Using species distribution and occupancy modeling to guide survey efforts and assess species status

Habitat loss and fragmentation continue to be major issues affecting the persistence and conservation of species, but identification of critical habitat remains a challenge. Species distribution modeling and occupancy modeling are both approaches that have been used to predict species distributions and can identify critical habitat characteristics associated with species occurrence. Additionally, occupancy sampling can provide measures of detectability, increasing the confidence that a species is truly absent when not detected. While increasingly popular, these methods are infrequently used in synergy, and rarely at fine spatial scales. We provide a case study of using distribution and occupancy modeling in unison to direct survey efforts, provide estimates of species presence/absence, and to identify local and landscape features important for species occurrence. The focal species for our study was Ambystoma jeffersonianum, a threatened salamander in the state of Illinois, U.S.A. We found that fine-scale distribution models accurately discriminated occupied from unoccupied breeding ponds (78–91% accuracy), and surveys could be effectively guided using a well-fit model. We achieved a high detection rate (0.774) through occupancy sampling, and determined that A. jeffersonianum never used ponds inhabited by fish, and the probability of a pond being used for breeding increased as canopy cover increased. When faced with limited resources, combining fine-scale distribution modeling with a robust occupancy sampling design can expedite survey efforts, confidently designate species occupancy status, prioritise habitat for future surveys and/or restoration, and identify critical habitat features. This approach is broadly applicable to other taxa that have specific habitat requirements.     

Spatial conservation planning under climate change: Using species distribution modeling to assess priority for adaptive management of Fagus crenata in Japan

Protected areas are the basis of modern conservation systems, but current climate change causes gaps between protected areas and the species distribution ranges. To mitigate the impact of climate change on species distribution ranges, revision of protected areas are necessary. Alternatively, active management such as excluding competitive species or transplanting target species would be effective. In this study, we assessed optimal actions (revision of protected areas or active management) in each geographical region to establish an effective spatial conservation plan in Japan. Gaps between the protected areas and future potential habitats were assessed using species distribution models and 20 future climate simulations. Fagus crenata, an endemic and dominant species in Japan, was used as a target species. Potential habitats within the protected areas were predicted to decrease from 22,122 km² at present to 12,309 km² under future climate conditions. Sustainable potential habitats (consistent potential habitats both at present and in future) without the protected areas extended to 13,208 km², and were mainly found in northeast Japan. These results suggest that, in northeast Japan, revisions to protected areas would be effective in preserving sustainable potential habitats under future climate change. However, the potential habitats of southwestern Japan, in which populations were genetically different from northeastern populations, were predicted to virtually disappear both within and outside of protected areas. Active management is thus necessary in southwestern Japan to ensure intraspecific genetic diversity under future climate change.     

Phylogeny of Chaetothyriaceae in northern Thailand including three new species

In a recent study unusual taxa of epiphyllous ascomycota belonging to Chaetothyriaceae (Eurotiomycetes) were collected in northern Thailand. This family is poorly understood due to morphological confusion and lack of phylogenetic studies. This paper deals with three new species, Ceramothyrium thailandicum, Chaetothyrium brischofiacola and Phaeosaccardinula ficus, which are fully described and illustrated. A DNA sequence analyses of LSU and ITS rDNA genes shows that the new species cluster in the Chaetothyriaceae. This paper adds six sequences for Chaetothyriaceae to GenBank, providing much needed data for the family.     

Invaders at the doorstep: Using species distribution modeling to enhance invasive plant watch lists

