Presence–Absence versus Abundance Data for Monitoring Threatened Species

Abstract:  Effective detection of population trend is crucial for managing threatened species. Little theory exists, however, to assist managers in choosing the most cost-effective monitoring techniques for diagnosing trend. We present a framework for determining the optimal monitoring strategy by simulating a manager collecting data on a declining species, the Chestnut-rumped Hylacola ( Hylacola pyrrhopygia parkeri ), to determine whether the species should be listed under the IUCN (World Conservation Union) Red List. We compared the efficiencies of two strategies for detecting trend, abundance, and presence–absence surveys, under financial constraints. One might expect the abundance surveys to be superior under all circumstances because more information is collected at each site. Nevertheless, the presence–absence data can be collected at more sites because the surveyor is not obliged to spend a fixed amount of time at each site. The optimal strategy for monitoring was very dependent on the budget available. Under some circumstances, presence–absence surveys outperformed abundance surveys for diagnosing the IUCN Red List categories cost-effectively. Abundance surveys were best if the species was expected to be recorded more than 16 times/year; otherwise, presence–absence surveys were best. The relationship between the strategies we investigated is likely to be relevant for many comparisons of presence–absence or abundance data. Managers of any cryptic or low-density species who hope to maximize their success of estimating trend should find an application for our results.     

A Global Indicator for Biological Invasion

Abstract:  "Trends in invasive alien species" is one of only two indicators of threat to biodiversity that form part of the Convention on Biological Diversity's (CBD) framework for monitoring progress toward its "2010 target" (i.e., the commitment to achieve by 2010 a significant reduction in the current rate of biodiversity loss). To date, however, there is no fully developed indicator for invasive alien species (IAS) that combines trends, derived from a standard set of methods, across species groups, ecosystems, and regions. Here we provide a rationale for the form and characteristics of an indicator of trends in IAS that will meet the 2010 framework goal and targets for this indicator. We suggest single and composite indicators that include problem-status and management-status measures that are designed to be flexible, readily disaggregated, and as far as possible draw on existing data. The single indicators at national and global scales are number of IAS and numbers of operational management plans for IAS. Global trends in IAS are measured as the progress of nations toward the targets of stabilizing IAS numbers and the implementation of IAS management plans. The proposed global indicator thus represents a minimum information set that most directly addresses the indicator objective and simultaneously aims to maximize national participation. This global indicator now requires testing to assess its accuracy, sensitivity, and tractability. Although it may not be possible to achieve the desired objective for a global indicator of biological invasion by 2010 as comprehensively as desired, it seems possible to obtain trend estimates for a component of the taxa, ecosystems, and regions involved. Importantly, current indicator development initiatives will also contribute to developing the mechanisms necessary for monitoring global trends in IAS beyond 2010.     

A Successful Predictive Model of Species Richness Based on Indicator Species

Abstract:  Because complete species inventories are expensive and time-consuming, scientists and land managers seek techniques to alleviate logistic constraints on measuring species richness, especially over large spatial scales. We developed a method to identify indicators of species richness that is applicable to any taxonomic group or ecosystem. In an initial case study, we found that a model based on the occurrence of five indicator species explained 88% of the deviance of species richness of 56 butterflies in a mountain range in western North America. We validated model predictions and spatial transferability of the model using independent, newly collected data from another, nearby mountain range. Predicted and observed values of butterfly species richness were highly correlated with 93% of the observed values falling within the 95% credible intervals of the predictions. We used a Bayesian approach to update the initial model with both the model-building and model-validation data sets. In the updated model, the effectiveness of three of the five indicator species was similar, whereas the effectiveness of two species was reduced. The latter species had more erratic distributions in the validation data set than in the original model-building data set. This objective method for identifying indicators of species richness could substantially enhance our ability to conduct large-scale ecological assessments of any group of animals or plants in any geographic region and to make effective conservation decisions.     

