Keep collecting: accurate species distribution modelling requires more collections than previously thought

Predicting suitable areas for Metcalfa pruinosa (Hemiptera: Flatidae) under climate change and implications for management

Climate change is a prominent factor reshaping the distribution of invasive species. Metcalfa pruinosa (Say 1830) (Hemiptera: Flatidae), native to North America, has invaded other continents and poses a serious threat to various agricultural crops and the human residential environment. Understanding the distribution of M. pruinosa based on climatic conditions is a critical first step to prevent its further invasion. Therefore, based on its occurrence records and associated environmental variables, a Maxent model was developed to predict suitable areas for this species in the present and future on a global scale. The model exhibited outstanding performance, with a mean area under the receiver operating characteristic curve and true skill statistic values of 0.9329 and 0.926, respectively. The model also indicated that annual precipitation (Bio12) and max temperature of the warmest month (Bio5) were the key environmental variables limiting the distribution of M. pruinosa. Moreover, the model revealed that the current suitable area is 1.01 × 107 km2 worldwide, with southern China, southern Europe, and the eastern United States predicted to be the primary and highly suitable areas in the latter 2 regions. This area is expected to increase under future climate scenarios, mainly in the northern direction. The study's findings contribute to our understanding of climate change's impact on M. pruinosa distribution, and they will aid governments in developing appropriate pest management strategies, including global monitoring and strict quarantine measures.

Mapping the habitat suitability of Ottelia species in Africa

Understanding the influence of environmental covariates on plant distribution is critical, especially for aquatic plant species. Climate change is likely to alter the distribution of aquatic species. However, knowledge of this change on the burden of aquatic macroorganisms is often fraught with difficulty. Ottelia, a model genus for studying the evolution of the aquatic family Hydrocharitaceae, is mainly distributed in slow-flowing creeks, rivers, or lakes throughout pantropical regions in the world. Due to recent rapid climate changes, natural Ottelia populations have declined significantly. By modeling the effects of climate change on the distribution of Ottelia species and assessing the degree of niche similarity, we sought to identify high suitability regions and help formulate conservation strategies. The models use known background points to determine how environmental covariates vary spatially and produce continental maps of the distribution of the Ottelia species in Africa. Additionally, we estimated the possible influences of the optimistic and extreme pessimistic representative concentration pathways scenarios RCP 4.5 and RCP 8.5 for the 2050s. Our results show that the distinct distribution patterns of studied Ottelia species were influenced by topography (elevation) and climate (e.g., mean temperature of driest quarter, annual precipitation, and precipitation of the driest month). While there is a lack of accord in defining the limiting factors for the distribution of Ottelia species, it is clear that water-temperature conditions have promising effects when kept within optimal ranges. We also note that climate change will impact Ottelia by accelerating fragmentation and habitat loss. The assessment of niche overlap revealed that Ottelia cylindrica and O . verdickii had slightly more similar niches than the other Ottelia species. The present findings identify the need to enhance conservation efforts to safeguard natural Ottelia populations and provide a theoretical basis for the distribution of various Ottelia species in Africa.

Identifying priority areas for bat conservation in the Western Ghats mountain range, peninsular India

Understanding patterns of species distribution and diversity plays a vital role in biodiversity conservation. Such documentation is frequently lacking for bats, which are relatively little studied and often threatened. The Western Ghats biodiversity hotspot in peninsular India is a bat hotspot with 63 species. We conducted a comprehensive bat survey across the southern Western Ghats and used maximum entropy modeling (MaxEnt) to model the potential distribution of 37 bat species for which sufficient data were available. We generated binary maps of each species using species-specific thresholds to estimate suitable habitat areas and overlaid binary maps of species to produce bat hotspots (we use the term “bat hotspot” for regions that were suitable for more than 25 bat species). We also estimated species richness across protected area networks in the southern Western Ghats to assess the level of protection. The highest levels of species richness were found mainly along the southmost Periyar–Agastyamalai landscape. The study also identified a 1,683 km2 area of potential bat hotspot and 726 km2 (43%) of the total bat hotspots are currently within the protected area network. However, more than 50% of suitable habitats for each of the 37 species remain unprotected. Therefore, conservation decisions are needed to take into account both bat hotspots and species with restricted distributions.

