Spatial Distribution of Precise Suitability of Plantation: A Case Study of Main Coniferous Forests in Hubei Province, China

(1) Background. Conifers are the main plantation species in southern China, including Masson Pine (MP), Chinese fir (CF) and Chinese thuja (CT). Clarifying the suitable site conditions for these conifers is helpful for large-area afforestation, so as to manage forests to provide a higher level of ecosystem services. To achieve the research goals, we take the conifers in Hubei Province of southern China as a case study. (2) Methods. The situations of conifers, as well as environmental conditions of 448 sampling plots, were then investigated. The suitable growth environment of conifers in the studied area was determined by the maximum entropy algorithm, and the suitability spatial distribution of coniferous forests at the provincial level was also analyzed. (3) Results. The effect of the conifers suitability prediction model reached an accurate level, where AUC values of MP, CF and CT training set were 0.828, 0.856 and 0.970, respectively. Among multiple environmental factors, such as geography and climate, altitude is the most important factor affecting conifer growth. The contribution of altitude to the growth suitability of MP, CF and CT was 38.1%, 36.2% and 36.1%, respectively. Suitable areas of MP, CF and CT were 97,400 ha, 74,300 ha and 39,900 ha, accounting for 52.45%, 39.97% and 21.46% of the studied area, respectively. We concluded that the suitable site conditions of conifer plantations were 2800-5600 oC annual accumulated temperature, 40-1680 m a.s.l., and < 40° slopes. (4) Conclusions. The study suggests that accurate spatial suitability evaluation should be carried out to provide sufficient support for the large-area afforestation in southern China. However, due to our data and study area limitations, further studies are needed to explore the above findings for a full set of plantation species in an extensive area of southern China.

Reliability in Distribution Modeling—A Synthesis and Step-by-Step Guidelines for Improved Practice

Information about the distribution of a study object (e.g., species or habitat) is essential in face of increasing pressure from land or sea use, and climate change. Distribution models are instrumental for acquiring such information, but also encumbered by uncertainties caused by different sources of error, bias and inaccuracy that need to be dealt with. In this paper we identify the most common sources of uncertainties and link them to different phases in the modeling process. Our aim is to outline the implications of these uncertainties for the reliability of distribution models and to summarize the precautions needed to be taken. We performed a step-by-step assessment of errors, biases and inaccuracies related to the five main steps in a standard distribution modeling process: (1) ecological understanding, assumptions and problem formulation; (2) data collection and preparation; (3) choice of modeling method, model tuning and parameterization; (4) evaluation of models; and, finally, (5) implementation and use. Our synthesis highlights the need to consider the entire distribution modeling process when the reliability and applicability of the models are assessed. A key recommendation is to evaluate the model properly by use of a dataset that is collected independently of the training data. We support initiatives to establish international protocols and open geodatabases for distribution models.

Constraints of using historical data for modelling the spatial distribution of helminth parasites in ruminants

Dicrocoelium dendriticum is a trematode that infects ruminant livestock and requires two different intermediate hosts to complete its lifecycle. Modelling the spatial distribution of this parasite can help to improve its management in higher risk regions. The aim of this research was to assess the constraints of using historical data sets when modelling the spatial distribution of helminth parasites in ruminants. A parasitological data set provided by CREMOPAR (Napoli, Italy) and covering most of Italy was used in this paper. A baseline model (Random Forest, VECMAP^®) using the entire data set was first used to determine the minimal number of data points needed to build a stable model. Then, annual distribution models were computed and compared with the baseline model. The best prediction rate and statistical output were obtained for 2012 and the worst for 2016, even though the sample size of the former was significantly smaller than the latter. We discuss how this may be explained by the fact that in 2012, the samples were more evenly geographically distributed, whilst in 2016 most of the data were strongly clustered. It is concluded that the spatial distribution of the input data appears to be more important than the actual sample size when computing species distribution models. This is often a major issue when using historical data to develop spatial models. Such data sets often include sampling biases and large geographical gaps. If this bias is not corrected, the spatial distribution model outputs may display the sampling effort rather than the real species distribution.

Ecosystem services show variable responses to future climate conditions in the Colombian páramos

Background

The páramos, the high-elevation ecosystems of the northern Andes, are well-known for their high species richness and provide a variety of ecosystem services to local subsistence-based communities and regional urbanizations. Climate change is expected to negatively affect the provision of these services, but the level of this impact is still unclear. Here we assess future climate change impact on the ecosystem services provided by the critically important páramos of the department of Boyacá in Colombia, of which over 25% of its territory is páramo.

