Using circuit theory to model connectivity in ecology, evolution, and conservation

Connectivity among populations and habitats is important for a wide range of ecological processes. Understanding, preserving, and restoring connectivity in complex landscapes requires connectivity models and metrics that are reliable, efficient, and process based. We introduce a new class of ecological connectivity models based in electrical circuit theory. Although they have been applied in other disciplines, circuit-theoretic connectivity models are new to ecology. They offer distinct advantages over common analytic connectivity models, including a theoretical basis in random walk theory and an ability to evaluate contributions of multiple dispersal pathways. Resistance, current, and voltage calculated across graphs or raster grids can be related to ecological processes (such as individual movement and gene flow) that occur across large population networks or landscapes. Efficient algorithms can quickly solve networks with millions of nodes, or landscapes with millions of raster cells. Here we review basic circuit theory, discuss relationships between circuit and random walk theories, and describe applications in ecology, evolution, and conservation. We provide examples of how circuit models can be used to predict movement patterns and fates of random walkers in complex landscapes and to identify important habitat patches and movement corridors for conservation planning.     

The theory of power and efficiency in ecology

Some previous work on the theory of power and optimal efficiency in ecology is shown to be either incorrect or of limited applicability. The standard analysis based on Atwood's machine is not valid, and the optimal efficiency for maximum power production depends on the characteristics of the individual system. For optimally foraging aquatic organisms the theoretical efficiency is less than 50% when the hydrodynamic flow is laminar, but under turbulent conditions the optimal efficiency can rise as high as 67%. Despite these restrictions it still appears plausible that ecological efficiency should be the same for broad classes of living organisms.     

The potential for citizen science to produce reliable and useful information in ecology

We examined features of citizen science that influence data quality, inferential power, and usefulness in ecology. As background context for our examination, we considered topics such as ecological sampling (probability based, purposive, opportunistic), linkage between sampling technique and statistical inference (design based, model based), and scientific paradigms (confirmatory, exploratory). We distinguished several types of citizen science investigations, from intensive research with rigorous protocols targeting clearly articulated questions to mass-participation internet-based projects with opportunistic data collection lacking sampling design, and examined overarching objectives, design, analysis, volunteer training, and performance. We identified key features that influence data quality: project objectives, design and analysis, and volunteer training and performance. Projects with good designs, trained volunteers, and professional oversight can meet statistical criteria to produce high-quality data with strong inferential power and therefore are well suited for ecological research objectives. Projects with opportunistic data collection, little or no sampling design, and minimal volunteer training are better suited for general objectives related to public education or data exploration because reliable statistical estimation can be difficult or impossible. In some cases, statistically robust analytical methods, external data, or both may increase the inferential power of certain opportunistically collected data. Ecological management, especially by government agencies, frequently requires data suitable for reliable inference. With standardized protocols, state-of-the-art analytical methods, and well-supervised programs, citizen science can make valuable contributions to conservation by increasing the scope of species monitoring efforts. Data quality can be improved by adhering to basic principles of data collection and analysis, designing studies to provide the data quality required, and including suitable statistical expertise, thereby strengthening the science aspect of citizen science and enhancing acceptance by the scientific community and decision makers.     

Estimating growth charts via nonparametric quantile regression: a practical framework with application in ecology

We discuss a practical and effective framework to estimate reference growth charts via regression quantiles. Inequality constraints are used to ensure both monotonicity and non-crossing of the estimated quantile curves and penalized splines are employed to model the nonlinear growth patterns with respect to age. A companion R package is presented and relevant code discussed to favour spreading and application of the proposed methods.     

Logistic regression and fuzzy logic as a classification method for feral fish sampling sites

This study presents a classification method combining logistic regression and fuzzy logic in the determination of sampling sites for feral fish, Nile Tilapia (Tilapia rendalli). This method statistically analyzes the variable domains involved in the problem, by using a logistic regression model. This in turn generates the knowledge necessary to construct the rule base and fuzzy clusters of the fuzzy inference system (FIS) variables. The proposed hybrid method was validated using three fish stress indices; the Fulton Condition Factor (FCF) and the gonadossomatic and hepatossomatic indices (GSI and HSI, respectively), from fish sampled at 3 different locations in the Rio de Janeiro State. A multinomial logistic regression allowed for the FIS construction of the proposed method and both statistical approaches, when combined, complemented each other satisfactorily, allowing for the construction of an efficient classification method regarding feral fish sampling sites that, in turn, has great value regarding fish captures and fishery resource management.     