Native range estimates for red-listed vascular plants

Besides being central for understanding both global biodiversity patterns and associated anthropogenic impacts, species range maps are currently only available for a small subset of global biodiversity. Here, we provide a set of assembled spatial data for terrestrial vascular plants listed at the global IUCN red list. The dataset consists of pre-defined native regions for 47,675 species, density of available native occurrence records for 30,906 species, and standardized, large-scale Maxent predictions for 27,208 species, highlighting environmentally suitable areas within species’ native regions. The data was generated in an automated approach consisting of data scraping and filtering, variable selection, model calibration and model selection. Generated Maxent predictions were validated by comparing a subset to available expert-drawn range maps from IUCN (n = 4,257), as well as by qualitatively inspecting predictions for randomly selected species. We expect this data to serve as a substitute whenever expert-drawn species range maps are not available for conducting large-scale analyses on biodiversity patterns and associated anthropogenic impacts.

Measurement(s)	species distributions
Technology Type(s)	machine learning
Sample Characteristic - Organism	Tracheophyta
Sample Characteristic - Environment	terrestrial natural environment

Predicting the suitable habitats of parasitic desert species based on a niche model with Haloxylon ammodendron and Cistanche deserticola as examples

Haloxylon ammodendron, an excellent tree species for sand fixation and afforestation in the desert areas of western China, is threatened by climate change and anthropogenic activities. The suitable habitat of this species is shrinking at a remarkable rate, although conservation measures have been implemented. Cistanche deserticola is an entirely parasitic herb that occurs in deserts, is a source of "desert ginseng" worldwide, and has extremely high medicinal value. Little is known about using niche models to simulate habitat suitability and evaluate important environmental variables related to parasitic species. In this study, we modeled the current suitable habitat of H. ammodendron and C. deserticola by MaxEnt based on occurrence record data of the distributions of these two species in China. We grouped H. ammodendron and C. deserticola into three groups according to the characteristics of parasitic species and modeled them with environmental factors. The results showed that bioclimate was the most important environmental parameter affecting the H. ammodendron and C. deserticola distribution. Precipitations, such as annual precipitation, precipitation seasonality, and precipitation in the driest quarter, were identified as the most critical parameters. The slope, diurnal temperature range, water vapor pressure, ground-frost frequency, and solar radiation also substantially contributed to the distribution of the two species. The proportions of the most suitable areas for Groups 1, 2, and 3 were 1.2%, 1.3%, and 1.7%, respectively, in China. When combined with cultural geography, five hot spot conservation areas were determined within the distribution of H. ammodendron and C. deserticola. The comprehensive analysis indicated that by using MaxEnt to model the suitable habitat of parasitic species, we further improved the accuracy of the prediction and coupled the error of the distribution of a single species. This study provides a useful reference for the protection of H. ammodendron forests and the management of C. deserticola plantations.

Assessment of potential invasion for six phytophagous quarantine pests in Taiwan

Pest risk assessment is typically performed by expert taxonomists using a pest’s biological data. However, the biological data or expert taxonomists may be difficult to find. Here, we used species distribution modelling to predict potential invasion in which phytophagous quarantine pests survive in Taiwan; the pests (unrecorded yet in Taiwan) included were three notorious quarantine whiteflies (Crenidorsum aroidephagus, Aleurothrixus trachoides, and Paraleyrodes minei) and three aphids (Nasonovia ribisnigri, Macrosiphum euphorbiae, and Viteus vitifoliae). In brief, maximum entropy modelling (MaxEnt) was used to predict the suitability of the pests’ habitats under certain climatic conditions, and then receiver operating characteristic curve analysis was performed (to verify the prediction result). We then analysed environmental variables affecting the habitat suitability and matched them with Taiwan’s crop cultivation areas for the assessment of potential invasion. We observed that the habitat suitability of the cultivation areas of host plants was low for C. aroidephagus, A. trachoides, and N. ribisnigri but was high for the remaining three species. Moreover, precipitation of coldest quarter negatively affected habitat suitability for C. aroidephagus, P. minei, N. ribisnigri, and M. euphorbiae. Seasonal temperature changes also negatively affected the habitat suitability for A. trachoides. This is the first study to demonstrate the use of species distribution modelling as the preliminary step for the pest risk assessment of these emerging pests with limited biological data before their invasion.

