Effects of sample survey design on the accuracy of classification tree models in species distribution models

We evaluated the effects of probabilistic (hereafter DESIGN) and non-probabilistic (PURPOSIVE) sample surveys on resultant classification tree models for predicting the presence of four lichen species in the Pacific Northwest, USA. Models derived from both survey forms were assessed using an independent data set (EVALUATION). Measures of accuracy as gauged by resubstitution rates were similar for each lichen species irrespective of the underlying sample survey form. Cross-validation estimates of prediction accuracies were lower than resubstitution accuracies for all species and both design types, and in all cases were closer to the true prediction accuracies based on the EVALUATION data set. We argue that greater emphasis should be placed on calculating and reporting cross-validation accuracy rates rather than simple resubstitution accuracy rates. Evaluation of the DESIGN and PURPOSIVE tree models on the EVALUATION data set shows significantly lower prediction accuracy for the PURPOSIVE tree models relative to the DESIGN models, indicating that non-probabilistic sample surveys may generate models with limited predictive capability. These differences were consistent across all four lichen species, with 11 of the 12 possible species and sample survey type comparisons having significantly lower accuracy rates. Some differences in accuracy were as large as 50%. The classification tree structures also differed considerably both among and within the modelled species, depending on the sample survey form. Overlap in the predictor variables selected by the DESIGN and PURPOSIVE tree models ranged from only 20% to 38%, indicating the classification trees fit the two evaluated survey forms on different sets of predictor variables. The magnitude of these differences in predictor variables throws doubt on ecological interpretation derived from prediction models based on non-probabilistic sample surveys.     

Invasive species distribution modeling (iSDM): Are absence data and dispersal constraints needed to predict actual distributions?

Species distribution models (SDMs) based on statistical relationships between occurrence data and underlying environmental conditions are increasingly used to predict spatial patterns of biological invasions and prioritize locations for early detection and control of invasion outbreaks. However, invasive species distribution models (iSDMs) face special challenges because (i) they typically violate SDM's assumption that the organism is in equilibrium with its environment, and (ii) species absence data are often unavailable or believed to be too difficult to interpret. This often leads researchers to generate pseudo-absences for model training or utilize presence-only methods, and to confuse the distinction between predictions of potential vs. actual distribution. We examined the hypothesis that true-absence data, when accompanied by dispersal constraints, improve prediction accuracy and ecological understanding of iSDMs that aim to predict the actual distribution of biological invasions. We evaluated the impact of presence-only, true-absence and pseudo-absence data on model accuracy using an extensive dataset on the distribution of the invasive forest pathogen Phytophthora ramorum in California. Two traditional presence/absence models (generalized linear model and classification trees) and two alternative presence-only models (ecological niche factor analysis and maximum entropy) were developed based on 890 field plots of pathogen occurrence and several climatic, topographic, host vegetation and dispersal variables. The effects of all three possible types of occurrence data on model performance were evaluated with receiver operating characteristic (ROC) and omission/commission error rates. Results show that prediction of actual distribution was less accurate when we ignored true-absences and dispersal constraints. Presence-only models and models without dispersal information tended to over-predict the actual range of invasions. Models based on pseudo-absence data exhibited similar accuracies as presence-only models but produced spatially less feasible predictions. We suggest that true-absence data are a critical ingredient not only for accurate calibration but also for ecologically meaningful assessment of iSDMs that focus on predictions of actual distributions.     

Factors Associated with Alien Plants Transitioning from Casual, to Naturalized, to Invasive

