspecific interactions, demographic stochasticity, population growth rate and species abundance on extinction risk in populations under various autocorrelation (colour) regimes of environmental forcing. These factors were evaluated in two cases, where either a single species or the whole community was affected by the external forcing. Species' susceptibility to environmental noise with different autocorrelation structure depended markedly on population dynamics, species' position in the abundance hierarchy and how similarly community members responded to external forcing. We also found interactions between demographic stochasticity and environmental noise leading to a reversal in extinction probabilities from under- to overcompensatory dynamics. We compare our results with studies of single species populations and contrast possible mechanisms leading to extinctions. Our findings indicate that abundance rank, the form of population dynamics, and the colour of environmental variation interact in affecting species extinction risk. These interactions are further modified by interspecific interactions within competitive communities as the interactions filter and modulate the environmental noise.     

Partitioning the variation among spatial,temporal and environmental components in a multivariate data set

Abstract We propose a method of partitioning the variation in a multivariate set of data according to (i) environmental variables, (ii) variables describing the spatial structure in the data and (iii) temporal variables. This method is an extension of an existing method for partialling out the spatial component of environmental variation, using canonical analysis. Our proposed method extends this approach by including temporal variables in the analysis. Thus, the partitioning of variation for a data matrix of species’abundances or biomass can include, by our methodology, the following components: (1) pure environmental, (2) pure spatial, (3) pure temporal, (4) pure spatial component of environmental, (5) pure temporal component of environmental, (6) pure combined spatial/temporal component, (7) combined spatial/temporal component of environmental and (8) unexplained. In addition, permutation testing accompanying the analyses allows tests of significance for the relationship between the different components and the species data. We illustrate the method with a set of survey data of penaeid species (prawns) obtained on the far northern Great Barrier Reef, Australia. This extension is a useful tool for multivariate analysis of ecological data from surveys, where space, time and environment commonly overlap and are important influences on observed variation.     

Shifting trends: detecting environmentally mediated regulation in long-lived marine vertebrates using time-series data

Assessing the status and trends in animal populations is essential for effective species conservation and management practices. However, unless time-series abundance data demonstrate rapid and reliable fluctuations, objective appraisal of directionality of trends is problematic. We adopted a multiple-working hypotheses approach based on information-theoretic and Bayesian multi-model inference to examine the population trends and form of intrinsic regulation demonstrated by a long-lived species, the southern elephant seal. We also determined the evidence for density dependence in 11 other well-studied marine mammal species. (1) We tested the type of population regulation for elephant seals from Marion Island (1986–2004) and from 11 other marine mammal species, and (2) we described the trends and behavior of the 19-year population time series at Marion Island to identify changes in population trends. We contrasted five plausible trend models using information-theoretic and Bayesian-inference estimates of model parsimony. Our analyses identified two distinct phases of population growth for this population with the inflexion occurring in 1998. Thus, the population decreased between 1986 and 1997 (−3.7% per annum) and increased between 1997 and 2004 (1.9% per annum). An index of environmental stochasticity, the Southern Oscillation Index, explained some of the variance in r and N. We determined analytically that there was good evidence for density dependence in the Marion Island population and that density dependence was widespread among marine mammal species (67% of species showed evidence for population regulation). This approach demonstrates the potential functionality of a relatively simple technique that can be applied to short time series to identify the type of regulation, and the uncertainty associated with the phenomenon, operating in populations of large mammals.     

Functional characters combined with null models reveal inconsistency in mechanisms of species turnover in lacustrine fish communities

