Diversity and geographic distribution of ciliates (Protista: Ciliophora)

About 4,500 free-living ciliate morphospecies have been described, applying an average synonymy rate of 20%. We estimate that 83–89% of the ciliate diversity is still undescribed, using the following probabilities: detailed habitat studies suggest that the described number of morphospecies must be doubled: 4,500 → 9,000; this figure has to be increased by about 50% due to species with similar interphase morphology but different resting cysts: 9,000 → 13,500; the genetic and molecular data suggest that this value must be doubled or trebled: 13,500 → 27,000 to 40,000 free-living, biological ciliate species. The knowledge on geographic distribution of ciliates heavily depends on flagship species and statistical analyses because reliable faunistic studies are rare and molecular data are still in its infancy. We present a list of 52 ciliate flagship species as a testable hypothesis, i.e., the hypothesis of restricted distribution of certain ciliate species must be refused when a considerable number of them is found in all or most biogeographic regions. Flagship species and statistical analyses consistently show Gondwanan and Laurasian ciliate communities, suggesting that the split of Pangaea deeply influenced ciliate distribution and rare species play a key role in geographic differentiation. However, there is also substantial evidence for continental, regional, and local endemism of free-living ciliates. The molecular studies usually show a high level of genetic diversity underlying ciliate morphospecies, suggesting that morphologic and molecular evolution may be decoupled in many ciliate species. Molecular studies on ciliate biogeography are at variance, possibly because most are still focusing on single molecular markers. In sum, the data indicate that ciliate biogeography is similar to that of plants and animals, but with an increased proportion of cosmopolites, favouring the moderate endemicity model. Special Issue: Protist diversity and geographic distribution. Guest editor: W. Foissner.     

Habitat distribution models for intertidal seaweeds: responses to climatic and non‐climatic drivers

Aim Because intertidal organisms often live close to their physiological tolerance limits, they are potentially sensitive indicators of climate‐driven changes in the environment. The goals of this study were to assess the effect of climatic and non‐climatic factors on the geographical distribution of intertidal macroalgae, and to predict future distributions under different climate‐warming scenarios. Location North‐western Iberian Peninsula, southern Europe. Methods We developed distribution models for six ecologically important intertidal seaweed species. Occurrence and microhabitat data were sampled at 1‐km² resolution and analysed with climate variables measured at larger spatial scales. We used generalized linear models and applied the deviance and Bayesian information criterion to model the relationship between environmental variables and the distribution of each target species. We also used hierarchical partitioning (HP) to identify predictor variables with higher independent explanatory power. Results The distributions of Himanthalia elongata and Bifurcaria bifurcata were correlated with measures of terrestrial and marine climate, although in opposite directions. Model projections under two warming scenarios indicated the extinction of the former at a faster rate in the Cantabrian Sea (northern Spain) than in the Atlantic (west). In contrast, these models predicted an increase in the occurrence of B. bifurcata in both areas. The occurrences of Ascophyllum nodosum and Pelvetia canaliculata, species showing rather static historical distributions, were related to specific non‐climatic environmental conditions and locations, such as the location of sheltered sites. At the southernmost distributional limit, these habitats may present favourable microclimatic conditions or provide refuges from competitors or natural enemies. Model performances for Fucus vesiculosus and F. serratus were similar and poor, but several climatic variables influenced the occurrence of the latter in the HP analyses. Main conclusions The correlation between species distributions and climate was evident for two species, whereas the distributions of the others were associated with non‐climatic predictors. We hypothesize that the distribution of F. serratus responds to diverse combinations of factors in different sections of the north‐west Iberian Peninsula. Our study shows how the response of species distributions to climatic and non‐climatic variables may be complex and vary geographically. Our analyses also highlight the difficulty of making predictions based solely on variation in climatic factors measured at coarse spatial scales.     

The influence of sampling intensity on the perception of the spatial distribution of tropical diversity and endemism: a case study of ferns from Bolivia