Abstract:  To explain current plant invasions, or predict future ones, more knowledge on which factors increase the probability of alien species becoming naturalized and subsequently invasive is needed. We created a database of the alien plants in seminatural habitats in Ireland that included data on taxonomy, invasive status, invasion history, distribution, and biological and ecological plant characteristics. We used information from this database to determine the importance of these factors in increasing the ability of species to become naturalized and invasive. More specifically, we used two multiple logistic regressions to identify factors that distinguish naturalized from casual alien plant species and invasive from noninvasive, naturalized alien species. Clonal growth, moisture-indicator value, nitrogen-indicator value, native range, and date of first record affected (in order of decreasing importance) the probability of naturalization. Factors that distinguished invasive from noninvasive species were ornamental introduction, hermaphrodite flowers, pollination mode, being invasive elsewhere, onset of flowering season, moisture-indicator value, native range, and date of first record. Incorporation of phylogenetic information had little influence on the results, suggesting that the capacity of alien species to naturalize and become invasive evolved largely independently in several phylogenetic lineages. Whereas some of the variables were important for both transitions, others were only important for naturalization or for invasion. This emphasizes the importance of studying different stages of the invasion process when looking for mechanisms of becoming a successful invasive plant, instead of simply comparing invasive with noninvasive alien species. Our results also suggest that a combination of species traits and other variables is likely to produce the most accurate prediction of invasions.     

Using habitat suitability index and particle dispersion models for early detection of marine invaders.

Eradication and control of invasive species are often possible only if populations are detected when they are small and localized. To be efficient, detection surveys should be targeted at locations where there is the greatest risk of incursions. We examine the utility of habitat suitability index (HSI) and particle dispersion models for targeting sampling for marine pests. Habitat suitability index models are a simple way to identify suitable habitat when species distribution data are lacking. We compared the performance of HSI models with statistical models derived from independent data from New Zealand on the distribution of two nonindigenous bivalves: Theora lubrica and Musculista senhousia. Logistic regression models developed using the HSI scores as predictors of the presence/absence of Theora and Musculista explained 26.7% and 6.2% of the deviance in the data, respectively. Odds ratios for the HSI scores were greater than unity, indicating that they were genuine predictors of the presence/ absence of each species. The fit and predictive accuracy of each logistic model were improved when simulated patterns of dispersion from the nearest port were added as a predictor variable. Nevertheless, the combined model explained, at best, 46.5% of the deviance in the distribution of Theora and correctly predicted 56% of true presences and 50% of all cases. Omission errors were between 6% and 16%. Although statistical distribution models built directly from environmental predictors always outperformed the equivalent HSI models, the gain in model fit and accuracy was modest. High residual deviance in both types of model suggests that the distributions realized by Theora and Musculista in the field data were influenced by factors not explicitly modeled as explanatory variables and by error in the environmental data used to project suitable habitat for the species. Our results highlight the difficulty of accurately predicting the distribution of invasive marine species that exhibit low habitat occupancy and patchy distributions in time and space. Although the HSI and statistical models had utility as predictors of the likely distribution of nonindigenous marine species, the level of spatial accuracy achieved with them may be well below expectations for sensitive surveillance programs.     

Comparative water use of native and invasive plants at multiple scales: a global meta-analysis

Ecohydrology and invasive ecology have become increasingly important in the context of global climate change. This study presents the first in-depth analysis of the water use of invasive and native plants of the same growth form at multiple scales: leaf, plant, and ecosystem. We reanalyzed data for several hundred native and invasive species from over 40 published studies worldwide to glean global trends and to highlight how patterns vary depending on both scale and climate. We analyzed all pairwise combinations of co-occurring native and invasive species for higher comparative resolution of the likelihood of an invasive species using more water than a native species and tested for significance using bootstrap methods. At each scale, we found several-fold differences in water use between specific paired invasive and native species. At the leaf scale, we found a strong tendency for invasive species to have greater stomatal conductance than native species. At the plant scale, however, natives and invasives were equally likely to have the higher sap flow rates. Available data were much fewer for the ecosystem scale; nevertheless, we found that invasive-dominated ecosystems were more likely to have higher sap flow rates per unit ground area than native-dominated ecosystems. Ecosystem-scale evapotranspiration, on the other hand, was equally likely to be greater for systems dominated by invasive and native species of the same growth form. The inherent disconnects in the determination of water use when changing scales from leaf to plant to ecosystem reveal hypotheses for future studies and a critical need for more ecosystem-scale water use measurements in invasive- vs. native-dominated systems. The differences in water use of native and invasive species also depended strongly on climate, with the greater water use of invasives enhanced in hotter, wetter climates at the coarser scales.     