Functional characters have the potential to act as indicators of species turnover between local communities. Null models provide a powerful statistical approach to test for patterns using functional character information. A combined null model/functional character approach provides the ability to distinguish between the effect of competition and environmental filtering on species turnover. We measured 13 functional characters relating directly to resource use for the fish species found in French lakes. We combined this functional character data with a null model approach to test whether co-occurring species overlapped more or less than expected at random for four primary niche axes. We used an environmentally constrained null model approach to determine if the same mechanisms were responsible for species turnover at different sections of the altitudinal gradient. Functional diversity indices were used to examine the variation in functional character diversity with altitude, as a test of the hypothesis that competitive intensity decreases with increasing environmental adversity. The unconstrained null model showed that environmental filtering was the dominant influence on species turnover between lakes. In the constrained null model, there was much less evidence for environmental filtering, emphasising the strong effect of altitude on turnover in functional character values between local communities. Different results were obtained for low-altitude and high-altitude lake subsets, with more evidence for the effect of environmental filtering being found in the high-altitude lakes. This demonstrates that different processes may influence species turnover throughout an environmental gradient. Functional diversity values showed a slight decrease with altitude, indicating that there was only weak evidence that competitive intensity decreased with increasing altitude. Variation resource availability and environmental stress probably cause the observed turnover in functional characters along the altitudinal gradient, though the effects of dispersal limitation and species introductions in high-altitude lakes cannot be ruled out.     

Comparative landscape genetic analysis of three Pacific salmon species from subarctic North America

We examined the assumption that landscape heterogeneity similarly influences the spatial distribution of genetic diversity in closely related and geographically overlapping species. Accordingly, we evaluated the influence of watershed affiliation and nine habitat variables from four categories (spatial isolation, habitat size, climate, and ecology) on population divergence in three species of Pacific salmon (Oncorhynchus tshawytscha, O. kisutch, and O. keta) from three contiguous watersheds in subarctic North America. By incorporating spatial data we found that the three watersheds did not form the first level of hierarchical population structure as predicted. Instead, each species exhibited a broadly similar spatial pattern: a single coastal group with populations from all watersheds and one or more inland groups primarily in the largest watershed. These results imply that the spatial scale of conservation should extend across watersheds rather than at the watershed level which is the scale for fishery management. Three independent methods of multivariate analysis identified two variables as having influence on population divergence across all watersheds: precipitation in all species and subbasin area (SBA) in Chinook. Although we found general broad-scale congruence in the spatial patterns of population divergence and evidence that precipitation may influence population divergence in each species, we also found differences in the level of population divergence (coho > Chinook and chum) and evidence that SBA may influence population divergence only in Chinook. These differences among species support a species-specific approach to evaluating and planning for the influence of broad-scale impacts such as climate change.     

Asymmetric introgression between fishes in the Red River basin of Texas is associated with variation in water quality

When ecologically divergent taxa encounter one another, hybrid zones can form when reproductive isolation is incomplete. The location of such hybrid zones can be influenced by environmental variables, and an ecological context can provide unique insights into the mechanisms by which species diverge and are maintained. Two ecologically differentiated species of small benthic fishes, the endemic and imperiled prairie chub, Macrhybopsis australis, and the shoal chub, Macrhybopsis hyostoma, are locally sympatric within the upper Red River Basin of Texas. We integrated population genomic data and environmental data to investigate species divergence and the maintenance of species boundaries in these two species. We found evidence of advanced‐generation asymmetric hybridization and introgression, with shoal chub alleles introgressing more frequently into prairie chubs than the reciprocal. Using a Bayesian Genomic Cline framework, patterns of genomic introgression were revealed to be quite heterogeneous, yet shoal chub alleles were found to have likely selectively introgressed across species boundaries significantly more often than prairie chub alleles, potentially explaining some of the observed asymmetry in hybridization. These patterns were remarkably consistent across two sampled geographic regions of hybridization. Several environmental variables were found to significantly predict individual admixture, suggesting ecological isolation might maintain species boundaries.     

Interspecific and temporal variation in herbivore responses to hybrid willows

We studied herbivory of two species of willows (Salix sericea and S. eriocephala) and their interspecific hybrids to test alternative hypotheses concerning the effects of hybridization on plant resistance. Individually marked plants were identified using morphological traits in the field and random amplified polymorphic DNA (RAPD) band analysis was used to verify the genetic status of many parental and hybrid plants. The desities of 12 herbivore species on plants in the field were compared between two parents and their F₂-type hybrids. We found about equal support for the additive, dominance, and hybrid susceptibility hypotheses over 4 years. In one year, one species supported the hybrid resistance hypothesis. Guild membership was not a good predictor of similar responses of species to hybrid versus parental plants. There were marked differences in support for particular hypotheses among years for four herbivore species. This study demonstrates the diversity of responses of phytophages in response to interspecific hybridization, and indicates that year-to-year variation in relative resistance of hybrid plants can be important.     