Watch lists of invasive species that threaten a particular land management unit are useful tools because they can draw attention to invasive species at the very early stages of invasion when early detection and rapid response efforts are often most successful. However, watch lists typically rely on the subjective selection of invasive species by experts or on the use of spotty occurrence records. Further, incomplete records of invasive plant occurrences bias these watch lists towards the inclusion of invasive plant species that may already be present in a land management unit, because the occurrences have not been formally integrated into publicly accessible biodiversity databases. However, these problems may be overcome by an iterative approach that guides more complete detection and compilation of invasive plant species records within land management units. To address issues from unobserved or unrecorded occurrences, we combined predicted suitable habitat from species distribution models and aggregated invasive plant occurrence records to develop ranked watch lists of 146 priority invasive plant species on >4000 land management units from five different administrative types within the United States. Based on this analysis, we determined that on average 84% of priority invasive plants with suitable habitat within a given land management unit were as yet unobserved, and that 41% of those were ‘doorstep species’ – found within 50 miles of the unit boundary yet not detected within the unit. Two case studies, developed in collaboration with staff at U.S. Fish and Wildlife Service Refuges, showed that by combining both habitat suitability models and invasive plant occurrence records, we could identify additional problematic invasive plants that had been previously overlooked. Model-based watch lists of ‘doorstep species’ are useful tools because they can objectively alert land managers to threats from invasive plants with high likelihood of establishment.     

Relative frequency function models for species distribution modeling

A new approach to habitat distribution modeling is presented and tested with data on North American plants. The relative frequency function (RFF) algorithm compares the relative frequencies of a species’ sample points to that of random points or absence points on the landscape to compute a frequency ratio. The relative frequency ratio r is smoothed across the range of values using moving, overlapping windows. The ratio of frequencies at a sample point for each variable is used to compute the geometric mean score for all data with non‐missing values. Variables are added using a forward stepwise method. Confidence intervals are computed with bootstrap resampling. The method was tested with artificial and species habitat and geographic range data. The RFF method in all cases gave results comparable to other methods tested. For the species with good geographic range maps, the results were consistent with known biogeography. The RFF method is particularly well‐suited to irregularly shaped distributions and can classify sample points even when the data contain missing values. The method is extremely simple to use and comes with a free software tool, does not require a large sample size, does not require absence data, and is more interpretable and portable than certain other methods.     

Three new species of Lentinus from northern Thailand

There have been few studies on the taxonomy and biodiversity of the genus Lentinus in Thailand, which is a genus of edible mushrooms. Recently, collections from 17 sites in northern Thailand yielded 47 specimens of Lentinus sensu lato. Three were shown to be new species of Lentinus sensu stricto and Lentinus roseus, L. concentricus and L. megacystidiatus are introduced in this paper. The new species are described and illustrated with line drawings and are justified and compared with similar taxa. Furthermore, ITS sequence data do not match closely with any species presently lodged in GenBank.     

Importance of estimating matrix quality for modeling species distribution in complex tropical landscapes: a test with Atlantic forest small mammals

Information to guide decision making is especially urgent in human dominated landscapes in the tropics, where urban and agricultural frontiers are still expanding in an unplanned manner. Nevertheless, most studies that have investigated the influence of landscape structure on species distribution have not considered the heterogeneity of altered habitats of the matrix, which is usually high in human dominated landscapes. Using the distribution of small mammals in forest remnants and in the four main altered habitats in an Atlantic forest landscape, we investigated 1) how explanatory power of models describing species distribution in forest remnants varies between landscape structure variables that do or do not incorporate matrix quality and 2) the importance of spatial scale for analyzing the influence of landscape structure. We used standardized sampling in remnants and altered habitats to generate two indices of habitat quality, corresponding to the abundance and to the occurrence of small mammals. For each remnant, we calculated habitat quantity and connectivity in different spatial scales, considering or not the quality of surrounding habitats. The incorporation of matrix quality increased model explanatory power across all spatial scales for half the species that occurred in the matrix, but only when taking into account the distance between habitat patches (connectivity). These connectivity models were also less affected by spatial scale than habitat quantity models. The few consistent responses to the variation in spatial scales indicate that despite their small size, small mammals perceive landscape features at large spatial scales. Matrix quality index corresponding to species occurrence presented a better or similar performance compared to that of species abundance. Results indicate the importance of the matrix for the dynamics of fragmented landscapes and suggest that relatively simple indices can improve our understanding of species distribution, and could be applied in modeling, monitoring and managing complex tropical landscapes.     