Predicting migration routes for three species of migratory bats using species distribution models

Understanding seasonal variation in the distribution and movement patterns of migratory species is essential to monitoring and conservation efforts. While there are many species of migratory bats in North America, little is known about their seasonal movements. In terms of conservation, this is important because the bat fatalities from wind energy turbines are significant and may fluctuate seasonally. Here we describe seasonally resolved distributions for the three species that are most impacted by wind farms (Lasiurus borealis (eastern red bat), L. cinereus (hoary bat) and Lasionycteris noctivagans (silver-haired bat)) and use these distributions to infer their most likely migratory pathways. To accomplish this, we collected 2,880 occurrence points from the Global Biodiversity Information Facility over five decades in North America to model species distributions on a seasonal basis and used an ensemble approach for modeling distributions. This dataset included 1,129 data points for L. borealis, 917 for L. cinereus and 834 for L. noctivagans. The results suggest that all three species exhibit variation in distributions from north to south depending on season, with each species showing potential migratory pathways during the fall migration that follow linear features. Finally, we describe proposed migratory pathways for these three species that can be used to identify stop-over sites, assess small-scale migration and highlight areas that should be prioritized for actions to reduce the effects of wind farm mortality.

Niche Modeling May Explain the Historical Population Failure of Phytoseiulus persimilis in Taiwan: Implications of Biocontrol Strategies

Biological control commonly involves the commercialization and introduction of natural enemies. Phytoseiulus persimilis Athias-Henriot, a mite species widely used in the control of spider mites, was imported to Taiwan in the 1990s and was mass-reared and released into the field. However, none have been observed in comprehensive surveys of phytoseiid mites for over 30 years. In this study, the distribution of P. persimilis in Taiwan was predicted, and environmental variables that affect its distribution were analyzed. The mountainous region of southcentral Taiwan was determined to be suitable for the establishment of this species, whereas the four sites at which it was released in the 1990s, particularly those in southwestern Taiwan, exhibited low suitability. Notably, the minimum temperature of the coldest month was identified as a crucial limiting factor affecting the distribution of P. persimilis, indicating that a Mediterranean climate is more suitable for this species. To the best of our knowledge, this study is the first to predict the suitable distribution of exotic predatory mites in a biological control program. The present findings serve as a pivotal assessment framework for the commercialization and foreign introduction of natural enemies.

Evaluation metrics and validation of presence-only species distribution models based on distributional maps with varying coverage

We examine how different datasets, including georeferenced hardcopy maps of different extents and georeferenced herbarium specimens (spanning the range from 100 to 85,000 km²) influence ecological niche modeling. We check 13 of the available environmental niche modeling algorithms, using 30 metrics to score their validity and evaluate which are useful for the selection of the best model. The validation is made using an independent dataset comprised of presences and absences collected in a range-wide field survey of Carpathian endemic plant Leucanthemum rotundifolium (Compositae). Our analysis of models' predictive performances indicates that almost all datasets may be used for the construction of a species distributional range. Both very local and very general datasets can produce useful predictions, which may be more detailed than the original ranges. Results also highlight the possibility of using the data from manually georeferenced archival sources in reconstructions aimed at establishing species' ecological niches. We discuss possible applications of those data and associated problems. For the evaluation of models, we suggest employing AUC, MAE, and Bias. We show an example of how AUC and MAE may be combined to select the model with the best performance.

Small herbaria contribute unique biogeographic records to county, locality, and temporal scales

PREMISE

With digitization and data sharing initiatives underway over the last 15 years, an important need has been prioritizing specimens to digitize. Because duplicate specimens are shared among herbaria in exchange and gift programs, we investigated the extent to which unique biogeographic data are held in small herbaria vs. these data being redundant with those held by larger institutions. We evaluated the unique specimen contributions that small herbaria make to biogeographic understanding at county, locality, and temporal scales.

METHODS

We sampled herbarium specimens of 40 plant taxa from each of eight states of the United States of America in four broad status categories: extremely rare, very rare, common native, and introduced. We gathered geographic information from specimens held by large (≥100,000 specimens) and small (<100,000 specimens) herbaria. We built generalized linear mixed models to assess which features of the collections may best predict unique contributions of herbaria and used an Akaike information criterion-based information-theoretic approach for our model selection to choose the best model for each scale.