Methods

We first performed an extensive literature review to identify useful species of Boyacá, and selected 103 key plant species that, based on their uses, support the provision of ecosystem services in the páramos. We collated occurrence information for each key species and using a Mahalanobis distance approach we applied climate niche modelling for current and future conditions.

Results

We show an overall tendency of reduction in area for all ecosystem services under future climate conditions (mostly a loss of 10% but reaching up to a loss of 40%), but we observe also increases, and responses differ in intensity loss. Services such as Food for animals, Material and Medicinal, show a high range of changes that includes both positive and negative outcomes, while for Food for humans the responses are mostly substantially negative. Responses are less extreme than those projected for individual species but are often complex because a given ecosystem service is provided by several species. As the level of functional or ecological redundancy between species is not yet known, there is an urgency to expand our knowledge on páramos ecosystem services for more species. Our results are crucial for decision-makers, social and conservation organizations to support sustainable strategies to monitor and mitigate the potential consequences of climate change for human livelihoods in mountainous settings.

The Potential Distribution of Tree Species in Three Periods of Time under a Climate Change Scenario

Species distribution models have become some of the most important tools for assessment of impact of climatic change, impact of human activity and for the detection of failure in silvicultural or conservation management plans. In this study, we modeled the potential distribution of 13 tree species of temperate forests distributed in the Mexican state Durango in the Sierra Madre Occidental, for three periods of time. Models were constructed for each period of time using 19 climate variables from the MaxEnt (Maximum Entropy algorithm) modelling algorithm. Those constructed for the future used a severe climate change scenario. When comparing the potential areas of the periods, some species such as Pinus durangensis (Martínez), Pinus teocote (Schiede ex Schltdl. & Cham.) and Quercus crassifolia (Bonpl.) showed no drastic changes. Rather, the models projected a slight reduction, displacement or fragmentation in the potential area of Pinus arizonica (Engelm.), P. cembroides (Zucc), P. engelmanni (Carr), P. leiophylla (Schl), Quercus arizonica (Sarg), Q. magnolifolia (Née) and Q. sideroxila (Humb. & Bonpl.) in the future period. Thus, establishing conservation and reforestation strategies in the medium and long term could guarantee a wide distribution of these species in the future.

The role of niche divergence and geographic arrangement in the speciation of Eared Pheasants (Crossoptilon, Hodgson 1938)

One of the most contentious theories in current ecology is the ecological niche conservatism, which is defined as conservatism among closely related species; however, the ecological niche can also be shifted, as documented in several cases. Genetic drift and ecological divergent selection may cause ecological niche divergence. The current study aims to test whether the ecological niche is conserved or divergent and to determine the main factor that drives ecological niche divergence or conservation. We analyzed the phylogenetic relationship, ecological niche model (ENM) and demographic history of Eared Pheasants in the genus Crossoptilon (Galliformes: Phasianidae) to test niche conservatism with respect to different geographically distributed patterns. The phylogenetic relationship was reconstructed using ∗BEAST with mitochondrial cytochrome b (cyt b) and 44 unlinked autosomal exonic loci, and ENMs were reconstructed in MAXENT using an average of 41 occurrence sites in each species and 22 bioclimatic variables. A background similarity test was used to detect whether the ecological niche is conserved. Demographic history was estimated using the isolation with migration (IM) model. We found that there was asymmetric gene flow between the allopatric sister species Crossoptilon mantchuricum and C. auritum and the parapatric sister species C. harmani and C. crossoptilon. We found that ecological niches were divergent, not conserved, between C. mantchuricum and C. auritum, which began to diverge at approximately 0.3 million years ago. However, the ecological niches were conserved between C. crossoptilon and C. harmani, which gradually diverged approximately half a million years ago. Ecological niches can be either conserved or divergent, and ecological divergent selection for local adaptation is probably an important factor that promotes and maintains niche divergence in the face of gene flow. This study provides a better understanding of the role that divergent selection has in the initial speciation process. The platform combined demographic processes and ecological niches to offer new insights into the mechanism of biogeography patterns.

