Ensemble Forecasting Approach Research Articles

High-speed railway express delivery volume forecast based on data-driven ensemble forecast approaches: The China case

Current researches on logistics delivery volume forecast mainly focus on traditional transportation modes such as road, railway, and aviation, with little research on predicting high-speed railway express delivery (HSRED) volume. In this paper, a data-driven ensemble forecast approach is proposed to forecast the HSRED. Firstly, we use four kinds of single methods to predicate the HSRED, include Auto-Regressive Moving Average Model (ARMA), Linear Regression (LR), Random Forest (RF) and Singular Spectrum Analysis (SSA). Secondly, based on the predication results of single methods, we apply two ensemble methods including Arithmetic Mean (AM) and Allocation Integration Operator (AIO) to determine 22 combinations of ensemble forecast approaches. The Mean Absolute Deviation (MAD), Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE) and Correlation Coefficient (CC) are used to evaluate the performance of each single and ensemble approach. Next, a real-world case study is conducted by using the HSRED in China as the background, the delivery demand data from Jan. 2014 to Mar. 2023 is used as the input. The results show that: The prediction results based on SSA, ARMA, and RF are close to the actual data. LR has the worst predication performance. The overall performance indicators of the ensemble models are better than those of the single prediction models. The ensemble models by using AIO are generally better than those by using AM. SSA-ARMA ensemble model constructed by AIO shows the best predictive performance. The trend of HSRED changes from 2025 to 2050 is gentle, and there will be no explosive growth, and it will gradually decrease and eventually stabilize. Finally, we distribute the predicted results to each province based on the gravity model and conduct visual analysis. The results show that: Provinces with high demand for HSRED include Shandong, Zhejiang, Jiangsu, Shanghai, and Beijing, mostly concentrated in the eastern and southeastern regions, we can consider strengthening the development and increasing investment of HSRED in these cities. Provinces with less demand, efforts can be made to strengthen publicity, accelerate the formation process of the high-speed railway network, and increase support for the HSRED industry.

Multiagency Ensemble Forecast of Wildfire Air Quality in the United States: Toward Community Consensus of Early Warning

Abstract Wildfires pose increasing risks to human health and properties in North America. Due to large uncertainties in fire emission, transport, and chemical transformation, it remains challenging to accurately predict air quality during wildfire events, hindering our collective capability to issue effective early warnings to protect public health and welfare. Here, we present a new real-time Hazardous Air Quality Ensemble System (HAQES) by leveraging various wildfire smoke forecasts from three U.S. federal agencies (NOAA, NASA, and Navy). Compared to individual models, the HAQES ensemble forecast significantly enhances forecast accuracy. To further enhance forecasting performance, a weighted ensemble forecast approach was introduced and tested. Compared to the unweighted ensemble mean, the multilinear regression weighted ensemble reduced fractional bias by 34% in the major fire regions, false alarm rate by 72%, and increased hit rate by 17%. Finally, we improved the weighted ensemble using quantile regression and weighted regression methods to enhance the forecast of extreme air quality events. The advanced weighted ensemble increased the PM2.5 exceedance hit rate by 55% compared to the ensemble mean. Our findings provide insights into the development of advanced ensemble forecast methods for wildfire air quality, offering a practical way to enhance decision-making support to protect public health.

Modelling 21st century refugia and impact of climate change on Amazonia's largest primates

Edaphic and vegetation conditions can render climatically suitable sites inadequate for a species to persist, constraining the amount of suitable habitat and the possibilities of tracking preferred climatic conditions as they shift in response to climate change. We combined climatic and remotely sensed data to model current and future distributions of nine extant taxa of ateline primates across the Amazon basin. We used the models to identify and quantify potential range changes and refugia of suitable habitat from the present to the latter half of the 21st century. We applied an ensemble forecasting approach for species distribution models using 596 spatially rarefied occurrences. We parameterised these models combining reflectance data from a basin‐wide Landsat TM/ETM+ image composite, and three sets of bioclimatic layers containing data for the current time period, and two different (moderate and worst‐case) climate change scenarios for 2041–2070. Eight out of nine taxa are likely to experience pronounced range losses, with seven of them predicted to lose over 50% of their currently suitable habitats irrespective of climate change scenarios. Modelled ateline richness exhibited a broad decrease in high‐richness areas, and a possible redistribution along the northernmost parts of western Amazonia. Refugia from 21st century climate change for the whole complex were mostly concentrated in western Amazonia, especially in its southern part. We identified hotspots of vulnerability to climate change and 21st century refugia for all Amazonian atelines while accounting for habitat characteristics that are important to guarantee the continued existence of suitable habitats for these strictly arboreal taxa. Increasing the understanding of climate change impacts on Amazonia's largest primates can help to inform spatial conservation planning decisions and management to sustain forest‐dwelling biodiversity over large areas such as Amazonia.