RESULTS

Small herbaria contributed unique specimens at all scales in proportion with their contribution of specimens to our data set. The best models for all scales were the full models that included the factors of species status and herbarium size when accounting for state as a random variable.

CONCLUSIONS

We demonstrated that small herbaria contribute unique information for research. It is clear that unique contributions cannot be predicted based on herbarium size alone. We must prioritize digitization and data sharing from herbaria of all sizes.

Potential Distribution of the Biocontrol Agent Toxorhynchites rutilus By 2070

Climate change projections indicate that mosquito distributions will expand to include new areas of North America, increasing human exposure to mosquito-borne disease. Controlling these vectors is imperative, as mosquito-borne disease incidence will rise in response to expansion of mosquito range and increased seasonality. One means of mosquito control used in the USA is the biocontrol agent, Toxorhynchites rutilus. Climate change will open new habitats for its use by vector control organizations, but the extent of this change in habitat is currently unknown. We used a maximum entropy approach to create species distribution models for Tx. rutilus under 4 climate change scenarios by 2070. Mean temperature of warmest quarter (22.6°C to 29.1°C), annual precipitation (1,025.15 mm to 1,529.40 mm), and precipitation seasonality (≤17.86) are the most important bioclimatic variables for suitable habitat. The center of current possible habitat distribution of Tx. rutilus is in central Tennessee. Depending upon the scenario, we expect centroids to shift north-northeast by 97.68 km to 280.16 km by 2070. The extreme change in area of greater than 50% suitable habitat probability is 141.14% with 99.44% area retained. Our models indicate limited change in current habitat as well as creation of new habitat. These results are promising for North American mosquito control programs for the continued and potential combat of vector mosquitoes using Tx. rutilus.

Assessing congruence of opportunistic records and systematic surveys for predicting Hispaniolan mammal species distributions

Comparative assessment of the relative information content of different independent spatial data types is necessary to evaluate whether they provide congruent biogeographic signals for predicting species ranges. Opportunistic occurrence records and systematically collected survey data are available from the Dominican Republic for Hispaniola's surviving endemic non-volant mammals, the Hispaniolan solenodon (Solenodon paradoxus) and Hispaniolan hutia (Plagiodontia aedium); opportunistic records (archaeological, historical and recent) exist from across the entire country, and systematic survey data have been collected from seven protected areas. Species distribution models were developed in maxent for solenodons and hutias using both data types, with species habitat suitability and potential country-level distribution predicted using seven biotic and abiotic environmental variables. Three different models were produced and compared for each species: (a) opportunistic model, with starting model incorporating abiotic-only predictors; (b) total survey model, with starting model incorporating biotic and abiotic predictors; and (c) reduced survey model, with starting model incorporating abiotic-only predictors to allow further comparison with the opportunistic model. All models predict suitable environmental conditions for both solenodons and hutias across a broadly congruent, relatively large area of the Dominican Republic, providing a spatial baseline of conservation-priority landscapes that might support native mammals. Correlation between total and reduced survey models is high for both species, indicating the substantial explanatory power of abiotic variables for predicting Hispaniolan mammal distributions. However, correlation between survey models and opportunistic models is only moderately positive. Species distribution models derived from different data types can provide different predictions about habitat suitability and conservation-priority landscapes for threatened species, likely reflecting incompleteness and bias in spatial sampling associated with both data types. Models derived using both opportunistic and systematic data must therefore be applied critically and cautiously.

An initial industrial flora: A framework for botanical research in cooperation with industry for biodiversity conservation

Humans have created an accelerating, increasingly connected, globalized economy, resulting in a more globalized, shared flora. The prevention of new, establishing species is less costly, both economically and ecologically, and is more manageable than eradicating nonnative invasive species once they are widespread and negatively impactful. We ask if international trade hubs and points-of-entry with high-volume trade, constant disturbance, and propagule rain have a higher number of nonnative species compared to surrounding areas and if they may serve as initial establishment sites and refugia of nonnative, invasive populations. Therefore, we partnered with various federal, state, and private interests to evaluate the floristic composition at the Garden City Terminal of the Port of Savannah, Georgia, USA. We conducted the following study to demonstrate the collaborative relationship-building between researchers and industry and to develop a framework for biodiversity conservation. In our study, we collected all reproductive vascular plants in the secured areas of the Garden City Terminal during four major seasonal time points over two years. The percent of nonnative species and number of nonnative plant species per hectare at this industrial location exceeded all other comparison floras. The mean coefficient of conservatism was lowest among the comparison floras, indicating a highly disturbed habitat with nonnative, weedy native, and other native species tolerant of disturbance. Our study represents one of the first inventories of an Industrialized Flora and indicates that such areas are hot-spots of nonnative plant diversity and possible sources of emergent plant invasions. We posit that industrial sites and international points-of-entry should be considered laboratories for research on species transport and introduction, adaptability, and taxonomic delineation to better understand the mechanisms and consequences of biotic homogenization due to the volume and frequency of anthropogenic activities.