Patterns of spatial and temporal variation of macrofauna under boulders in a sheltered boulder field

Abstract Spatial and temporal patterns of abundance of animals and plants must be quantified before models to explain distributions can be developed. These patterns also provide essential data for measuring potential effects of environmental disturbances. Studies in many different habitats have shown that most organisms, particularly invertebrates, have highly variable and interactive patterns of abundance, with much variability at the smallest temporal and spatial scales. Intertidal boulder fields in New South Wales, Australia, support a diverse fauna, many species of which are relatively rare. These habitats are commonly found near rock‐platforms and in sheltered estuaries and are subjected to many human disturbances. Although there have been a few studies on the fauna in boulder fields, none has documented variability of the assemblage using multivariate and univariate techniques and most studies have not incorporated different spatial and temporal scales. This study quantifies spatial variation at three scales (metres, tens of metres alongshore and tens of metres upshore) and temporal variation at two scales (3 months and 2 years) of the assemblage of molluscs and echinoderms in a sheltered boulder field subjected to little natural or human disturbance. Multivariate analyses revealed that each site contained a distinct assemblage, mainly due to the relative abundances of a few species. Most species, those generally only found under boulders and common, widespread species, had considerable spatial variability in abundances, with more than 90% measured at the smallest scale, that is metre to metre within a site. Changes in abundances over 3 months or 2 years varied among species and sites in unpredictable ways. These data show that sampling designs to measure impacts on these fauna will need to be complex and must incorporate a number of spatial and temporal scales if they are to be able to detect impact against such a variable background.     

Spatial and temporal variation in population trends in a long‐distance migratory bird

Aim Over the past three decades, evidence has been growing that many Afro‐Palaearctic migratory bird populations have suffered sustained and severe declines. As causes of these declines exist across both the breeding and non‐breeding season, identifying potential drivers of population change is complex. In order to explore the roles of changes in regional and local environmental conditions on population change, we examine spatial and temporal variation in population trajectories of one of Europe’s most abundant Afro‐Palaearctic summer migrants, the willow warbler, Phylloscopus trochilus. Location Britain and Ireland. Methods We use national survey data from Britain and Ireland (BBS: BTO/RSPB/JNCC Breeding Bird Survey and CBS: BWI/NPWS/Heritage Council Countryside Breeding Survey) from 1994 to 2006 to model the spatial and temporal variation in willow warbler population trends. Results Across Britain and Ireland, population trends follow a gradient from sharp declines in the south and east of England to shallow declines and/or slight increases in parts of north and west England, across Scotland and Ireland. Decreasing the spatial scale of analysis reveals variation in both the rate and spatial extent of population change within central England and the majority of Scotland. The rates of population change also vary temporally; declines in the south of England are shallower now than at the start of the time series, whereas populations further north in Britain have undergone periods of increase and decline. Main conclusion These patterns suggest that regional‐scale drivers, such as changing climatic conditions, and local‐scale processes, such as habitat change, are interacting to produce spatially variable population trends. We discuss the potential mechanisms underlying these interactions and the challenges in addressing such changes at scales relevant to migratory species.     

Estimating the variation,autocorrelation, and environmental sensitivity of phenotypic selection

Despite considerable interest in temporal and spatial variation of phenotypic selection, very few methods allow quantifying this variation while correctly accounting for the error variance of each individual estimate. Furthermore, the available methods do not estimate the autocorrelation of phenotypic selection, which is a major determinant of eco‐evolutionary dynamics in changing environments. We introduce a new method for measuring variable phenotypic selection using random regression. We rely on model selection to assess the support for stabilizing selection, and for a moving optimum that may include a trend plus (possibly autocorrelated) fluctuations. The environmental sensitivity of selection also can be estimated by including an environmental covariate. After testing our method on extensive simulations, we apply it to breeding time in a great tit population in the Netherlands. Our analysis finds support for an optimum that is well predicted by spring temperature, and occurs about 33 days before a peak in food biomass, consistent with what is known from the biology of this species. We also detect autocorrelated fluctuations in the optimum, beyond those caused by temperature and the food peak. Because our approach directly estimates parameters that appear in theoretical models, it should be particularly useful for predicting eco‐evolutionary responses to environmental change.     