Fourth-corner generation of plant functional response groups

Plant functional response groups (PFGs) are now widely established as a tool to investigate plant—environment relationships. Different statistical methods to form PFGs are used in the literature. One way is to derive emergent groups by classifying species based on correlation of biological attributes and subjecting these groups to tests of response to environmental variables. Another way is to search for associations of occurrence data, environmental variables and trait data simultaneously. The fourth-corner method is one way to assess the relationships between single traits and habitat factors. We extended this statistical method to a generally applicable procedure for the generation of plant functional response groups by developing new randomization procedures for presence/absence data of plant communities. Previous PFG groupings used either predefined groups or emergent groups i.e. classifications based on correlations of biological attributes (Lavorel et al Trends Ecol Evol 12:474–478, 1997), of the global species pool and assessed their functional response. However, since not all PFGs might form emergent groups or may be known by experts, we used a permutation procedure to optimise functional grouping. We tested the method using an artificial test data set of virtual plants occurring in different disturbance treatments. Direct trait-treatment relationships as well as more complex associations are incorporated in the test data. Trait combinations responding to environmental variables could be clearly distinguished from non-responding combinations. The results are compared with the method suggested by Pillar (J Veg Sci 10:631–640) for the identification of plant functional groups. After exploring the statistical properties using an artificial data set, the method is applied to experimental data of a greenhouse experiment on the assemblage of plant communities. Four plant functional response groups are formed with regard to differences in soil fertility on the basis of the traits canopy height and spacer length.     

Knowledge gain and behavioral change in citizen-science programs

Citizen-science programs are often touted as useful for advancing conservation literacy, scientific knowledge, and increasing scientific-reasoning skills among the public. Guidelines for collaboration among scientists and the public are lacking and the extent to which these citizen-science initiatives change behavior is relatively unstudied. Over two years, we studied 82 participants in a three-day program that included education about non-native invasive plants and collection of data on the occurrence of those plants. Volunteers were given background knowledge about invasive plant ecology and trained on a specific protocol for collecting invasive plant data. They then collected data and later gathered as a group to analyze data and discuss responsible environmental behavior with respect to invasive plants. We tested whether participants without experience in plant identification and with little knowledge of invasive plants increased their knowledge of invasive species ecology, participation increased knowledge of scientific methods, and participation affected behavior. Knowledge of invasive plants increased on average 24%, but participation was insufficient to increase understanding of how scientific research is conducted. Participants reported increased ability to recognize invasive plants and increased awareness of effects of invasive plants on the environment, but this translated into little change in behavior regarding invasive plants. Potential conflicts between scientific goals, educational goals, and the motivation of participants must be considered during program design.     

Aquatic plant community invasibility and scale-dependent patterns in native and invasive species richness

Invasive species richness often is negatively correlated with native species richness at the small spatial scale of sampling plots, but positively correlated in larger areas. The pattern at small scales has been interpreted as evidence that native plants can competitively exclude invasive species. Large-scale patterns have been understood to result from environmental heterogeneity, among other causes. We investigated species richness patterns among submerged and floating-leaved aquatic plants (87 native species and eight invasives) in 103 temperate lakes in Connecticut (northeastern USA) and found neither a consistently negative relationship at small (3-m2) scales, nor a positive relationship at large scales. Native species richness at sampling locations was uncorrelated with invasive species richness in 37 of the 60 lakes where invasive plants occurred; richness was negatively correlated in 16 lakes and positively correlated in seven. No correlation between native and invasive species richness was found at larger spatial scales (whole lakes and counties). Increases in richness with area were uncorrelated with abiotic heterogeneity. Logistic regression showed that the probability of occurrence of five invasive species increased in sampling locations (3 m2, n = 2980 samples) where native plants occurred, indicating that native plant species richness provided no resistance against invasion. However, the probability of three invasive species' occurrence declined as native plant density increased, indicating that density, if not species richness, provided some resistance with these species. Density had no effect on occurrence of three other invasive species. Based on these results, native species may resist invasion at small spatial scales only in communities where density is high (i.e., in communities where competition among individuals contributes to community structure). Most hydrophyte communities, however, appear to be maintained in a nonequilibrial condition by stress and/or disturbance. Therefore, most aquatic plant communities in temperate lakes are likely to be vulnerable to invasion.     