Climatic-niche evolution follows similar rules in plants and animals

Climatic niches are essential in determining where species can occur and how they will respond to climate change. However, it remains unclear if climatic-niche evolution is similar in plants and animals or is intrinsically different. For example, previous authors have proposed that plants have broader environmental tolerances than animals but are more sensitive to climate change. Here, we test ten predictions about climatic-niche evolution in plants and animals, using phylogenetic and climatic data for 19 plant clades and 17 vertebrate clades (2,087 species total). Surprisingly, we find that for all ten predictions, plants and animals show similar patterns. For example, in both groups, climatic niches change at similar mean rates and species have similar mean niche breadths, and niche breadths show similar relationships with latitude across groups. Our results suggest that there are general 'rules' of climatic-niche evolution that span plants and animals, despite the fundamental differences in their biology. These results may help to explain why plants and animals have similar responses to climate change and why they often have shared species richness patterns, biogeographic regions, biomes and biodiversity hotspots.

A new striking and critically endangered species of Nasa (Loasaceae, Cornales) from North Peru

Nasaangeldiazioides sp. nov. is described and illustrated. The species is restricted to two forest remnants on the western slope of the northern Peruvian Andes (Dept. Lambayeque) where it is found in the undergrowth of primary forest. The new taxon shows a unique leaf morphology in the family Loasaceae. Molecular and morphological data show that the new species belongs to the Nasatriphylla group. Since the relic forests of the north-western Andes are increasingly threatened by the effects of climate change, i.e. droughts and wildfires, the new species already faces imminent extinction.

A novel proof of concept for capturing the diversity of endophytic fungi preserved in herbarium specimens

Herbarium specimens represent important records of morphological and genetic diversity of plants that inform questions relevant to global change, including species distributions, phenology and functional traits. It is increasingly appreciated that plant microbiomes can influence these aspects of plant biology, but little is known regarding the historic distribution of microbes associated with plants collected in the pre-molecular age. If microbiomes can be observed reliably in herbarium specimens, researchers will gain a new lens with which to examine microbial ecology, evolution, species interactions. Here, we describe a method for accessing historical plant microbiomes from preserved herbarium specimens, providing a proof of concept using two plant taxa from the imperiled boreal biome (Andromeda polifolia and Ledum palustre subsp. groenlandicum, Ericaceae). We focus on fungal endophytes, which occur within symptomless plant tissues such as leaves. Through a three-part approach (i.e. culturing, cloning and next-generation amplicon sequencing via the Illumina MiSeq platform, with extensive controls), we examined endophyte communities in dried, pressed leaves that had been processed as regular herbarium specimens and stored at room temperature in a herbarium for four years. We retrieved only one endophyte in culture, but cloning and especially the MiSeq analysis revealed a rich community of foliar endophytes. The phylogenetic distribution and diversity of endophyte assemblages, especially among the Ascomycota, resemble endophyte communities from fresh plants collected in the boreal biome. We could distinguish communities of endophytes in each plant species and differentiate likely endophytes from fungi that could be surface contaminants. Taxa found by cloning were observed in the larger MiSeq dataset, but species richness was greater when subsets of the same tissues were evaluated with the MiSeq approach. Our findings provide a proof of concept for capturing endophyte DNA from herbarium specimens, supporting the importance of herbarium records as roadmaps for understanding the dynamics of plant-associated microbial biodiversity in the Anthropocene.This article is part of the theme issue 'Biological collections for understanding biodiversity in the Anthropocene'.