Distribution and diversity of palms in a tropical biodiversity hotspot (Thailand) assessed by species distribution modeling

Species distribution modeling has been widely used to address questions related to ecology, biogeography and species conservation on global and regional scales. Here, we study palms (Arecaceae) in a tropical biodiversity hotspot (Thailand) using species distribution modeling to assess range‐limiting factors and estimate distribution and diversity patterns based on a comprehensive compilation of occurrence records. We focused on palms as a model group due to their key‐stone importance for ecosystem functioning and socio‐economics. Different combinations of climatic, non‐climatic environmental and spatial predictors were used. The most accurate models as indicated by the ‘area under the receiver operating characteristic curve’ (AUC) statistic were those that combined all predictors. The four strongest single predictors of palm species distributions were, in decreasing order of importance, 1) latitude, 2) precipitation of driest quarter, 3) annual precipitation, and 4) minimum temperature of the coldest month, suggesting rainfall patterns and latitudinal spatial constraints as the main range determinants. Overlaying the predicted distributions revealed that potential palm hotspots are situated in the provinces of Satun and Yala in southern Thailand where vast areas remain relatively open to the discovery of new palm records and perhaps even new species.     

Mitrephora sirikitiae (Annonaceae): a remarkable new species endemic to northern Thailand

A new species of Annonaceae, Mitrephora sirikitiae , is described from Mae Hong Son Province in northern Thailand. It is easily distinguished from the seven species of Mitrephora previously recorded from Thailand due to its very large, showy flowers. It is most similar to M. winitii , but differs in its larger flowers, with inner petals that become undulate at maturity. The profuse blooming of the new species and its large flowers with mild fragrance suggest that may be of significant horticultural potential.     

Using niche modelling and human influence index to indicate conservation priorities for Atlantic forest deer species

To prioritise conservation actions and management strategies for threatened forest deer species at the Atlantic forest, we aimed to identify and describe the most suitable habitat areas for forest deer species and to indicate conservation measures for state agents and local communities. We adopt an approach based on ecological niche modelling, key variable thresholds and spatial analyses. In addition, we associated our approach with a human influence index, an invasive species dataset of occurrences, protected area cover and IUCN category. We indicate 2 % (484 km²) of the Atlantic forest cover as conservation priority areas (CPAs). Of these, 56.8 % are outside protected areas, 20.7 % are inside IUCN categories i, ii and iii protected areas, 19.9 % are inside IUCN categories iv, v, and vi protected areas, and 2.6 % are inside indigenous areas. Also, we indicate the most relevant protected areas for deer conservation in the Atlantic forest. The CPAs were classified into more human-influenced areas (MHIA) and less human-influenced areas (LHIA), and we identified 21 significant (greater than120 km²) continuous CPAs outside protected areas. We highlight actions in several perspectives of human influence, governance levels and law protection that would rationalise the use of funds and human resources.     

Niche variability and its consequences for species distribution modeling

When species distribution models (SDMs) are used to predict how a species will respond to environmental change, an important assumption is that the environmental niche of the species is conserved over evolutionary time-scales. Empirical studies conducted at ecological time-scales, however, demonstrate that the niche of some species can vary in response to environmental change. We use habitat and locality data of five species of stream fishes collected across seasons to examine the effects of niche variability on the accuracy of projections from Maxent, a popular SDM. We then compare these predictions to those from an alternate method of creating SDM projections in which a transformation of the environmental data to similar scales is applied. The niche of each species varied to some degree in response to seasonal variation in environmental variables, with most species shifting habitat use in response to changes in canopy cover or flow rate. SDMs constructed from the original environmental data accurately predicted the occurrences of one species across all seasons and a subset of seasons for two other species. A similar result was found for SDMs constructed from the transformed environmental data. However, the transformed SDMs produced better models in ten of the 14 total SDMs, as judged by ratios of mean probability values at known presences to mean probability values at all other locations. Niche variability should be an important consideration when using SDMs to predict future distributions of species because of its prevalence among natural populations. The framework we present here may potentially improve these predictions by accounting for such variability.     