The distribution of tropical plant and animal diversity is still poorly documented, especially at spatial resolutions of practical use for conservation. In the present study, we evaluated the level to which geographical incomplete data availability of species occurrence affects the perception of biodiversity patterns (species richness and endemism) among pteridophytes in Bolivia. We used a data base of Bolivian pteridophytes (27,501 records), divided it into three time periods (1900–70, up to 1990 and up to 2006), and created grid-files at 15'-resolution for species richness and endemism. For each of these biodiversity properties we estimated the species richness (Chao 2) and the index of sampling completeness (C index) per grid, and then all these variables at both species richness and endemism were correlated. Patterns of richness were fairly consistent along all periods; the richest areas were placed along the humid-montane forest, even though they were strongly influenced by collecting intensity. Endemism had a lower degree of correlation with collecting intensity, but varied much more strongly through time than species richness. According to the C index, which gives the ratio between estimated (by Chao 2) and recorded values of species richness and endemism, both biodiversity properties tended to be undersampled in the richest grid cells. Inter-temporal correlations showed sharper differences of correlations for endemism than species richness. Consequently, already in 1970, botanists had a correct idea of the spatial distribution of pteridophyte richness in Bolivia (even though the magnitude was grossly underestimated). In contrast, patterns of endemism, which are of high conservation importance, may not even today be reliably known.     

Impacts of predictor variables and species models on simulating Tamarix ramosissima distribution in Tarim Basin,northwestern China

Aims Preserving and restoring Tamarix ramosissima is urgently required in the Tarim Basin, Northwest China. Using species distribution models to predict the biogeographical distribution of species is regularly used in conservation and other management activities. However, the uncertainty in the data and models inevitably reduces their prediction power. The major purpose of this study is to assess the impacts of predictor variables and species distribution models on simulating T. ramosissima distribution, to explore the relationships between predictor variables and species distribution models and to model the potential distribution of T. ramosissima in this basin.Methods Three models—the generalized linear model (GLM), classification and regression tree (CART) and Random Forests—were selected and were processed on the BIOMOD platform. The presence/absence data of T. ramosissima in the Tarim Basin, which were calculated from vegetation maps, were used as response variables. Climate, soil and digital elevation model (DEM) data variables were divided into four datasets and then used as predictors. The four datasets were (i) climate variables, (ii) soil, climate and DEM variables, (iii) principal component analysis (PCA)-based climate variables and (iv) PCA-based soil, climate and DEM variables.Important findings The results indicate that predictive variables for species distribution models should be chosen carefully, because too many predictors can reduce the prediction power. The effectiveness of using PCA to reduce the correlation among predictors and enhance the modelling power depends on the chosen predictor variables and models. Our results implied that it is better to reduce the correlating predictors before model processing. The Random Forests model was more precise than the GLM and CART models. The best model for T. ramosissima was the Random Forests model with climate predictors alone. Soil variables considered in this study could not significantly improve the model's prediction accuracy for T. ramosissima. The potential distribution area of T. ramosissima in the Tarim Basin is ~3.57 × 10 4 km 2, which has the potential to mitigate global warming and produce bioenergy through restoring T. ramosissima in the Tarim Basin.     

Projecting future expansion of invasive species: comparing and improving methodologies for species distribution modeling

Modeling the distributions of species, especially of invasive species in non‐native ranges, involves multiple challenges. Here, we developed some novel approaches to species distribution modeling aimed at reducing the influences of such challenges and improving the realism of projections. We estimated species–environment relationships for Parthenium hysterophorus L. (Asteraceae) with four modeling methods run with multiple scenarios of (i) sources of occurrences and geographically isolated background ranges for absences, (ii) approaches to drawing background (absence) points, and (iii) alternate sets of predictor variables. We further tested various quantitative metrics of model evaluation against biological insight. Model projections were very sensitive to the choice of training dataset. Model accuracy was much improved using a global dataset for model training, rather than restricting data input to the species’ native range. AUC score was a poor metric for model evaluation and, if used alone, was not a useful criterion for assessing model performance. Projections away from the sampled space (i.e., into areas of potential future invasion) were very different depending on the modeling methods used, raising questions about the reliability of ensemble projections. Generalized linear models gave very unrealistic projections far away from the training region. Models that efficiently fit the dominant pattern, but exclude highly local patterns in the dataset and capture interactions as they appear in data (e.g., boosted regression trees), improved generalization of the models. Biological knowledge of the species and its distribution was important in refining choices about the best set of projections. A post hoc test conducted on a new Parthenium dataset from Nepal validated excellent predictive performance of our ‘best’ model. We showed that vast stretches of currently uninvaded geographic areas on multiple continents harbor highly suitable habitats for parthenium. However, discrepancies between model predictions and parthenium invasion in Australia indicate successful management for this globally significant weed.     