The informative value of museum collections for ecology and conservation: A comparison with target sampling in the Brazilian Atlantic forest

Since two decades the richness and potential of natural history collections (NHC) were rediscovered and emphasized, promoting a revolution in the access on data of species occurrence, and fostering the development of several disciplines. Nevertheless, due to their inherent erratic nature, NHC data are plagued by several biases. Understanding these biases is a major issue, particularly because ecological niche models (ENMs) are based on the assumption that data are not biased. Based on it, a recent body of research have focused on searching adequate methods for dealing with biased data and proposed the use of filters in geographical and environmental space. Although the strength of filtering in environmental space has been shown with virtual species, nothing has yet been tested with a real dataset including field validation. In order to contribute to this task, we explore this issue by comparing a dataset from NHC to a recent targeted sampling of the cockroach genus Monastria Saussure, 1864 in the Brazilian Atlantic forest. We showed that, despite strong similarities, the area modeled with NHC data was much smaller. These differences were due to strong climate biases, which increased model's specificity and reduced sensitivity. By applying two forms of rarefaction in the environmental space, we showed that deleting points at random in the most biased climate class is a powerful way for increasing model's sensitivity, so making predictions more suitable to the reality.

A decision tree for assessing the risks and benefits of publishing biodiversity data

Inadequate information on the geographical distribution of biodiversity hampers decision-making for conservation. Major efforts are underway to fill knowledge gaps, but there are increasing concerns that publishing the locations of species is dangerous, particularly for species at risk of exploitation. While we recognize that well-informed control of location data for highly sensitive taxa is necessary to avoid risks, such as poaching or habitat disturbance by recreational visitors, we argue that ignoring the benefits of sharing biodiversity data could unnecessarily obstruct conservation efforts for species and locations with low risks of exploitation. We provide a decision tree protocol for scientists that systematically considers both the risks of exploitation and potential benefits of increased conservation activities. Our protocol helps scientists assess the impacts of publishing biodiversity data and aims to enhance conservation opportunities, promote community engagement and reduce duplication of survey efforts.

A methodological approach to identify agro-biodiversity hotspots for priority in situ conservation of plant genetic resources

Agro-biodiversity is seriously threatened worldwide and strategies to preserve it are dramatically required. We propose here a methodological approach aimed to identify areas with a high level of agro-biodiversity in which to set or enhance in situ conservation of plant genetic resources. These areas are identified using three criteria: Presence of Landrace diversity, Presence of wild species and Agro-ecosystem ecological diversity. A Restrictive and an Additive prioritization strategy has been applied on the entire Italian territory and has resulted in establishing nationwide 53 and 197 agro-biodiversity hotspots respectively. At present the strategies can easily be applied at a European level and can be helpful to develop conservation strategies everywhere.

Determining threatened species distributions in the face of limited data: Spatial conservation prioritization for the Chinese giant salamander (Andrias davidianus)

The purpose of this study was to determine whether limited occurrence data for highly threatened species can provide useful spatial information to inform conservation. The study was conducted across central and southern China. We developed a habitat suitability model for the Critically Endangered Chinese giant salamander (Andrias davidianus) based on one biotic and three abiotic parameters from single‐site locality records, which represent the only relevant environmental data available for this species. We then validated model quality by testing whether increased percentage of predicted suitable habitat at the county level correlated with independent data on giant salamander presence. We randomly selected 48 counties containing historical records which were distinct from, and independent of, the single‐site records used to develop the model, and 47 additional counties containing >50% predicted suitable habitat. We interviewed 2,812 respondents near potential giant salamander habitat across these counties and tested for differences in respondent giant salamander reports between counties selected using each method. Our model predicts that suitable giant salamander habitat is found widely across central and southern China, with counties containing ≥50% predicted suitable habitat distributed in 13 provinces. Counties with historical records contain significantly more predicted suitable habitat than counties without historical records. There are no statistical differences in any patterns of respondent giant salamander reports in surveyed counties selected from our model compared with the areas of known historical giant salamander distribution. A Chinese giant salamander habitat suitability model with strong predictive power can be derived from the restricted range of environmental variables associated with limited available presence‐only occurrence records, constituting a cost‐effective strategy to guide spatial allocation of conservation planning. Few reported sightings were recent, however, with most being over 20 years old, so that identification of areas of suitable habitat does not necessarily indicate continued survival of the species at these locations.