Reproduction of olive tree habitat suitability for global change impact assessment

The olive tree is so typical of the Mediterranean climate that its presence in a territory qualifies the climate of this as Mediterranean. Many clues indicated that in the past olive cultivation limits moved northward or southward in the Northern Hemisphere according to warmer or cooler climate, respectively. This makes the olive tree cultivation area a possible biological indicator of changes in climate and the identification of the climatological parameters that limit its cultivation plays an important role for climate change impact assessment. In this work, three different approaches were compared, with the aim to compare methodologies suited to predict olive tree distribution over the Mediterranean basin: two classifiers (Random Forest, RF and an Artificial Neural Network, ANN) and a spatial model to infer climatic limiters of plant distribution (CLPD). These methodologies were applied within a framework including a geographical information system (GIS), which spatially defined olive tree cultivated area, and climatological informative layers (average temperature and cumulated rainfall, 50 km × 50 km), which were used as predictor variables. The results indicated that RF achieved on the whole, the lowest classification error (113 misclassified cases on 1906 test cases) followed by ANN (128 cases) and CLPD (153 cases). A validation test, performed over areas out of the Mediterranean basin where olive tree is cultivated (i.e. California and Southern Australia), confirmed the goodness of the RF fitted model in predicting olive tree suitable areas. In general, climatic predictor variables of the coldest and warmest periods of the year were the most significant in determining the limits of suitable olive cultivation area for these methodologies. In particular, temperature of January and July and rainfall of October and July were the climatic predictor variables having highest significance for both RF and ANN. Temperature of January >2 °C, of July >20 °C and cumulated annual rainfall >240 mm were the bounds found in the spatial model. The fitted RF model, coupled with the results of both Regional and General Circulation Model, was finally proposed to assess climate change impact on olive tree cultivated area in the Mediterranean basin.     

Urbanized landscapes favored by fig-eating birds increase invasive but not native juvenile strangler fig abundance

Propagule pressure can determine the success or failure of invasive plant range expansion. Range expansion takes place at large spatial scales, often encompassing many types of land cover, yet the effect of landscape context on propagule pressure remains largely unknown. Many studies have reported a positive correlation between invasive plant abundance and human land use; increased propagule pressure in these landscapes may be responsible for this correlation. We tested the hypothesis that increased rates of seed dispersal by fig-eating birds, which are more common in urban habitats, result in an increase in invasive strangler fig abundance in landscapes dominated by human land use. We quantified abundance of an invasive species (Ficus microcarpa) and a native species (F. aurea) of strangler fig in plots spanning the entire range of human land use in South Florida, USA, from urban parking lots to native forest. We then compared models that predicted juvenile fig abundance based on distance to adult fig seed sources and fig-eating bird habitat quality with models that lacked one or both of these terms. The best model for juvenile invasive fig abundance included both distance to adult and fig-eating bird habitat terms, suggesting that landscape effects on invasive fig abundance are mediated by seed-dispersing birds. In contrast, the best model for juvenile native fig abundance included only presence/absence of adults, suggesting that distance from individual adult trees may have less effect on seed limitation for a native species compared to an invasive species undergoing range expansion. However, models for both species included significant effects of adult seed sources, implying that juvenile abundance is limited by seed arrival. This result was corroborated by a seed addition experiment that indicated that both native and invasive strangler figs were strongly seed limited. Understanding how landscape context affects the mechanisms of plant invasion may lead to better management techniques. Our results suggest that prioritizing removal of adult trees in sites with high fig-eating bird habitat may be the most effective method to control F. microcarpa abundance.     