The Pyrenees as a cradle of plant diversity: phylogeny,phylogeography and niche modeling of Saxifraga longifolia

The current distribution of most species results from ecological niche, past distribution, and migrations during glacial–interglacial periods and in situ evolution. Here, we disentangle the colonization history of Saxifraga longifolia Lapeyr., a limestone plant abundant in the Pyrenees and rare in other Iberian mountains and the African Atlas. Our working hypothesis is that the current distribution results from the shrinkage of a more extensive distribution in previous cold periods. We sampled 160 individuals of 32 populations across the whole distribution range and sequenced four DNA regions (rpl32-trnL, rps16-trnQ, trnS-trnG, and ITS). Ecological conditions were modeled to identify factors promoting high genetic diversity and long-term persistence areas for S. longifolia. In addition, we inferred phylogenetic relationships, phylogeographic divergence, genetic diversity, and migration routes. Seven plastid haplotypes were found, of which six occur in the Pyrenees and one in the High Atlas (Morocco). Discrete phylogeographic analysis (DPA) estimated migration routes predominantly from the Pyrenees to the other areas. Colonization events to those areas appear to have taken place recently given that the rest of the Iberian mountains do not harbor exclusive haplotypes. Iberian–Northern African distribution was inferred to be the result of long-distance dispersal because the split between Iberian and High Atlas haplotypes is estimated to have taken place in the last 4 million years ago when the Strait of Gibraltar was already open. Migrations from the Pyrenees to the south may have been favored by a corridor of predominant limestone rocks along Eastern Iberia, followed by successful overcoming the Strait of Gibraltar to reach northern Africa.     

Drivers of aboveground wood production in a lowland tropical forest of West Africa: teasing apart the roles of tree density,tree diversity,soil phosphorus,and historical logging

Tropical forests currently play a key role in regulating the terrestrial carbon cycle and abating climate change by storing carbon in wood. However, there remains considerable uncertainty as to whether tropical forests will continue to act as carbon sinks in the face of increased pressure from expanding human activities. Consequently, understanding what drives productivity in tropical forests is critical. We used permanent forest plot data from the Gola Rainforest National Park (Sierra Leone) – one of the largest tracts of intact tropical moist forest in West Africa – to explore how (1) stand basal area and tree diversity, (2) past disturbance associated with past logging, and (3) underlying soil nutrient gradients interact to determine rates of aboveground wood production (AWP). We started by statistically modeling the diameter growth of individual trees and used these models to estimate AWP for 142 permanent forest plots. We then used structural equation modeling to explore the direct and indirect pathways which shape rates of AWP. Across the plot network, stand basal area emerged as the strongest determinant of AWP, with densely packed stands exhibiting the fastest rates of AWP. In addition to stand packing density, both tree diversity and soil phosphorus content were also positively related to productivity. By contrast, historical logging activities negatively impacted AWP through the removal of large trees, which contributed disproportionately to productivity. Understanding what determines variation in wood production across tropical forest landscapes requires accounting for multiple interacting drivers – with stand structure, tree diversity, and soil nutrients all playing a key role. Importantly, our results also indicate that logging activities can have a long‐lasting impact on a forest's ability to sequester and store carbon, emphasizing the importance of safeguarding old‐growth tropical forests.     

Assessing high conservation value areas for rare,endemic and threatened (RET) species: A study in high altitude Changthang landscape of India

The Forest Stewardship Council developed the concept of High Conservation Values (HCVs) as a criteria in the forest certification process in order to promote sustainable forest management. It has six major components or values and component one and two of HCVs deal with the habitat for viable populations of “rare, endemic and threatened (RET) species” using the IUCN Red List category and other national / regional / local lists. But a consistent robust methodology for identification of these areas, does not exist. The present study tried to develop for the first time, a straight forward inclusive methodology for identification of HCVAs for the RET species on a spatio-temporal scale. A total of 50 RET and other significant species (32 flora, 10 fauna and 8 avifauna) were identified after a thorough literature review, field surveys and consultations with experts. Occurrence data of the selected species was collected from different secondary sources, field surveys, institutes and scientists who have worked on them. A 10 km grid-based approach and stratified random sampling was used for the primary GPS field surveys conducted during 2018–2019. MaxEnt species distribution model (SDM) software was used based on the occurrence data and environmental variables for identification of potential suitable habitats for the selected species. Linear support vector machine (LSVM) model was used for assessing the performance of the SDMs. The performance of each SDM has been validated through Cohen's Kappa (KAPPA), true skill statistic (TSS) and receiver operating characteristics (ROC) models. The proposed methodology addresses the urgent need for a holistic and robust set of techniques to apply the HCV toolkit. This is key to identify and map HCVAs for RET species at the landscape level and can be easily adapted to and adopted at the national, regional, state or local level in India. The methods offer an efficient, reliable approach for the application of the HCV concept, elsewhere in the world.