Influence of current climate, historical climate stability and topography on species richness and endemism in Mesoamerican geophyte plants

Background

A number of biotic and abiotic factors have been proposed as drivers of geographic variation in species richness. As biotic elements, inter-specific interactions are the most widely recognized. Among abiotic factors, in particular for plants, climate and topographic variables as well as their historical variation have been correlated with species richness and endemism. In this study, we determine the extent to which the species richness and endemism of monocot geophyte species in Mesoamerica is predicted by current climate, historical climate stability and topography.

Methods

Using approximately 2,650 occurrence points representing 507 geophyte taxa, species richness (SR) and weighted endemism (WE) were estimated at a geographic scale using grids of 0.5 × 0.5 decimal degrees resolution using Mexico as the geographic extent. SR and WE were also estimated using species distributions inferred from ecological niche modeling for species with at least five spatially unique occurrence points. Current climate, current to Last Glacial Maximum temperature, precipitation stability and topographic features were used as predictor variables on multiple spatial regression analyses (i.e., spatial autoregressive models, SAR) using the estimates of SR and WE as response variables. The standardized coefficients of the predictor variables that were significant in the regression models were utilized to understand the observed patterns of species richness and endemism.

Results

Our estimates of SR and WE based on direct occurrence data and distribution modeling generally yielded similar results, though estimates based on ecological niche modeling indicated broader distribution areas for SR and WE than when species richness was directly estimated using georeferenced coordinates. The SR and WE of monocot geophytes were highest along the Trans-Mexican Volcanic Belt, in both cases with higher levels in the central area of this mountain chain. Richness and endemism were also elevated in the southern regions of the Sierra Madre Oriental and Occidental mountain ranges, and in the Tehuacán Valley. Some areas of the Sierra Madre del Sur and Sierra Madre Oriental had high levels of WE, though they are not the areas with the highest SR. The spatial regressions suggest that SR is mostly influenced by current climate, whereas endemism is mainly affected by topography and precipitation stability.

Conclusions

Both methods (direct occurrence data and ecological niche modeling) used to estimate SR and WE in this study yielded similar results and detected a key area that should be considered in plant conservation strategies: the central region of the Trans-Mexican Volcanic Belt. Our results also corroborated that species richness is more closely correlated with current climate factors while endemism is related to differences in topography and to changes in precipitation levels compared to the LGM climatic conditions.

Amazon plant diversity revealed by a taxonomically verified species list

Recent debates on the number of plant species in the vast lowland rain forests of the Amazon have been based largely on model estimates, neglecting published checklists based on verified voucher data. Here we collate taxonomically verified checklists to present a list of seed plant species from lowland Amazon rain forests. Our list comprises 14,003 species, of which 6,727 are trees. These figures are similar to estimates derived from nonparametric ecological models, but they contrast strongly with predictions of much higher tree diversity derived from parametric models. Based on the known proportion of tree species in neotropical lowland rain forest communities as measured in complete plot censuses, and on overall estimates of seed plant diversity in Brazil and in the neotropics in general, it is more likely that tree diversity in the Amazon is closer to the lower estimates derived from nonparametric models. Much remains unknown about Amazonian plant diversity, but this taxonomically verified dataset provides a valid starting point for macroecological and evolutionary studies aimed at understanding the origin, evolution, and ecology of the exceptional biodiversity of Amazonian forests.

Global priorities for an effective information basis of biodiversity distributions

Gaps in digital accessible information (DAI) on species distributions hamper prospects of safeguarding biodiversity and ecosystem services, and addressing central ecological and evolutionary questions. Achieving international targets on biodiversity knowledge requires that information gaps be identified and actions prioritized. Integrating 157 million point records and distribution maps for 21,170 terrestrial vertebrate species, we find that outside a few well-sampled regions, DAI on point occurrences provides very limited and spatially biased inventories of species. Surprisingly, many large, emerging economies are even more under-represented in global DAI than species-rich, developing countries in the tropics. Multi-model inference reveals that completeness is mainly limited by distance to researchers, locally available research funding and participation in data-sharing networks, rather than transportation infrastructure, or size and funding of Western data contributors as often assumed. Our results highlight the urgent need for integrating non-Western data sources and intensifying cooperation to more effectively address societal biodiversity information needs.