Predicting the Potential Distribution of Polygala tenuifolia Willd. under Climate Change in China

Global warming has created opportunities and challenges for the survival and development of species. Determining how climate change may impact multiple ecosystem levels and lead to various species adaptations is necessary for both biodiversity conservation and sustainable biological resource utilization. In this study, we employed Maxent to predict changes in the habitat range and altitude of Polygala tenuifolia Willd. under current and future climate scenarios in China. Four representative concentration pathways (RCP2.6, RCP4.5, RCP6.0, and RCP8.5) were modeled for two time periods (2050 and 2070). The model inputs included 732 presence points and nine sets of environmental variables under the current conditions and the four RCPs in 2050 and 2070. The area under the receiver-operating characteristic (ROC) curve (AUC) was used to evaluate model performance. All of the AUCs were greater than 0.80, thereby placing these models in the "very good" category. Using a jackknife analysis, the precipitation in the warmest quarter, annual mean temperature, and altitude were found to be the top three variables that affect the range of P. tenuifolia. Additionally, we found that the predicted highly suitable habitat was in reasonable agreement with its actual distribution. Furthermore, the highly suitable habitat area was slowly reduced over time.

Real-time estimation of wildfire perimeters from curated crowdsourcing

Real-time information about the spatial extents of evolving natural disasters, such as wildfire or flood perimeters, can assist both emergency responders and the general public during an emergency. However, authoritative information sources can suffer from bottlenecks and delays, while user-generated social media data usually lacks the necessary structure and trustworthiness for reliable automated processing. This paper describes and evaluates an automated technique for real-time tracking of wildfire perimeters based on publicly available “curated” crowdsourced data about telephone calls to the emergency services. Our technique is based on established data mining tools, and can be adjusted using a small number of intuitive parameters. Experiments using data from the devastating Black Saturday wildfires (2009) in Victoria, Australia, demonstrate the potential for the technique to detect and track wildfire perimeters automatically, in real time, and with moderate accuracy. Accuracy can be further increased through combination with other authoritative demographic and environmental information, such as population density and dynamic wind fields. These results are also independently validated against data from the more recent 2014 Mickleham-Dalrymple wildfires.

Marble Algorithm: a solution to estimating ecological niches from presence-only records

We describe an algorithm that helps to predict potential distributional areas for species using presence-only records. The Marble Algorithm is a density-based clustering program based on Hutchinson's concept of ecological niches as multidimensional hypervolumes in environmental space. The algorithm characterizes this niche space using the density-based spatial clustering of applications with noise (DBSCAN) algorithm. When MA is provided with a set of occurrence points in environmental space, the algorithm determines two parameters that allow the points to be grouped into several clusters. These clusters are used as reference sets describing the ecological niche, which can then be mapped onto geographic space and used as the potential distribution of the species. We used both virtual species and ten empirical datasets to compare MA with other distribution-modeling tools, including Bioclimate Analysis and Prediction System, Environmental Niche Factor Analysis, the Genetic Algorithm for Rule-set Production, Maximum Entropy Modeling, Artificial Neural Networks, Climate Space Models, Classification Tree Analysis, Generalised Additive Models, Generalised Boosted Models, Generalised Linear Models, Multivariate Adaptive Regression Splines and Random Forests. Results indicate that MA predicts potential distributional areas with high accuracy, moderate robustness, and above-average transferability on all datasets, particularly when dealing with small numbers of occurrences.

Combined ecological risks of nitrogen and phosphorus in European freshwaters

Eutrophication is a key water quality issue triggered by increasing nitrogen (N) and phosphorus (P) levels and potentially posing risks to freshwater biota. We predicted the probability that an invertebrate species within a community assemblage becomes absent due to nutrient stress as the ecological risk (ER) for European lakes and streams subjected to N and P pollution from 1985 to 2011. The ER was calculated as a function of species-specific tolerances to NO3(-) and total P concentrations and water quality monitoring data. Lake and stream ER averaged 50% in the last monitored year (i.e. 2011) and we observed a decrease by 22% and 38% in lake and stream ER (respectively) of river basins since 1985. Additionally, the ER from N stress surpassed that of P in both freshwater systems. The ER can be applied to identify river basins most subjected to eutrophication risks and the main drivers of impacts.