Socioeconomic impact of agricultural water reallocation policies in the Upper Litani Basin (Lebanon): a remote sensing and microeconomic ensemble forecasting approach

The integration of remote sensing and socio-economic data is crucial for policy making in regions suffering from water scarcity and climate change. We present a framework that combines remotely sensed estimates of biomass and water use from FAO’s Water Productivity Open-access portal (WaPOR) (V2) with an ensemble of calibrated mathematical programming models to assess the impact of water reallocations on rain-fed and irrigated land use and production in water-stressed basins. We evaluated our model in the Upper Litani Basin of Lebanon using two water reallocation policies: (i) quotas that progressively reduces water allocation for irrigation up to 100%, at 1% intervals and (ii) a pricing policy that increases the agricultural water price up to 0.25 USD/m3, at 0.0025 USD/m3 intervals. Our results reveal that 1) the impacts of water availability reductions due to quotas on profit and employment are less-than-proportional when water allocation is reduced by <50%, but abruptly increase when water allocation is reduced by >50%; 2) irrigators responses to prices are inelastic until a price increase of 0.03 USD/m3, become significantly more elastic afterwards, and again become inelastic above a price increase of 0.06 USD/m3; 3) higher water prices progressively reduce profit and labor, significantly reduce water use over the elastic interval (where irrigators cultivating low value-added crops shift to rainfed agriculture), and increase tariff revenue during the inelastic interval (where irrigators largely stick to their crop portfolios and pay the higher water prices). Our results reveal nontrivial uncertainties and tipping points, thus highlighting the value of combining reliable water use data with multi-model and multi-scenario ensembles in informing robust policies.

Conservation Challenges Imposed by Evolutionary History and Habitat Suitability Shifts of Endangered Freshwater Mussels under a Global Climate Change Scenario

Climate change and associated shifts in temperature and precipitation patterns have become an increasing concern as drivers of ongoing biodiversity loss. The Mediterranean region is particularly vulnerable, being both a biodiversity hotspot and a region very prone to desertification. Freshwater mussels are amongst the most threatened invertebrate taxa worldwide. Unio tumidiformis is an endemic and endangered species restricted to the southern Iberian Peninsula, living in temporary Mediterranean-type streams. Freshwater mussels need a fish host for successful larval transformation, meaning U. tumidiformis must belong to the genus Squalius. The main objective of this study was to evaluate the vulnerability of U. tumidiformis to climate change, by studying its population genetics and evolutionary history, its current and future habitat suitability, and that of its hosts. Genetic population structure and diversity were assessed using Single-Nucleotide Polymorphisms through Genotyping by Sequencing and used to infer species evolutionary history. The species potential distribution was modeled using an ensemble forecasting approach, and future shifts in habitat suitability were assessed with the projected climate data layers from Worldclim. Most populations showed extreme genetic differentiation (Fst up to 0.745), even from close neighboring ones. Upper Guadiana populations were more diverse and less differentiated. We hypothesize that U. tumidiformis originated in Upper Guadiana and followed the same colonization routes as their hosts with numerous founder effects and bottlenecks. Our results also predicted a reduction of 99% of climatically suitable areas for U. tumidiformis in the Iberian Peninsula until 2040. For the fish hosts, a maximum 42% reduction in suitable areas was estimated throughout the century, with remaining adequate habitats in the north. Our results suggest that difficult conservation options are necessary, prioritizing the preservation of populations, translocations to the northern area of its historical range and stream engineering to increase resilience to droughts.

Earthquake forecasting from paleoseismic records

Forecasting large earthquakes along active faults is of critical importance for seismic hazard assessment. Statistical models of recurrence intervals based on compilations of paleoseismic data provide a forecasting tool. Here we compare five models and use Bayesian model-averaging to produce time-dependent, probabilistic forecasts of large earthquakes along 93 fault segments worldwide. This approach allows better use of the measurement errors associated with paleoseismic records and accounts for the uncertainty around model choice. Our results indicate that although the majority of fault segments (65/93) in the catalogue favour a single best model, 28 benefit from a model-averaging approach. We provide earthquake rupture probabilities for the next 50 years and forecast the occurrence times of the next rupture for all the fault segments. Our findings suggest that there is no universal model for large earthquake recurrence, and an ensemble forecasting approach is desirable when dealing with paleoseismic records with few data points and large measurement errors.