Governance factors in the identification of global conservation priorities for mammals

Global conservation priorities have often been identified based on the combination of species richness and threat information. With the development of the field of systematic conservation planning, more attention has been given to conservation costs. This leads to prioritizing developing countries, where costs are generally low and biodiversity is high. But many of these countries have poor governance, which may result in ineffective conservation or in larger costs than initially expected. We explore how the consideration of governance affects the selection of global conservation priorities for the world's mammals in a complementarity-based conservation prioritization. We use data on Control of Corruption (Worldwide Governance Indicators project) as an indicator of governance effectiveness, and gross domestic product per capita as an indicator of cost. We show that, while core areas with high levels of endemism are always selected as important regardless of governance and cost values, there are clear regional differences in selected sites when biodiversity, cost or governance are taken into account separately. Overall, the analysis supports the concentration of conservation efforts in most of the regions generally considered of high priority, but stresses the need for different conservation approaches in different continents owing to spatial patterns of governance and economic development.     

Using species distribution models to predict new occurrences for rare plants

Aim  To evaluate a suite of species distribution models for their utility as predictors of suitable habitat and as tools for new population discovery of six rare plant species that have both narrow geographical ranges and specialized habitat requirements.
Location  The Rattlesnake Creek Terrane (RCT) of the Shasta-Trinity National Forest in the northern California Coast Range of the United States.
Methods  We used occurrence records from 25 years of US Forest Service botanical surveys, environmental and remotely sensed climate data to model the distributions of the target species across the RCT. The models included generalized linear models (GLM), artificial neural networks (ANN), random forests (RF) and maximum entropy (ME). From the results we generated predictive maps that were used to identify areas of high probability occurrence. We made field visits to the top-ranked sites to search for new populations of the target species.
Results  Random forests gave the best results according to area under the curve and Kappa statistics, although ME was in close agreement. While GLM and ANN also gave good results, they were less restrictive and more varied than RF and ME. Cross-model correlations were the highest for species with the most records and declined with record numbers. Model assessment using a separate dataset confirmed that RF provided the best predictions of appropriate habitat. Use of RF output to prioritize search areas resulted in the discovery of 16 new populations of the target species.
Main conclusions  Species distribution models, such as RF and ME, which use presence data and information about the background matrix where species do not occur, may be an effective tool for new population discovery of rare plant species, but there does appear to be a lower threshold in the number of occurrences required to build a good model.     

Using measurement error models to account for georeferencing error in species distribution models

Georeferencing error is prevalent in datasets used to model species distributions, inducing uncertainty in covariate values associated with species occurrences that result in biased probability of occurrence estimates. Traditionally, this error has been dealt with at the data‐level by using only records with an acceptable level of error (filtering) or by summarizing covariates at sampling units by using measures of central tendency (averaging). Here we compare those previous approaches to a novel implementation of a Bayesian logistic regression with measurement error (ME), a seldom used method in species distribution modeling. We show that the ME model outperforms data‐level approaches on 1) specialist species and 2) when either sample sizes are small, the georeferencing error is large or when all georeferenced occurrences have a fixed level of error. Thus, for certain types of species and datasets the ME model is an effective method to reduce biases in probability of occurrence estimates and account for the uncertainty generated by georeferencing error. Our approach may be expanded for its use with presence‐only data as well as to include other sources of uncertainty in species distribution models.     