A case for quantile regression in behavioral ecology: getting more out of flight initiation distance data

A central goal of behavioral ecology is to quantify and explain variation in behavior. While much previous work has focused on the differences in mean behavior across groups or treatments, we present a complementary approach studying changes in the distribution of the response variable. This is important because changes in the edges of a distribution may be more informative than changes in the mean if behavior at the edges of a distribution better reflects behavioral constraints. Quantile regression estimates the rate of change of conditional quantiles of a response variable and thus allows the study of changes in any part of its distribution. Although quantile regression is gaining popularity in the ecological literature, it is strikingly unused in behavioral ecology. Here, we demonstrate the usefulness of this method by analyzing the relationship between the starting distance (SD) at which an observer approach a focal animal and its flight initiation distance (FID, the distance between the observer and the animal when it decides to flee). In particular, we used a simple model of flight initiation distance to show that in most situations ordinary least-square regression cannot be used to analyse the SD–FID relationship. Quantile regression conducted on the lowest quantiles appears more robust and we applied this approach to data from four bird species. Overall, changes in the lowest FID values appeared to be the most informative to determine if a species displays a “flush early” strategy, a strategy which has been hypothesized to be a general rule. We hope this example will bring quantile regression to the attention of behavioral ecologists as a valuable tool to add to their statistical toolbox.     

Social information use is a process across time, space, and ecology, reaching heterospecifics

Decision making can be facilitated by observing other individuals faced with the same or similar problem, and recent research suggests that this social information use is a widespread phenomenon. Implications of this are diverse and profound: for example, social information use may trigger cultural evolution, affect distribution and dispersal of populations, and involve intriguing cognitive traits. We emphasize here that social information use is a process consisting of the scenes of (1) event, (2) observation, (3) decision, and (4) consequence, where the initial event is a scene in such a process of another individual. This helps to construct a sound conceptual framework for measuring and studying social information use. Importantly, the potential value of social information is affected by the distance in time, space, and ecology between the initial observation and eventual consequence of a decision. Because negative interactions between individuals (such as direct and apparent competition) also depend on the distance between individuals along these dimensions, the potential value of information and the negative interactions may form a trade-off situation. Optimal solutions to this trade-off can result in adaptively extended social information use, where using information gathered some time ago, some distance away, and from ecologically different individuals is preferred. Conceivably, using information gathered from a heterospecific individual might often be optimal. Many recent studies demonstrate that social information use does occur between species, and the first review of published cases is provided here. Such interaction between species, especially in habitat selection, has important consequences for community ecology and conservation. Adaptively extended social information use may also be an important evolutionary force in guild formation. Complex coevolutionary patterns may result depending on the effect of information use on the provider of information.     

A linked model of animal ecology and human behavior for the management of wildlife tourism

Wildlife tourism attractions are characterized as having intricately coupled human-wildlife interactions. Accordingly, the ability to mitigate negative impacts of tourism on wildlife necessitates research into the ecology of the system and of the human dimensions, since plans aimed at optimizing wildlife fitness must also be acceptable to tourists. We developed an integrated systems dynamics model for the management of tourist-stingray interactions at ‘Stingray City Sandbar’ (SCS), Cayman Islands. The model predicts the state of the tourism attraction over time in relation to stingray population size, stingray life expectancy, and tourist visitation under various management scenarios. Stingray population data in the model comprised growth rates and survival estimates (from mark-and-recapture data) and mortality estimates. Inputted changes in their respective rates under different management scenarios were informed by previous research. Original research on the demand of heterogeneous tourist segments for management regulations via a stated choice model was used to calculate changes in the tourist population growth rate from data supplied by the Caymanian government. The management attributes to which tourists were responsive also have anticipated effects on stingray ecology (migration and mortality), and vice versa, thus linking the two components. We found that the model's predictions over a 25-year time span were sensitive to the stingray population growth rate and alternate management options. Under certain management scenarios, it was possible to maximize both the tourist segment in favor of no management and stingray numbers while reducing stingray health. However, the most effective relative strategy included a reduction in visitor density, restricted stingray interactions, and an imposition of a small fee. Over time, although fewer stingrays were predicted to remain at SCS, they would live longer and experience fewer stochastic disease events, and the desirable tourist segment was predicted to predominate. By understanding how management will affect tourist activities and their subsequent impacts on both wildlife health and visitor satisfaction, one can explore the management alternatives that would optimize both.     