Climate-driven loss of taxonomic and functional richness in Brazilian Atlantic Forest anurans

Climate change is one of the key drivers of the dramatic erosion of global biodiversity. Poikilothermic organisms, such as amphibians, are particularly prone to alterations in temperature and precipitation and are therefore expected to be severely impacted by global climate change. We evaluated: i) the potential effects of climate change on taxonomic and functional richness of amphibians in the Atlantic Forest of Brazil, and ii) tested whether forest-dependent and open area species responded differently to climatic stressors. We applied an ensemble forecasting approach using point locality data to establish predictive maps of taxonomic and functional richness for different IPCC AR5 scenarios with two Representative Concentration Pathway trajectories (RCP 4.5 and RCP 8.5) at two temporal projections (2050 and 2070). A significant decline in both taxonomic and functional richness of Atlantic Forest amphibians is expected over the coming decades under the evaluated climate scenarios. The core areas projected to maintain significantly high anuran richness in both components were in coastal high-altitude localities. We found negative effects (losses) to be more pronounced for taxonomic richness. Model outcomes indicate significant losses in both forest and open area species. A few remaining high-altitude sites will then play an important role as potential climatic refuges to safeguard anuran diversity, requiring a dynamic landscape approach as conservation strategy.

Climate change may cause the extinction of the butterfly Lasiommata petropolitana in the Apennines

Climate change represents a threat to narrow-ranged mountain species living in low-altitude massifs. We studied the disjunct Apennine population of Lasiommata petropolitana (Lepidoptera, Nymphalidae) in the Gran Sasso and Monti della Laga National Park. We quantified the altitudinal shifts undergone in the last decades (1964–2021) in the Alps and Apennines and estimated the local extinction risk due to climate change. We also sequenced the COI mitochondrial marker of seven Apennine specimens, comparing them with those available across the Palearctic. We projected the probability of presence for the species under a future climatic scenario using an ensemble forecasting approach. We found that, despite geographical isolation, the Apennine population of L. petropolitana displays a single widespread COI haplotype also occurring in most European populations. In the Alps and Apennines, this species has shifted uphill an average of 6.3 m per year since 1964. Accordingly, our model predicted a likely extinction in the Apennines by about 2060, due to a reduction of the climatic suitability in this region.Implications for insect conservationImplications for insect conservation Despite its potential loss in the Apennines would not erode mitochondrial diversity, L. petropolitana characterises the butterfly community of the Gran Sasso massif as an alpine enclave. The loss of the Apennine population, together with those of other orophilous butterflies, could trigger a homogenization of alpha and beta diversity and induce a loss of functional diversity in the impoverished high-altitude biotas. As habitat heterogeneity is a key aspect for populations to endure climate change, the maintenance of varied microhabitats, mainly through grazing management, could address the decline of this population.

Predicting current and future distribution of Hovenia dulcis Thunb. (Rhamnaceae) worldwide

Biological invasions are increasingly recognized as one of the major threats to biodiversity. The Japanese raisin tree (Hovenia dulcis) is native to East Asia, however, in southeastern South America this species has become one of the most pervasive invaders. Hovenia dulcis has many biological characteristics that favor the process of invasion and few studies have indicated changes in the structure and composition of native plant communities where this species has become invader. Given the invasiveness shown in southeastern South America, our main goal was to identify the potentially suitable habitats for this invasive species at a global scale. In this sense, we modeled the potential distribution of H. dulcis along the terrestrial areas worldwide using an ensemble forecasting approach. Additionally, the percentage of overlapping biodiversity hotspot areas with the currently suitable areas for this species was calculated. Our results revealed that the current potential H. dulcis range is equivalent to 7.88% (12,719,365 km2) of the terrestrial area worldwide. For the future scenarios of climate change, the potential distribution area tends to have a small reduction. However, significant suitable areas were identified for H. dulcis range in the northern limits of the boreal distribution. Currently, around 17% of biodiversity hotspot areas overlap with the suitable areas for H. dulcis occurrence. In summary, given that the prevention is well-recognized as a more effective management action against invasive alien species, it is essential to implement policies to prevent H. dulcis introduction in suitable areas worldwide, as well as local population control, especially in biodiversity hotspots.