Using species distribution models for IUCN Red Lists of threatened species

Red Lists have been used for years globally and regionally in many countries to highlight species that need special attention because of the rarity or rapid decline of their populations. To ensure homogenized classification at the global and regional level, the International Union for Conservation of Nature (IUCN) defined categories of threat, and criteria to attribute the taxa to these categories. Nevertheless, the strict application of the criteria is not always straightforward, especially for invertebrates, because of the difficulties associated with precise estimates of the size and viability of their populations. This paper presents a method for the estimation of extent of occurrence (EOO) and area of occupancy (AOO) based on species distribution models using multivariate adaptive regression splines. To achieve this, presence data have been modeled against topographical and climatic explanatory variables. Predictions from the statistical distribution models have then been cut using the minimal convex hull around (EOO) or the watersheds in which (AOO) the species have really been observed in recent years. This allows us to delimit the EOO and AOO according to the IUCN criteria, and better take into account the ecological requirements of the species. Furthermore, the method allows for the use of historical data (e.g. from museum’s collections) and the direct comparison of historical and recent distributions of species. The method has been tested on six species of butterflies. The results show the possibility of using species distribution models to define the Red Lists status according to the IUCN guidelines, and shows that the results are consistent with previous Red Lists assessments.     

Habitat differentiation of Lauraceae species in a tropical lower montane forest in northern Thailand

Dependency on topographical habitat was examined for Lauraceae tree species in a lower montane forest using a large-scale research plot established at Doi Inthanon National Park, northern Thailand. Twenty species of 10 genera of Lauraceae were recorded in a 7.5-ha part of the plot; Lauraceae accounted for 18% of the total basal area. Lauraceae was the most species-rich and most abundant family in the plot. In a cluster analysis based on the matrix of spatial associations between species, two clusters were recognized. Members of one cluster seemed to associate with lower-elevation habitats, and members of the other associated with habitats on ridges. By subdividing the study plot into 20m×20m squares, a discriminant analysis could be applied to the presence–absence data for the 17 species that had sufficient population density. The predictor variables used were the relative elevation, slope inclination, slope direction (transformed to deviation from SSW) and slope convexity for each of the squares. The discriminant models were tested statistically by applying the random shift technique. The models were significant for 11 of the species (65% of the species examined) and were associated with the topographical condition of the habitat. Stepwise selection of the predictor variables for these 11 species revealed that relative elevation and slope convexity were the most important factors for predicting the presence or absence of the Lauraceae species. Both these variables were considered to indicate the hydrological condition of the habitat.     

Using species distribution models to identify suitable areas for biofuel feedstock production

The 2007 Energy Independence and Security Act mandates a five‐fold increase in US biofuel production by 2022. Given this ambitious policy target, there is a need for spatially explicit estimates of landscape suitability for growing biofuel feedstocks. We developed a suitability modeling approach for two major US biofuel crops, corn (Zea mays) and switchgrass (Panicum virgatum), based upon the use of two presence‐only species distribution models (SDMs): maximum entropy (Maxent) and support vector machines (SVM). SDMs are commonly used for modeling animal and plant distributions in natural environments, but have rarely been used to develop landscape models for cultivated crops. AUC, Kappa, and correlation measures derived from test data indicate that SVM slightly outperformed Maxent in modeling US corn production, although both models produced significantly accurate results. When compared with results from a mechanistic switchgrass model recently developed by Oak Ridge National Laboratory (ORNL), SVM results showed higher correlation than Maxent results with models fit using county‐scale point inputs of switchgrass production derived from expert opinion estimates. However, Maxent results for an alternative switchgrass model developed with point inputs from research trial sites showed higher correlation to the ORNL model than the corresponding results obtained from SVM. Further analysis indicates that both modeling approaches were effective in predicting county‐scale increases in corn production from 2006 to 2007, a time period in which US corn production increased by 24%. We conclude that presence‐only methods are a powerful first‐cut tool for estimating relative land suitability across geographic regions in which candidate biofuel feedstocks can be grown, and may also provide important insight into potential land‐use change patterns likely to be associated with increased biofuel demand.     

New geographical distribution and host records of rust fungi from northern Thailand

Ten species of rust fungi (Crossopsora 2, Maravalia 1, Pileolaria 1, Puccinia 1, Ravenelia 1, Sphaerophragmium 1, Uredo 2, and Uromyces 1) are newly recorded together with six new host plants in Thailand.Contribution no. 194, Laboratory of Plant Parasitic Mycology, Graduate School of Life and Environmental Sciences, University of Tsukuba, Japan