Nowhere to invade: Rumex crispus and Typha latifolia projected to disappear under future climate scenarios

Future climate change has been predicted to affect the potential distribution of plant species. However, only few studies have addressed how invasive species may respond to future climate change despite the known effects of plant species invasion on nutrient cycles, ecosystem functions, and agricultural yields. In this study, we predicted the potential distributions of two invasive species, Rumex crispus and Typha latifolia, under current and future (2050) climatic conditions. Future climate scenarios considered in our study include A1B, A2, A2A, B1, and B2A. We found that these two species will lose their habitat under the A1B, A2, A2A, and B1 scenarios. Their distributions will be maintained under future climatic conditions related to B2A scenarios, but the total area will be less than 10% of that under the current climatic condition. We also investigated variations of the most influential climatic variables that are likely to cause habitat loss of the two species. Our results demonstrate that rising mean annual temperature, variations of the coldest quarter, and precipitation of the coldest quarter are the main factors contributing to habitat loss of R. crispus. For T. latifolia, the main factors are rising mean annual temperature, variations in temperature of the coldest quarter, mean annual precipitation, and precipitation of the coldest quarter. These results demonstrate that the warmer and wetter climatic conditions of the coldest season (or month) will be mainly responsible for habitat loss of R. crispus and T. latifolia in the future. We also discuss uncertainties related to our study (and similar studies) and suggest that particular attention should be directed toward the manner in which invasive species cope with rapid climate changes because evolutionary change can be rapid for species that invade new areas.

Modeling microbial community structure and functional diversity across time and space

Microbial communities exhibit exquisitely complex structure. Many aspects of this complexity, from the number of species to the total number of interactions, are currently very difficult to examine directly. However, extraordinary efforts are being made to make these systems accessible to scientific investigation. While recent advances in high-throughput sequencing technologies have improved accessibility to the taxonomic and functional diversity of complex communities, monitoring the dynamics of these systems over time and space - using appropriate experimental design - is still expensive. Fortunately, modeling can be used as a lens to focus low-resolution observations of community dynamics to enable mathematical abstractions of functional and taxonomic dynamics across space and time. Here, we review the approaches for modeling bacterial diversity at both the very large and the very small scales at which microbial systems interact with their environments. We show that modeling can help to connect biogeochemical processes to specific microbial metabolic pathways.

Modeling microbial communities: current, developing, and future technologies for predicting microbial community interaction

Never has there been a greater opportunity for investigating microbial communities. Not only are the profound effects of microbial ecology on every aspect of Earth's geochemical cycles beginning to be understood, but also the analytical and computational tools for investigating microbial Earth are undergoing a rapid revolution. This environmental microbial interactome, the system of interactions between the microbiome and the environment, has shaped the planet's past and will undoubtedly continue to do so in the future. We review recent approaches for modeling microbial community structures and the interactions of microbial populations with their environments. Different modeling approaches consider the environmental microbial interactome from different aspects, and each provides insights to different facets of microbial ecology. We discuss the challenges and opportunities for the future of microbial modeling and describe recent advances in microbial community modeling that are extending current descriptive technologies into a predictive science.

Livestock Helminths in a Changing Climate: Approaches and Restrictions to Meaningful Predictions

Climate change is a driving force for livestock parasite risk. This is especially true for helminths including the nematodes Haemonchus contortus, Teladorsagia circumcincta, Nematodirus battus, and the trematode Fasciola hepatica, since survival and development of free-living stages is chiefly affected by temperature and moisture. The paucity of long term predictions of helminth risk under climate change has driven us to explore optimal modelling approaches and identify current bottlenecks to generating meaningful predictions. We classify approaches as correlative or mechanistic, exploring their strengths and limitations. Climate is one aspect of a complex system and, at the farm level, husbandry has a dominant influence on helminth transmission. Continuing environmental change will necessitate the adoption of mitigation and adaptation strategies in husbandry. Long term predictive models need to have the architecture to incorporate these changes. Ultimately, an optimal modelling approach is likely to combine mechanistic processes and physiological thresholds with correlative bioclimatic modelling, incorporating changes in livestock husbandry and disease control. Irrespective of approach, the principal limitation to parasite predictions is the availability of active surveillance data and empirical data on physiological responses to climate variables. By combining improved empirical data and refined models with a broad view of the livestock system, robust projections of helminth risk can be developed.

Presence-only data and the em algorithm

SUMMARY

In ecological modeling of the habitat of a species, it can be prohibitively expensive to determine species absence. Presence-only data consist of a sample of locations with observed presences and a separate group of locations sampled from the full landscape, with unknown presences. We propose an expectation-maximization algorithm to estimate the underlying presence-absence logistic model for presence-only data. This algorithm can be used with any off-the-shelf logistic model. For models with stepwise fitting procedures, such as boosted trees, the fitting process can be accelerated by interleaving expectation steps within the procedure. Preliminary analyses based on sampling from presence-absence records of fish in New Zealand rivers illustrate that this new procedure can reduce both deviance and the shrinkage of marginal effect estimates that occur in the naive model often used in practice. Finally, it is shown that the population prevalence of a species is only identifiable when there is some unrealistic constraint on the structure of the logistic model. In practice, it is strongly recommended that an estimate of population prevalence be provided.