Ensemble streamflow forecasting over a cascade reservoir catchment with integrated hydrometeorological modeling and machine learning

Abstract. A popular way to forecast streamflow is to use bias-corrected meteorological forecasts to drive a calibrated hydrological model, but these hydrometeorological approaches suffer from deficiencies over small catchments due to uncertainty in meteorological forecasts and errors from hydrological models, especially over catchments that are regulated by dams and reservoirs. For a cascade reservoir catchment, the discharge from the upstream reservoir contributes to an important part of the streamflow over the downstream areas, which makes it tremendously hard to explore the added value of meteorological forecasts. Here, we integrate meteorological forecasts, land surface hydrological model simulations and machine learning to forecast hourly streamflow over the Yantan catchment, where the streamflow is influenced by both the upstream reservoir water release and the rainfall–runoff processes within the catchment. Evaluation of the hourly streamflow hindcasts during the rainy seasons of 2013–2017 shows that the hydrometeorological ensemble forecast approach reduces probabilistic and deterministic forecast errors by 6 % compared with the traditional ensemble streamflow prediction (ESP) approach during the first 7 d. The deterministic forecast error can be further reduced by 6 % in the first 72 h when combining the hydrometeorological forecasts with the long short-term memory (LSTM) deep learning method. However, the forecast skill for LSTM using only historical observations drops sharply after the first 24 h. This study implies the potential of improving flood forecasts over a cascade reservoir catchment by integrating meteorological forecasts, hydrological modeling and machine learning.

Improving Operational Short- to Medium-Range (SR2MR) Streamflow Forecasts in the Upper Zambezi Basin and Its Sub-Basins Using Variational Ensemble Forecasting

The combination of Hydrological Models and high-resolution Satellite Precipitation Products (SPPs) or regional Climatological Models (RCMs), has provided the means to establish baselines for the quantification, propagation, and reduction in hydrological uncertainty when generating streamflow forecasts. This study aimed to improve operational real-time streamflow forecasts for the Upper Zambezi River Basin (UZRB), in Africa, utilizing the novel Variational Ensemble Forecasting (VEF) approach. In this regard, we describe and discuss the main steps required to implement, calibrate, and validate an operational hydrologic forecasting system (HFS) using VEF and Hydrologic Processing Strategies (HPS). The operational HFS was constructed to monitor daily streamflow and forecast them up to eight days in the future. The forecasting process called short- to medium-range (SR2MR) streamflow forecasting was implemented using real-time rainfall data from three Satellite Precipitation Products or SPPs (The real-time TRMM Multisatellite Precipitation Analysis TMPA-RT, the NOAA CPC Morphing Technique CMORPH, and the Precipitation Estimation from Remotely Sensed data using Artificial Neural Networks, PERSIANN) and rainfall forecasts from the Global Forecasting System (GFS). The hydrologic preprocessing (HPR) strategy considered using all raw and bias corrected rainfall estimates to calibrate three distributed hydrological models (HYMOD_DS, HBV_DS, and VIC 4.2.b). The hydrologic processing (HP) strategy considered using all optimal parameter sets estimated during the calibration process to increase the number of ensembles available for operational forecasting. Finally, inference-based approaches were evaluated during the application of a hydrological postprocessing (HPP) strategy. The final evaluation and reduction in uncertainty from multiple sources, i.e., multiple precipitation products, hydrologic models, and optimal parameter sets, was significantly achieved through a fully operational implementation of VEF combined with several HPS. Finally, the main challenges and opportunities associated with operational SR2MR streamflow forecasting using VEF are evaluated and discussed.