A Realistic Assessment of the Indicator Potential of Butterflies and Other Charismatic Taxonomic Groups

Abstract:  Charismatic groups of animals and plants often are proposed as sentinels of environmental status and trends. Nevertheless, many claims that a certain taxonomic group can provide more-general information on environmental quality are not evaluated critically. To address several of the many definitions of indicator species, we used butterflies to explore in some detail the attributes that affect implementation of indicators generically. There probably are few individual species, or sets of species, that can serve as scientifically valid, cost-effective measures of the status or trend of an environmental phenomenon that is difficult to measure directly. Nevertheless, there are species with distributions, abundances, or demographic characteristics that are responsive to known environmental changes. In this context, single or multiple species can serve as indicators when targets are defined explicitly, ecological relationships between the target and the putative indicators are well understood, and data are sufficient to differentiate between deterministic and stochastic responses. Although these situations exist, they are less common than might be apparent from an extensive and often confounded literature on indicators. Instead, the public appeal of charismatic groups may be driving much of their acclaim as indicators. The same taxon may not be appropriate for marketing a general conservation mission and for drawing strong inference about specific environmental changes. To provide insights into the progress of conservation efforts, it is essential to identify scientific and practical criteria for selection and application of indicators and then to examine whether a given taxonomic group meets those criteria .     

Ecological Uses of Vertebrate Indicator Species: A Critique

Abstract: Plant and animal species have been used for decades as indicators of air and water quality and agricultural and range conditions. Increasingly, vertebrates are used to assess population trends and habitat quality for other species. In this paper we review the conceptual bases, assumptions, and published guidelines for selection and use of vertebrates as ecological indicators. We conclude that an absence of precise definitions and procedures, confounded criteria used to select species, and discordance with ecological literature severely weaken the effectiveness and credibility of using vertebrates as ecological indicators. In many cases the use of ecological indicator species is inappropriate, but when necessary, the following recommendations will make their use more rigorous: (1) clearly state assessment goals, (2) use indicators only when other assessment options are unavailable, (3) choose indicator species by explicitly defined criteria that are in accord with assessment goals, (4) include all species that fulfill stated selection criteria (5) know the biology of the indicator in detail, and treat the indicator as a formal estimator in conceptual and statistical models, (6) identify and define sources of subjectivity when selecting monitoring and intetpreting indicator species, (7) submit assessment design, methods of data collection and statistical analysis, interpretations, and recommendations to peer review and (8) direct research at developing an overall strategy for monitoring wildlife that accounts for natural variability in population attributes and incorporates concepts from landscape ecology.     

Effectiveness of Scat‐Detection Dogs in Determining Species Presence in a Tropical Savanna Landscape

Abstract: Most protected areas are too small to sustain populations of wide‐ranging mammals; thus, identification and conservation of high‐quality habitat for those animals outside parks is often a high priority, particularly for regions where extensive land conversion is occurring. This is the case in the vicinity of Emas National Park, a small protected area in the Brazilian Cerrado. Over the last 40 years the native vegetation surrounding the park has been converted to agriculture, but the region still supports virtually all of the animals native to the area. We determined the effectiveness of scat‐detection dogs in detecting presence of five species of mammals threatened with extinction by habitat loss: maned wolf (Chrysocyon brachyurus), puma (Puma concolor), jaguar (Panthera onca), giant anteater (Myrmecophaga tridactyla), and giant armadillo (Priodontes maximus). The probability of scat detection varied among the five species and among survey quadrats of different size, but was consistent across team, season, and year. The probability of occurrence, determined from the presence of scat, in a randomly selected site within the study area ranged from 0.14 for jaguars, which occur primarily in the forested areas of the park, to 0.91 for maned wolves, the most widely distributed species in our study area. Most occurrences of giant armadillos in the park were in open grasslands, but in the agricultural matrix they tended to occur in riparian woodlands. At least one target species occurred in every survey quadrat, and giant armadillos, jaguars, and maned wolves were more likely to be present in quadrats located inside than outside the park. The effort required for detection of scats was highest for the two felids. We were able to detect the presence for each of five wide‐ranging species inside and outside the park and to assign occurrence probabilities to specific survey sites. Thus, scat dogs provide an effective survey tool for rare species even when accurate detection likelihoods are required. We believe the way we used scat‐detection dogs to determine the presence of species can be applied to the detection of other mammalian species in other ecosystems.     