Assisting Australian indigenous resource management and sustainable utilization of species through the use of GIS and environmental modeling techniques

Information on distribution and relative abundance of species is integral to sustainable management, especially if they are to be harvested for subsistence or commerce. In northern Australia, natural landscapes are vast, centers of population few, access is difficult, and Aboriginal resource centers and communities have limited funds and infrastructure. Consequently defining distribution and relative abundance by comprehensive ground survey is difficult and expensive. This highlights the need for simple, cheap, automated methodologies to predict the distribution of species in use, or having potential for use, in commercial enterprise. The technique applied here uses a Geographic Information System (GIS) to make predictions of probability of occurrence using an inductive modeling technique based on Bayes' theorem. The study area is in the Maningrida region, central Arnhem Land, in the Northern Territory, Australia. The species examined, Cycas arnhemica and Brachychiton diversifolius, are currently being 'wild harvested' in commercial trials, involving sale of decorative plants and use as carving wood, respectively. This study involved limited and relatively simple ground surveys requiring approximately 7 days of effort for each species. The overall model performance was evaluated using Cohen's kappa statistics. The predictive ability of the model for C. arnhemica was classified as moderate and for B. diversifolius as fair. The difference in model performance can be attributed to the pattern of distribution of these species. C. arnhemica tends to occur in a clumped distribution due to relatively short distance dispersal of its large seeds and vegetative growth from long-lived rhizomes, while B. diversifolius seeds are smaller and more widely dispersed across the landscape. The output from analysis predicts trends in species distribution that are consistent with independent on-site sampling for each species and therefore should prove useful in gauging the extent of resource availability. However, some caution needs to be applied as the models tend to over predict presence which is a function of distribution patterns and of other variables operating in the landscape such as fire histories which were not included in the model due to limited availability of data.

The zoophilic fruitfly Phortica variegata: morphology, ecology and biological niche

Flies belonging to the subfamily Steganinae (Drosophilidae) display unusual zoophilic feeding habits at the adult and/or larval stage. Phortica variegata (Fallén) feeds on tears or eye liquid around the eyes of humans and carnivores. When feeding it is a potential vector of Thelazia callipaeda (Railliet and Henry) eyeworms. Adult and larval stages of this fly may be easily confused with other species belonging to the same genus, and little is known on the biology and ecology of P. variegata. In April-November 2005, a total of 969 P. variegata were collected in an area with a high prevalence of canine thelaziosis. The number of flies collected weekly was then related to climatic and environmental parameters (e.g. temperature, relative humidity and total rainfall) recorded daily at the collection site. The highest number of Phortica were collected during July-August. The sex ratio (number of males : females) rose from approximately 0.5 during May-July, to approximately 3.0 in August and 181 during September-October. Distributional data, representing 242 sites at which P. variegata has been collected in Europe, were analysed using a desktop implementation of the genetic algorithm for rule-set prediction (GARP) to model ecological requirements across Europe, as well as in Italy. P. variegata is shown to be mainly active at 20-25 degrees C and 50-75% RH. The ecological niche model suggests with a high degree of confidence that large areas of Europe are likely to represent suitable habitat for this species, mostly concentrated in central Europe. The results reported here contribute basic knowledge on the ecology and geographical distribution of P. variegata flies, which will be fundamental to gaining a better understanding of their role as vectors of human and animal pathogens.

GEOGRAPHIC POTENTIAL FOR OUTBREAKS OF MARBURG HEMORRHAGIC FEVER

Marburg virus represents one of the least well-known of the hemorrhagic fever-causing viruses worldwide; in particular, its geographic potential in Africa remains quite mysterious. Ecologic niche modeling was used to explore the geographic and ecologic potential of Marburg virus in Africa. Model results permitted a reinterpretation of the geographic point of infection in the initiation of the 1975 cases in Zimbabwe, and also anticipated the potential for cases in Angola, where a large outbreak recently (2004-2005) occurred. The geographic potential for additional outbreaks is outlined, including in several countries in which the virus is not known. Overall, results demonstrate that ecologic niche modeling can be a powerful tool in understanding geographic distributions of species and other biologic phenomena such as zoonotic disease transmission from natural reservoir populations.