Digitization workflows for flat sheets and packets of plants, algae, and fungi

Effective workflows are essential components in the digitization of biodiversity specimen collections. To date, no comprehensive, community-vetted workflows have been published for digitizing flat sheets and packets of plants, algae, and fungi, even though latest estimates suggest that only 33% of herbarium specimens have been digitally transcribed, 54% of herbaria use a specimen database, and 24% are imaging specimens. In 2012, iDigBio, the U.S. National Science Foundation's (NSF) coordinating center and national resource for the digitization of public, nonfederal U.S. collections, launched several working groups to address this deficiency. Here, we report the development of 14 workflow modules with 7-36 tasks each. These workflows represent the combined work of approximately 35 curators, directors, and collections managers representing more than 30 herbaria, including 15 NSF-supported plant-related Thematic Collections Networks and collaboratives. The workflows are provided for download as Portable Document Format (PDF) and Microsoft Word files. Customization of these workflows for specific institutional implementation is encouraged.

Climate reconstruction analysis using coexistence likelihood estimation (CRACLE): a method for the estimation of climate using vegetation

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PREMISE OF THE STUDY

Plant distributions have long been understood to be correlated with the environmental conditions to which species are adapted. Climate is one of the major components driving species distributions. Therefore, it is expected that the plants coexisting in a community are reflective of the local environment, particularly climate.•

METHODS

Presented here is a method for the estimation of climate from local plant species coexistence data. The method, Climate Reconstruction Analysis using Coexistence Likelihood Estimation (CRACLE), is a likelihood-based method that employs specimen collection data at a global scale for the inference of species climate tolerance. CRACLE calculates the maximum joint likelihood of coexistence given individual species climate tolerance characterization to estimate the expected climate.•

KEY RESULTS

Plant distribution data for more than 4000 species were used to show that this method accurately infers expected climate profiles for 165 sites with diverse climatic conditions. Estimates differ from the WorldClim global climate model by less than 1.5°C on average for mean annual temperature and less than ∼250 mm for mean annual precipitation. This is a significant improvement upon other plant-based climate-proxy methods.•

CONCLUSIONS

CRACLE validates long hypothesized interactions between climate and local associations of plant species. Furthermore, CRACLE successfully estimates climate that is consistent with the widely used WorldClim model and therefore may be applied to the quantitative estimation of paleoclimate in future studies.

An estimate of the number of tropical tree species

The high species richness of tropical forests has long been recognized, yet there remains substantial uncertainty regarding the actual number of tropical tree species. Using a pantropical tree inventory database from closed canopy forests, consisting of 657,630 trees belonging to 11,371 species, we use a fitted value of Fisher's alpha and an approximate pantropical stem total to estimate the minimum number of tropical forest tree species to fall between ∼ 40,000 and ∼ 53,000, i.e., at the high end of previous estimates. Contrary to common assumption, the Indo-Pacific region was found to be as species-rich as the Neotropics, with both regions having a minimum of ∼ 19,000-25,000 tree species. Continental Africa is relatively depauperate with a minimum of ∼ 4,500-6,000 tree species. Very few species are shared among the African, American, and the Indo-Pacific regions. We provide a methodological framework for estimating species richness in trees that may help refine species richness estimates of tree-dependent taxa.

Reptiles of Chubut province, Argentina: richness, diversity, conservation status and geographic distribution maps

An accurate estimation of species and population geographic ranges is essential for species-focused studies and conservation and management plans. Knowledge of the geographic distributions of reptiles from Patagonian Argentina is in general limited and dispersed over manuscripts from a wide variety of topics. We completed an extensive review of reptile species of central Patagonia (Argentina) based on information from a wide variety of sources. We compiled and checked geographic distribution records from published literature and museum records, including extensive new data from the LJAMM-CNP (CENPAT-CONICET) herpetological collection. Our results show that there are 52 taxa recorded for this region and the highest species richness was seen in the families Liolaemidae and Dipsadidae with 31 and 10 species, respectively. The Patagónica was the phytogeographic province most diverse in species and Phymaturus was the genus of conservation concern most strongly associated with it. We present a detailed species list with geographical information, richness species, diversity analyses with comparisons across phytogeographical provinces, conservation status, taxonomic comments and distribution maps for all of these taxa.