Modelling plant species richness using functional groups

Conservation biologists increasingly rely on spatial predictive models of biodiversity to support decision-making. Therefore, highly accurate and ecologically meaningful models are required at relatively broad spatial scales. While statistical techniques have been optimized to improve model accuracy, less focus has been given to the question: How does the autecology of a single species affect model quality? We compare a direct modelling approach versus a cumulative modelling approach for predicting plant species richness, where the latter gives more weight to the ecology of functional species groups. In the direct modelling approach, species richness is predicted by a single model calibrated for all species. In the cumulative modelling approach, the species were partitioned into functional groups, with each group calibrated separately and species richness of each group was cumulated to predict total species richness. We hypothesized that model accuracy depends on the ecology of individual species and that the cumulative modelling approach would predict species richness more accurately. The predictors explained plant species richness by ca. 25%. However, depending on the functional group the deviance explained varied from 3 to 67%. While both modelling approaches performed equally well, the models of the different functional groups highly varied in their quality and their spatial richness pattern. This variability helps to improve our understanding on how plant functional groups respond to ecological gradients.     

The performance of state-of-the-art modelling techniques depends on geographical distribution of species

We explored the effects of prevalence, latitudinal range and clumping (spatial autocorrelation) of species distribution patterns on the predictive accuracy of eight state-of-the-art modelling techniques: Generalized Linear Models (GLMs), Generalized Boosting Method (GBM), Generalized Additive Models (GAMs), Classification Tree Analysis (CTA), Artificial Neural Network (ANN), Multivariate Adaptive Regression Splines (MARS), Mixture Discriminant Analysis (MDA) and Random Forest (RF). One hundred species of Lepidoptera, selected from the Distribution Atlas of European Butterflies, and three climate variables were used to determine the bioclimatic envelope for each butterfly species. The data set consisting of 2620 grid squares 30′ × 60′ in size all over Europe was randomly split into the calibration and the evaluation data sets. The performance of different models was assessed using the area under the curve (AUC) of a receiver operating characteristic (ROC) plot. Observed differences in modelling accuracy among species were then related to the geographical attributes of the species using GAM. The modelling performance was negatively related to the latitudinal range and prevalence, whereas the effect of spatial autocorrelation on prediction accuracy depended on the modelling technique. These three geographical attributes accounted for 19–61% of the variation in the modelling accuracy. Predictive accuracy of GAM, GLM and MDA was highly influenced by the three geographical attributes, whereas RF, ANN and GBM were moderately, and MARS and CTA only slightly affected. The contrasting effects of geographical distribution of species on predictive performance of different modelling techniques represent one source of uncertainty in species spatial distribution models. This should be taken into account in biogeographical modelling studies and assessments of climate change impacts.     

A field experiment characterizing variable detection rates during plant surveys

Surveys aimed at finding threatened and invasive species can be challenging due to individual rarity and low and variable individual detection rates. Detection rate in plant surveys typically varies due to differences among observers, among the individual plants being surveyed (targets), and across background environments. Interactions among these 3 components may occur but are rarely estimated due to limited replication and control during data collection. We conducted an experiment to investigate sources of variation in detection of 2 Pilosella species that are invasive and sparsely distributed in the Alpine National Park, Australia. These species are superficially similar in appearance to other yellow-flowered plants occurring in this landscape. We controlled the presence and color of flowers on target Pilosella plants and controlled their placement in plots, which were selected for their variation in cover of non-target yellow flowers and dominant vegetation type. Observers mimicked Pilosella surveys in the plots and reported 1 categorical and 4 quantitative indicators of their survey experience level. We applied survival analysis to detection data to model the influence of both controlled and uncontrolled variables on detection rate. Orange- and yellow-flowering Pilosella in grass- and heath-dominated vegetation were detected at a higher rate than nonflowering Pilosella. However, this detection gain diminished as the cover of other co-occurring yellow-flowering species increased. Recent experience with Pilosella surveys improved detection rate. Detection experiments are a direct and accessible means of understanding detection processes and interpreting survey data for threatened and invasive species. Our detection findings have been used for survey planning and can inform progress toward eradication. Interaction of target and background characteristics determined detection rate, which enhanced predictions in the Pilosella eradication program and demonstrated the difficulty of transferring detection findings into untested environments.     