Using Niche-Based Models to Improve the Sampling of Rare Species

Abstract:  Because data on rare species usually are sparse, it is important to have efficient ways to sample additional data. Traditional sampling approaches are of limited value for rare species because a very large proportion of randomly chosen sampling sites are unlikely to shelter the species. For these species, spatial predictions from niche-based distribution models can be used to stratify the sampling and increase sampling efficiency. New data sampled are then used to improve the initial model. Applying this approach repeatedly is an adaptive process that may allow increasing the number of new occurrences found. We illustrate the approach with a case study of a rare and endangered plant species in Switzerland and a simulation experiment. Our field survey confirmed that the method helps in the discovery of new populations of the target species in remote areas where the predicted habitat suitability is high. In our simulations the model-based approach provided a significant improvement (by a factor of 1.8 to 4 times, depending on the measure) over simple random sampling. In terms of cost this approach may save up to 70% of the time spent in the field.     

Using abiotic variables to predict importance of sites for species representation

In systematic conservation planning, species distribution data for all sites in a planning area are used to prioritize each site in terms of the site's importance toward meeting the goal of species representation. But comprehensive species data are not available in most planning areas and would be expensive to acquire. As a shortcut, ecologists use surrogates, such as occurrences of birds or another well‐surveyed taxon, or land types defined from remotely sensed data, in the hope that sites that represent the surrogates also represent biodiversity. Unfortunately, surrogates have not performed reliably. We propose a new type of surrogate, predicted importance, that can be developed from species data for a q% subset of sites. With species data from this subset of sites, importance can be modeled as a function of abiotic variables available at no charge for all terrestrial areas on Earth. Predicted importance can then be used as a surrogate to prioritize all sites. We tested this surrogate with 8 sets of species data. For each data set, we used a q% subset of sites to model importance as a function of abiotic variables, used the resulting function to predict importance for all sites, and evaluated the number of species in the sites with highest predicted importance. Sites with the highest predicted importance represented species efficiently for all data sets when q = 25% and for 7 of 8 data sets when q = 20%. Predicted importance requires less survey effort than direct selection for species representation and meets representation goals well compared with other surrogates currently in use. This less expensive surrogate may be useful in those areas of the world that need it most, namely tropical regions with the highest biodiversity, greatest biodiversity loss, most severe lack of inventory data, and poorly developed protected area networks.     

Indicator Taxa, Rapid Biodiversity Assessment, and Nestedness in an Endangered Ecosystem

Abstract: To prioritize areas for conservation, biologists and managers need information on species diversity in threatened habitats. The resources available for such inventories remain severely limited, increasing the need to develop speedier ways to estimate the status of target habitats. We present a study of the use of such techniques in the highly fragmented oak savannas of southern Ontario, including selection of indicator taxa, use of rapid biodiversity assessment based on morphospecies, and analysis of community structure. We found that butterflies and skippers can be used to predict richness among Hymenoptera in the study sites, which is consistent with the hypothesis that these easily surveyed Lepidoptera are good candidates for indicator status. Richness values for hymenoptera morphospecies in these savanna remnants were strongly correlated with species richness scores as estimated by systematists, although nonspecialists tended to "split" species into more than one morphospecies. Finally, both the Hymenoptera and Lepidoptera communities in these oak savannas exhibited a high degree of nestedness, suggesting that local extinctions, mostly undocumented, are important determinants of the richness patterns across these widely separated savanna study sites. We found no evidence of significant spatial autocorrelation, probably because of the wide separation of study sites.     