Testing the metabolic theory of ecology: allometric scaling exponents in mammals

Many fundamental traits of species measured at different levels of biological organization appear to scale as a power law to body mass (M) with exponents that are multiples of 1/4. Recent work has united these relationships in a "metabolic theory of ecology" (MTE) that explains the pervasiveness of quarter-power scaling by its dependence on basal metabolic rate (B), which scales as M(0.75). Central to the MTE is theory linking the observed -0.25 scaling of maximum population growth rate (rm) and body mass to the 0.75 scaling of metabolic rate and body mass via relationships with age at first reproduction (alpha) derived from a general growth model and demographic theory. We used this theory to derive two further predictions: that age at first reproduction should scale inversely to mass-corrected basal metabolic rate alpha infinity (B/M)(-l) such that rm infinity (B/M)1. We then used phylogenetic generalized least squares and model selection methods to test the predicted scaling relationships using data from 1197 mammalian species. There was a strong phylogenetic signal in these data, highlighting the need to account for phylogeny in allometric studies. The 95% confidence intervals included, or almost included, the scaling exponent predicted by MTE for B infinity M(0.75), rm infinity M(-0.25), and rm infinity alpha(-1), but not for alpha infinity M(0.25) or the two predictions that we generated. Our results highlight a mismatch between theory and observation and imply that the observed -0.25 scaling of maximum population growth rate and body mass does not arise via the mechanism proposed in the MTE.     

Graph theory as a proxy for spatially explicit population models in conservation planning.

Spatially explicit population models (SEPMs) are often considered the best way to predict and manage species distributions in spatially heterogeneous landscapes. However, they are computationally intensive and require extensive knowledge of species' biology and behavior, limiting their application in many cases. An alternative to SEPMs is graph theory, which has minimal data requirements and efficient algorithms. Although only recently introduced to landscape ecology, graph theory is well suited to ecological applications concerned with connectivity or movement. This paper compares the performance of graph theory to a SEPM in selecting important habitat patches for Wood Thrush (Hylocichla mustelina) conservation. We use both models to identify habitat patches that act as population sources and persistent patches and also use graph theory to identify patches that act as stepping stones for dispersal. Correlations of patch rankings were very high between the two models. In addition, graph theory offers the ability to identify patches that are very important to habitat connectivity and thus long-term population persistence across the landscape. We show that graph theory makes very similar predictions in most cases and in other cases offers insight not available from the SEPM, and we conclude that graph theory is a suitable and possibly preferable alternative to SEPMs for species conservation in heterogeneous landscapes.     

Using single- and multi-target regression trees and ensembles to model a compound index of vegetation condition

An important consideration in conservation and biodiversity planning is an appreciation of the condition or integrity of ecosystems. In this study, we have applied various machine learning methods to the problem of predicting the condition or quality of the remnant indigenous vegetation across an extensive area of south-eastern Australia—the state of Victoria. The field data were obtained using the ‘habitat hectares’ approach. This rapid assessment technique produces multiple scores that describe the condition of various attributes of the vegetation at a given site. Multiple sites were assessed and subsequently circumscribed with GIS and remote-sensed data.     

Interspecific hybridization in ants: at the intersection of ecology, evolution, and behavior

Ants are social and are haplodiploid. This combination may allow the evolution of a variety of unusual genetic pathways to achieve reproductive success. These include hybridizing across species, differential use of sperm to create a hybrid worker population, and reproductively isolated gene pools that depend on each other for their survival. Although there are demonstrable costs for colony development and reproduction, these phenomena may nevertheless be relatively common in nature. The specific ecological advantages that favor the evolution of these reproductive modes remain to be discovered.     

Feeding behavior and ecology of shallow-water unstalked crinoids (echinodermata) in the Caribbean Sea

The feeding behavior, living position, and skeletal morphology of 8 species of reef-dwelling Caribbean comatulid crinoids are intimately related to the regime of water movement prevailing in the microhabitat. These adaptations are related to the dependence of the crinoid suspension-feeding mechanism on externally produced water movements for a continuous food supply. Greater numbers of co-occurring comatulid species (6 to 7) and larger populations have been found off Colombia and Panamá than off Curaçao and Jamaica (4 species). It is suggested that these differences may be related to increased or diversified primary productivity close to the larger land masses. Overal food availability as determined by primary productivity may, thus, be an important factor controlling the regional diversity and abundance of these species.     