Pattern of variation in avian population growth rates

A central question in population ecology is to understand why population growth rates differ over time. Here, we describe how the long-term growth of populations is not only influenced by parameters affecting the expected dynamics, for example form of density dependence and specific population growth rate, but is also affected by environmental and demographic stochasticity. Using long-term studies of fluctuations of bird populations, we show an interaction between the stochastic and the deterministic components of the population dynamics: high specific growth rates at small densities r(1) are typically positively correlated with the environmental variance sigma(e)(2). Furthermore, theta, a single parameter describing the form of the density regulation in the theta-logistic density-regulation model, is negatively correlated with r(1). These patterns are in turn correlated with interspecific differences in life-history characteristics. Higher specific growth rates, larger stochastic effects on the population dynamics and stronger density regulation at small densities are found in species with large clutch sizes or high adult mortality rates than in long-lived species. Unfortunately, large uncertainties in parameter estimates, as well as strong stochastic effects on the population dynamics, will often make even short-term population projections unreliable. We illustrate that the concept of population prediction interval can be useful in evaluating the consequences of these uncertainties in the population projections for the choice of management actions.     

Drivers of global variation in land ownership

Land ownership shapes natural resource management and social–ecological resilience, but the factors determining ownership norms in human societies remain unclear. Here we conduct a global empirical test of long‐standing theories from ecology, economics and anthropology regarding potential drivers of land ownership and territoriality. Prior theory suggests that resource defensibility, subsistence strategies, population pressure, political complexity and cultural transmission mechanisms may all influence land ownership. We applied multi‐model inference procedures based on logistic regression to cultural and environmental data from 102 societies, 71 with some form of land ownership and 31 with no land ownership. We found an increased probability of land ownership in mountainous environments, where patchy resources may be more cost effective to defend via ownership. We also uncovered support for the role of population pressure, with a greater probability of land ownership in societies living at higher population densities. Our results also show more land ownership when neighboring societies also practiced ownership. We found less support for variables associated with subsistence strategies and political complexity.     

Assessing factors associated with changes in the numbers of birds visiting gardens in winter: Are predators partly to blame?

The factors governing the recent declines observed in many songbirds have received much research interest, in particular whether increases of avian predators have had a negative effect on any of their prey species. In addition, further discussion has centered on whether or not the choice of model formulation has an effect on model inference. The study goal was to evaluate changes in the number of 10 songbird species in relation to a suite of environmental covariates, testing for any evidence in support of a predator effect using multiple model formulations to check for consistency in the results. We compare two different approaches to the analysis of long‐term garden bird monitoring data. The first approach models change in the prey species between 1970 and 2005 as a function of environmental covariates, including the abundance of an avian predator, while the second uses a change–change approach. Significant negative relationships were found between Eurasian Sparrowhawk Accipiter nisus and three of the 10 species analyzed, namely house Sparrow Passer domesticus, starling Sturnus vulgaris, and blue tit Cyanistes caeruleus. The results were consistent under both modeling approaches. It is not clear if this is a direct negative impact on the overall populations of these species or a behavioral response of the prey species to avoid feeding stations frequented by Sparrowhawks (which may in turn have population consequences, by reducing available resources). The species showing evidence of negative effects of Sparrowhawks were three of the four species most at risk to Sparrowhawk predation according to their prevalence in the predator's diet. The associations could be causal in nature, although in practical terms the reduction in the rate of change in numbers visiting gardens accredited to Sparrowhawks is relatively small, and so unlikely to be the main driver of observed population declines.     