Obtaining Environmental Favourability Functions from Logistic Regression

Logistic regression is a statistical tool widely used for predicting species’ potential distributions starting from presence/absence data and a set of independent variables. However, logistic regression equations compute probability values based not only on the values of the predictor variables but also on the relative proportion of presences and absences in the dataset, which does not adequately describe the environmental favourability for or against species presence. A few strategies have been used to circumvent this, but they usually imply an alteration of the original data or the discarding of potentially valuable information. We propose a way to obtain from logistic regression an environmental favourability function whose results are not affected by an uneven proportion of presences and absences. We tested the method on the distribution of virtual species in an imaginary territory. The favourability models yielded similar values regardless of the variation in the presence/absence ratio. We also illustrate with the example of the Pyrenean desman’s (Galemys pyrenaicus) distribution in Spain. The favourability model yielded more realistic potential distribution maps than the logistic regression model. Favourability values can be regarded as the degree of membership of the fuzzy set of sites whose environmental conditions are favourable to the species, which enables applying the rules of fuzzy logic to distribution modelling. They also allow for direct comparisons between models for species with different presence/absence ratios in the study area. This makes them more useful to estimate the conservation value of areas, to design ecological corridors, or to select appropriate areas for species reintroductions. Received: June 2005 / Revised: July 2005     

Evaluation of Biogeographic Classification Schemes for Conservation Planning: Application to New Zealand's Coastal Marine Environment

Abstract:  For many regions worldwide, multiple and often contrasting biogeographic classifications exist that are derived from a variety of taxa and techniques. This presents a challenge for managers who must choose appropriate large-scale spatial frameworks for systematic conservation planning. We demonstrate how systematically collected community data can be used to evaluate existing biogeographic classifications, identify the most appropriate metric for biogeographic patterns seen in other taxonomic groups, and develop an independent biogeographic classification scheme for systematic conservation planning. We evaluated 6 existing biogeographic classifications for New Zealand's nearshore marine environment with community-similarity metrics derived from abundance and presence–absence data for macroalgae (107 species) and mobile macroinvertebrates (44 species). The concordance between community metrics and the previous classifications was high, as indicated by a high multivariate classification success (CS) (74.3–98.3%). Subsequently, we carried out an independent classification analysis on each community metric to identify biogeographic units within a hierarchical spatial framework. The classification derived from macroalgal presence–absence data achieved the highest CS and could be used as a mesoscale classification scheme in which 11 regional groupings (i.e., bioregions) (CS = 73.8–84.8%) are nested within northern and southern biogeographic provinces (CS = 90.3–98.7%). These techniques can be used in systematic conservation planning to inform the design of representative and comprehensive networks of marine protected areas through evaluation of the current coverage of marine reserves in each bioregion. Currently, 0.22% of the territorial sea around mainland New Zealand is protected in no-take marine protected areas in which 0–1.5% of each bioregion represented.     

Biogeography and decline of rare plants in New England: historical evidence and contemporary monitoring.

Detecting range shifts and contractions is critical for determining the conservation priority of rare and declining taxa. However, data on rare species occurrences frequently lack precise information on locations and habitats and may present a biased picture of biogeographic distributions and presumed habitat preferences. Herbarium or museum specimen data, which otherwise could be useful proxies for detecting temporal trends and spatial patterns in species distributions, pose particular challenges. Using data from herbaria and Natural Heritage Programs on numbers of occurrences within individual municipalities (towns, cities, or townships), we quantified temporal changes in the estimated distributions of 110 rare plant species in the six New England (USA) states. We used the partial Solow equation and a nonparametric test to estimate the probability of observing multiple absences (gaps in the collection record) if a given population was actually still extant. Bayes' Theorem was used to estimate the probability that occurrences were misclassified as extinct. Using the probabilities obtained from these three methods, we eliminated taxa with high probabilities of pseudo-absence (that would yield an inaccurate profile of species distributions), narrowing the set for final analysis to 71 taxa. We then expressed occurrences as centroids of town polygons and estimated current and historical range areas (extents of occurrence as defined by alpha-hulls inscribing occurrences), mean distances between occurrences, and latitudinal and longitudinal range boundaries. Using a geographic information system, we modeled first, second, and third circular standard deviational polygons around the mean center of the historical range. Examining the distribution of current occurrences within each standard deviational polygon, we asked whether ranges were collapsing to a center, expanding, fragmenting, or contracting to a margin of the former range. Extant ranges of the species were, on average, almost 67% smaller than their historical ranges, and distances among occurrences decreased. Five New England hotspots were observed to contain >35% of rare plant populations. Extant occurrences were more frequently marginalized at the periphery of the historical range than would be expected by chance. Coarse-grained data on current and historical occurrences can be used to examine large suites of species to prioritize taxa and sites for conservation.     