Implications for biological invasion of non-native plants for sale in the world's largest online market

Internet trade is increasingly recognized as a dispersal pathway of non-native plant species that is difficult to monitor. We sought to identify non-native flora present in the Chinese online market, the largest e-commerce market globally, and to decipher the effect of existing trade regulations, among other variables, on e-trading patterns and to inform policy. We used a comprehensive list of 811 non-native plant species in China present in 1 of the 3 phases of the invasion continuum (i.e., introduced, naturalized, and invasive). The price, propagule types, and quantities of the species offered for sale were retrieved from 9 online stores, including 2 of the largest platforms. Over 30% of the non-native species were offered for sale in the online marketplaces; invasive non-native species dominated the list (45.53%). No significant price difference was observed across the non-native species of the 3 invasion categories. Among the 5 propagule types, a significantly higher number of non-native species were offered for sale as seeds. The regression models and path analyses consistently revealed a direct positive effect of the number of uses and species’ minimum residence time and an indirect effect of biogeography on the pattern of trade in non-native plant species when minimal phylogenetic signal was detected. A review of the existing phytosanitary regulations in China revealed their inadequacy in managing e-trading of non-native plant species. To address the problem, we propose integration of a standardized risk assessment framework that considers perceptions of stakeholders and is adaptable based on continuous surveillance of the trade network. If implemented successfully, the measures could provide a template for other countries to strengthen trading regulations for non-native plant species and take proactive management measures.     

Participatory Approach for Rapid Assessment of Plant Diversity through a Folk Classification System in a Tropical Rainforest: Case Study in Xishuangbanna, China

Abstract:  Rural indigenous people are often very knowledgeable about plant and animal species, including their identification and ecology. The use of indigenous knowledge has increasingly attracted attention in scientific circles. The Dai people, a dominant nationality in southwestern Yunnan, China, have developed their own traditional plant classification system. In a case study in Xishuangbanna, we compared the differences in number of plant species identified between scientific and Dai folk classification. The Dai people identified more than 80% of the plant species, and the correspondence between folk and scientific plant species was 87.7%. Our results indicate that folk plant classification could be used in rapid assessment of plant species in certain regions. The use of folk systems of plant classification for rapid biodiversity assessment will contribute to conservation of both indigenous knowledge and regional biodiversity.     

Quantifying secondary pest outbreaks in cotton and their monetary cost with causal-inference statistics

Secondary pest outbreaks occur when the use of a pesticide to reduce densities of an unwanted target pest species triggers subsequent outbreaks of other pest species. Although secondary pest outbreaks are thought to be familiar in agriculture, their rigorous documentation is made difficult by the challenges of performing randomized experiments at suitable scales. Here, we quantify the frequency and monetary cost of secondary pest outbreaks elicited by early-season applications of broad-spectrum insecticides to control the plant bug Lygus spp. (primarily L. hesperus) in cotton grown in the San Joaquin Valley, California, USA. We do so by analyzing pest-control management practices for 969 cotton fields spanning nine years and 11 private ranches. Our analysis uses statistical methods to draw formal causal inferences from nonexperimental data that have become popular in public health and economics, but that are not yet widely known in ecology or agriculture. We find that, in fields that received an early-season broad-spectrum insecticide treatment for Lygus, 20.2% +/- 4.4% (mean +/- SE) of late-season pesticide costs were attributable to secondary pest outbreaks elicited by the early-season insecticide application for Lygus. In 2010 U.S. dollars, this equates to an additional $6.00 +/- $1.30 (mean +/- SE) per acre in management costs. To the extent that secondary pest outbreaks may be driven by eliminating pests' natural enemies, these figures place a lower bound on the monetary value of ecosystem services provided by native communities of arthropod predators and parasitoids in this agricultural system.