Matching the forecast horizon with the relevant spatial and temporal processes and data sources

Most phenomenological, statistical models used to generate ecological forecasts take either a time-series approach, based on long-term data from one location, or a space-for-time approach, based on data describing spatial patterns across environmental gradients. However, the magnitude and even the sign of environment–response relationships detected using these two approaches often differs, leading to contrasting predictions about responses to future environmental change. Here we consider how the forecast horizon determines whether more accurate predictions come from the time-series approach, the space-for-time approach or a combination of the two. As proof of concept, we use simulated case studies to show that forecasts for short and long forecast horizons need to focus on different ecological processes, which are reflected in different kinds of data. First, we simulated population or community dynamics under stationary temperature using two simple, mechanistic models. Second, we fit statistical models to the simulated data using a time-series approach, a space-for-time approach or a weighted average. We then forecast the response to a temperature increase using the statistical models, and compared these forecasts to temperature effects simulated by the mechanistic models. We found that the time-series approach made accurate short-term predictions because it captured initial conditions and effects of fast processes such as birth and death. The space-for-time approach made more accurate long-term predictions because it better captured the influence of slower processes such as evolutionary and ecological selection. The weighted average made accurate predictions at all time scales, including intermediate time-scales where the other two approaches performed poorly. A weighted average of time-series and space-for-time approaches shows promise, but making this weighted model operational will require new research to predict the rate at which slow processes begin to influence dynamics.     

Adaptive divergence in a high gene flow environment: Hsc70 variation in the European flounder (Platichthys flesus L.)

Little is known about local adaptations in marine fishes since population genetic surveys in these species have typically not applied genetic markers subject to selection. In this study, we used a candidate gene approach to investigate adaptive population divergence in the European flounder (Platichthys flesus L.) throughout the northeastern Atlantic. We contrasted patterns of genetic variation in a presumably neutral microsatellite baseline to patterns from a heat-shock cognate protein gene, Hsc70. Using two different neutrality tests we found that the microsatellite data set most likely represented a neutral baseline. In contrast, Hsc70 strongly deviated from neutral expectations. Importantly, when estimating standardized levels of population divergence (F(ST)'), we also found a large discrepancy in the patterns of structuring in the two data sets. Thus, samples grouped according to geographical or historical proximity with regards to microsatellites, but according to environmental similarities with regards to Hsc70. The differences between the data sets were particularly pronounced in pairwise comparisons involving populations in the western and central Baltic Sea. For instance, the genetic differentiation between geographically close Baltic Sea and North Sea populations was found to be 0.02 and 0.45 for microsatellites and Hsc70 respectively. Our results strongly suggest adaptive population divergence and indicate local adaptations at the DNA level in a background of high levels of gene flow, typically found in many marine fish species. Furthermore, this study highlights the usefulness of the candidate gene approach for demonstrating local selection in non-model organisms such as most marine fishes.     

Ecological niche variation in the Wilson's warbler Cardellina pusilla complex

Wilson's warbler comprises three subspecies separated into two geographic groups: C. p. pusilla that breeds in eastern North America; and C. p. pileolata and C. p. chryseola that breed in western North America. Given the differences between the groups in genetics, morphology, habitat use, and population decline, we tested for ecological niche similarity in both their breeding and wintering distribution using niche modeling based on temperature and precipitation data. We first conducted an inter‐prediction approach considering the percent of summer and winter localities of one group that are predicted by the potential distribution of the alternate group. We also applied a null model approach that compares self‐predictions and pseudoreplicates of each group to indicate similarity, divergence, or indeterminate niche overlap. Finally, we compared ecological distances between and within groups using the Gower similarity equation. We found that the western group had an ecological niche of broader climatic conditions, while the eastern group had a narrower ecological niche. The inter‐prediction approach showed that, for both summering and wintering ranges, ecological niche models of the western group predicted ～50% of the observed distribution of the eastern group, whereas eastern group models predicted < 18% of the western group distribution. The null model approach found that similarity in ecological niches was indeterminate, possibly due to the large area occupied by the two groups; but it suggests a more restricted set of climatic conditions of the eastern group distribution. However, the Gower coefficients demonstrated that the ecological distance between the two geographic groups was larger than the ecological distance within groups, indicating distinct ecological niches. Overall, our results support the hypothesis that the eastern and western groups of Wilson's warbler are two cryptic species; this should be taken into consideration for future analyses, particularly with respect to vulnerability categorization and conservation efforts.