Conservation Status as a Biodiversity Trend Indicator: Recommendations from a Decade of Listing Species at Risk in British Columbia

Abstract:  Species conservation status is commonly used as a broad-scale indicator of the state of biological diversity. To learn about its value for tracking trends, we examined provincial lists of terrestrial vertebrate species and subspecies at risk in British Columbia, Canada, for 1992 and 2002 to see whether changes in these lists reflected changes in the status of the taxa they represent. Examination of the case histories of individual species and subspecies showed that 65% of additions and deletions to the British Columbia Red List were the result of improvement in knowledge of species status, changes in assessment procedures, and refinements in taxonomy rather than actual changes in a species' status. Comparison to an alternate set of rank scores provided by NatureServe for taxa that appeared on both 1992 and 2002 British Columbia Red Lists revealed changes in status that were not reflected by movement from the list. Estimates of historical conservation status for species on the 1992 British Columbia Red List demonstrated ambiguity around the natural baseline with regard to tracking changes in list composition over time. We discourage the continued use of indicators based solely on conservation status as a means of tracking biodiversity. Instead we recommend advancing strategic indicators around species at risk based on long-term monitoring data, deliberate and explicitly stated baselines, and consistent methods of conservation ranking.     

Estimating Climate Resilience for Conservation across Geophysical Settings

Conservationists need methods to conserve biological diversity while allowing species and communities to rearrange in response to a changing climate. We developed and tested such a method for northeastern North America that we based on physical features associated with ecological diversity and site resilience to climate change. We comprehensively mapped 30 distinct geophysical settings based on geology and elevation. Within each geophysical setting, we identified sites that were both connected by natural cover and that had relatively more microclimates indicated by diverse topography and elevation gradients. We did this by scoring every 405 ha hexagon in the region for these two characteristics and selecting those that scored >SD 0.5 above the mean combined score for each setting. We hypothesized that these high‐scoring sites had the greatest resilience to climate change, and we compared them with sites selected by The Nature Conservancy for their high‐quality rare species populations and natural community occurrences. High‐scoring sites captured significantly more of the biodiversity sites than expected by chance (p < 0.0001): 75% of the 414 target species, 49% of the 4592 target species locations, and 53% of the 2170 target community locations. Calcareous bedrock, coarse sand, and fine silt settings scored markedly lower for estimated resilience and had low levels of permanent land protection (average 7%). Because our method identifies—for every geophysical setting—sites that are the most likely to retain species and functions longer under a changing climate, it reveals natural strongholds for future conservation that would also capture substantial existing biodiversity and correct the bias in current secured lands.     

Conservation Strategies for Endemic Fish Species Threatened by the Three Gorges Dam

Abstract:   The largest damming project to date, the Three Gorges Dam has been built along the Yangtze River (China), the most species-rich river in the Palearctic region. Among 162 species of fish inhabiting the main channel of the upper Yangtze, 44 are endemic and are therefore under serious threat of global extinction from the dam. Accordingly, it is urgently necessary to develop strategies to minimize the impacts of the drastic environmental changes associated with the dam. We sought to identify potential reserves for the endemic species among the 17 tributaries in the upper Yangtze, based on presence/absence data for the 44 endemic species. Potential reserves for the endemic species were identified by characterizing the distribution patterns of endemic species with an adaptive learning algorithm called a "self-organizing map" (SOM). Using this method, we also predicted occurrence probabilities of species in potential reserves based on the distribution patterns of communities. Considering both SOM model results and actual knowledge of the biology of the considered species, our results suggested that 24 species may survive in the tributaries, 14 have an uncertain future, and 6 have a high probability of becoming extinct after dam filling.     