Environmental and phylogenetic correlates of Eulemur behavior and ecology (Primates: Lemuridae)

The extent of diversity within closely related taxa may be a function of their shared evolutionary history or of selective forces causing adaptive changes. Examining variation among taxa within a single genus may help to identify flexibility in trait variation because recently diverged populations are more likely living in the environment of adaptation. This study examines correlates of diversity in Eulemur, a genus that has a wide distribution in a variety of habitat types throughout Madagascar. Previously published data were gathered from 11 long-term studies of Eulemur populations. Variables were categorized into multiple datasets: (1) environmental characteristics, (2) social organization, and (3) ecology, which included subsets for ranging behavior, diet, and activity budget. Molecular phylogenies from the literature were used to create the fourth and final dataset, a dissimilarity matrix of evolutionary distance among the 12 species and subspecies. Principal components and cluster analyses were implemented to examine the overall ecological similarity among Eulemur populations and to determine which variables contribute most to the variation among taxa. Partial Mantel tests were conducted to test for correlations among the dataset matrices. The results suggest ecological flexibility for the genus, in particular, populations in similar environments displayed similar activity patterns. In contrast, social organization showed no relationship with environment but was correlated with phylogenetic distance among populations. While Eulemur seems to demonstrate some flexibility for ecological adaptations, characteristics related to group size and sex ratio more closely track phylogeny and thus may be less flexible.Electronic supplementary material Supplementary material is available in the online version of this article at and is accessible to authorized users.     

Random forests for classification in ecology

Classification procedures are some of the most widely used statistical methods in ecology. Random forests (RF) is a new and powerful statistical classifier that is well established in other disciplines but is relatively unknown in ecology. Advantages of RF compared to other statistical classifiers include (1) very high classification accuracy; (2) a novel method of determining variable importance; (3) ability to model complex interactions among predictor variables; (4) flexibility to perform several types of statistical data analysis, including regression, classification, survival analysis, and unsupervised learning; and (5) an algorithm for imputing missing values. We compared the accuracies of RF and four other commonly used statistical classifiers using data on invasive plant species presence in Lava Beds National Monument, California, USA, rare lichen species presence in the Pacific Northwest, USA, and nest sites for cavity nesting birds in the Uinta Mountains, Utah, USA. We observed high classification accuracy in all applications as measured by cross-validation and, in the case of the lichen data, by independent test data, when comparing RF to other common classification methods. We also observed that the variables that RF identified as most important for classifying invasive plant species coincided with expectations based on the literature.     

Synthesis of vinasse-dolomite nanocomposite biochar via a novel developed functionalization method to recover phosphate as a potential fertilizer substitute

• Nanocomposites were prepared by adding dolomite to vinasse at different ratio. • Textural and morphological features of adsorbents were studied in detail. • CCD based RSM was used for investigation of P ion removal by nanocomposite. • The q_m based on Langmuir model for modified vinasse biochar was 178.57 mg/g. • P loaded nanocomposite improved plant growth and could be utilized as P-fertilizer. The effectiveness of phosphate (P) removal from aqueous solutions was investigated by novel low-cost biochars synthesized from vinasse and functionalized with calcined dolomite. The vinasse-derived biochar, synthesized via pyrolysis at different temperatures, showed easy preparation and a large surface area. The novel vinasse biochar nanocomposites were prepared by adding dolomite to the vinasse biochars with different weight percentages (10, 20 and 30%). The characteristics of the prepared materials were identified for further understanding of the inherent adsorption mechanism between P ions and vinasse biochars. Vinasse-dolomite nanocomposite was very effective in the adsorption of P species from aqueous media. The effect of the operational factors on Vinasse-dolomite nanocomposite was explored by applying response surface methodology (RSM). According to RSM results, the optimum condition was achieved to be contact time 90 (min), 250 (mg/L) of P concentration and pH 7. Thermodynamic isotherm and kinetic studies were applied on experimental data to understand the adsorption behavior. The Vinasse-dolomite nanocomposite revealed preferential P species adsorption in the presence of co-existing anions. The P species could be recovered by 1.0 M HCl where the efficiency was not affected up to the fifth cycle. The P-loaded Vinasse-dolomite nanocomposite was successfully tested on a plant; it significantly improved its growth and proved its potency as a P-based fertilizer substitute.     

Renewable resource management with a backstop substitute and nonautonomous prices

Policies for managing a renewable resource in the presence of a backstop substitute are analyzed. Conditions for renewable and backstop resources to be “relevant” are discussed. The extinction issue, the role of myopic decision rules and the optimality of singular arc policies are examined with nonautonomous prices.