Selection of Islands for Conservation in the Urban Archipelago of Helsinki, Finland

Abstract: The occurrence of vascular plants was surveyed on 207 islands (size range 0.01–390.2 ha, number of plant species 1–449) offshore from the city of Helsinki in the Baltic Sea to examine the conservation value of these islands. We calculated a rarity score for each species (1/number of islands occupied by the species) and a biodiversity score for each island (sum of the rarity scores of each species present on the island). Positive correlations between species number and biodiversity score (r _s = 0.97, p < 0.001) and between biodiversity score and island area (r _s = 0.87, p < 0.001) indicated that these parameters are heavily dependent on island size. With the goal of including at least one occurrence (island) of all plant species, an iterative selection algorithm chose a set of 41 islands whose average size (29.3 ha) was four times the average size of all existing islands (7.0 ha). Strong nestedness ( N < 54) explains the concentration of plant species diversity on large islands. An operational strategy for selection of sites for protection is to complement the set produced by a selection algorithm with target species not yet included (e.g., endangered species with several occurrences). Comprehensive mapping and analysis of a taxonomic group will help integrate conservation biology into land-use planning and increase the quality of the networks of protected areas.     

Conspecific Attraction and the Conservation of Territorial Songbirds

Abstract:  Conspecific attraction, the tendency for individuals of a species to settle near one another, is well described in colonial species, especially birds. Although this behavior may occur in territorial birds, evidence has been lacking. If territorial birds do exhibit this behavior, it would have major conservation implications. Birds could potentially be attracted to specific sites with artificial stimuli, making conservation of those species more efficient. In 2001 and 2002, we tested whether conspecific attraction occurs in an endangered, territorial songbird, the Black-capped Vireo ( Vireo atricapilla ) by playing vireo vocalizations in unoccupied habitats at Fort Hood, Texas. We were successful in attracting 73 birds to five experimental sites in 2001 and 75 birds to seven experimental sites in 2002. No birds settled on comparable control sites. Many birds attracted to the vocalizations paired and bred. At most research sites the primary threat to the species, the brood-parasitic Brown-headed Cowbird ( Molothrus ater ), was controlled, allowing vireos to achieve high nesting success relative to a nearby, unmanipulated population. Second-year birds were more responsive to conspecific vocalizations than older birds, as they were more common on experimental sites than in the established population. In 2002 birds recolonized experimental sites from 2001 where vocalizations were not played in 2002, indicating that 1 year of playbacks may be sufficient to establish a population. Our results provide the first experimental evidence that territorial songbirds use the presence of conspecifics when deciding where to settle and suggest that conspecific attraction may provide a valuable conservation tool.     

Estimating species occurrence, abundance, and detection probability using zero-inflated distributions

Researchers have developed methods to account for imperfect detection of species with either occupancy (presence absence) or count data using replicated sampling. We show how these approaches can be combined to simultaneously estimate occurrence, abundance, and detection probability by specifying a zero-inflated distribution for abundance. This approach may be particularly appropriate when patterns of occurrence and abundance arise from distinct processes operating at differing spatial or temporal scales. We apply the model to two data sets: (1) previously published data for a species of duck, Anas platyrhynchos, and (2) data for a stream fish species, Etheostoma scotti. We show that in these cases, an incomplete-detection zero-inflated modeling approach yields a superior fit to the data than other models. We propose that zero-inflated abundance models accounting for incomplete detection be considered when replicate count data are available.     

The Value of Biodiversity in Reserve Selection: Representation, Species Weighting, and Benefit Functions

Abstract:  The limited availability of resources for conservation has led to the development of many quantitative methods for selecting reserves that aim to maximize the biodiversity value of reserve networks. In published analyses, species are often considered equal, although some are in much greater need of protection than others. Furthermore, representation is usually treated as a threshold: a species is either represented or not, but varying levels of representation over or under a given target level are not valued differently. We propose that a higher representation level should also have higher value. We introduce a framework for reserve selection that includes species weights and benefit functions for under- and overrepresentation (number of locations for each species). We applied the method to conservation planning for herb-rich forests in southern Finland. Our use of benefit functions and weighting changed the identity of about 50% of the selected sites at different funding levels and improved the representation of rare and threatened species. We also identified a small area of additional land that would substantially enhance the existing reserve network. We suggest that benefit functions and species weighting should be considered as standard options in reserve-selection applications.     

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.