A new integrated methodology for characterizing and assessing suitable areas for viticulture: A case study in Northwest Spain

In 2018, Spain was the third largest producer and the leading exporter of wine in the world. Viticulture is an important economic activity in the Castilla y León region, and is an element capable of halting the flight from the countryside and favouring demographic settlement in rural areas. A new integrated methodology for characterizing and delimitating areas suitable for vineyard cultivation is proposed here. The approach combines traditional climate indices with others used in bioclimatology and with soil variables, and applies various statistical analyses to select the predictors that best characterize the vineyards. These predictors are then integrated in species distribution models and a geographic information system. The methodology was tested in the Denomination of Origin León (hereafter DO León) in northwest Spain. Ten single models using the maximum entropy modelling algorithm were run for each of the six wine-grape varieties. Single-model projections built as a consensus of the ten models into an ensemble-forecasting approach were later used to generate maps of suitable areas for each variety.The results confirm the delimitation of the DO León as a Denomination of Origin. The bioclimatic variables Compensated Thermicity Index and Continentality Index and the soil variables pH, Clay Content, Soil Retention Capacity and Soil Saturation Humidity are defining for all the varieties studied. Garnacha and Mencía were the most different varieties in relation to their bioclimatic and soil requirements. Suitability maps revealed that the DO could be extended into neighbouring areas up to 30 km around it.The proposed methodology is a useful tool for agronomic and oenological management; it allows a more effective selection of sites for new vineyards, improves vineyard management, and can even be used to protect territories with a historical and cultural heritage of grape cultivation, thus favouring demographic settlement in rural areas and avoiding depopulation.

Generating Ensemble Streamflow Forecasts: A Review of Methods and Approaches Over the Past 40 Years

AbstractEnsemble forecasting applied to the field of hydrology is currently an established area of research embracing a broad spectrum of operational situations. This work catalogs the various pathways of ensemble streamflow forecasting based on an exhaustive review of more than 700 studies over the last 40 years. We focus on the advanced state of the art in the model‐based (dynamical) ensemble forecasting approaches. Ensemble streamflow prediction systems are categorized into three leading classes: statistics‐based streamflow prediction systems, climatology‐based ensemble streamflow prediction systems and numerical weather prediction‐based hydrological ensemble prediction systems. For each ensemble approach, technical information, as well as details about its strengths and weaknesses, are provided based on a critical review of the studies listed. Through this literature review, the performance and uncertainty associated with the ensemble forecasting systems are underlined from both operational and scientific viewpoints. Finally, the remaining key challenges and prospective future research directions are presented, notably through hybrid dynamical ‐ statistical learning approaches, which obviously present new challenges to be overcome in order to allow the successful employment of ensemble streamflow forecasting systems in the next decades. Targeting students, researchers and practitioners, this review provides a detailed perspective on the major features of an increasingly important area of hydrological forecasting.

Predicting range shifts of African apes under global change scenarios

AbstractAimModelling African great ape distribution has until now focused on current or past conditions, while future scenarios remain scarcely explored. Using an ensemble forecasting approach, we predicted changes in taxon‐specific distribution under future scenarios of climate, land use and human populations for (1) areas outside protected areas (PAs) only (assuming complete management effectiveness of PAs), (2) the entire study region and (3) interspecies range overlap.LocationTropical Africa.MethodsWe compiled occurrence data (n = 5,203) on African apes from the IUCN A.P.E.S. database and extracted relevant climate‐, habitat‐ and human‐related predictors representing current and future (2050) conditions to predict taxon‐specific range change under a best‐ and a worst‐case scenario, using ensemble forecasting.ResultsThe predictive performance of the models varied across taxa. Synergistic interactions between predictors are shaping African ape distribution, particularly human‐related variables. On average across taxa, a range decline of 50% is expected outside PAs under the best scenario if no dispersal occurs (61% in worst scenario). Otherwise, an 85% range reduction is predicted to occur across study regions (94% worst). However, range gains are predicted outside PAs if dispersal occurs (52% best, 21% worst), with a slight increase in gains expected across study regions (66% best, 24% worst). Moreover, more than half of range losses and gains are predicted to occur outside PAs where interspecific ranges overlap.Main ConclusionsMassive range decline is expected by 2050, but range gain is uncertain as African apes will not be able to occupy these new areas immediately due to their limited dispersal capacity, migration lag and ecological constraints. Given that most future range changes are predicted outside PAs, Africa's current PA network is likely to be insufficient for preserving suitable habitats and maintaining connected ape populations. Thus, conservation planners urgently need to integrate land use planning and climate change mitigation measures at all decision‐making levels both in range countries and abroad.

Future climate change will impact the size and location of breeding and wintering areas of migratory thrushes in South America

AbstractBird migration patterns are changing worldwide due to current global climate changes. Addressing the effects of such changes on the migration of birds in South America is particularly challenging because the details about how birds migrate within the Neotropics are generally not well understood. Here, we aim to infer the potential effects of future climate change on breeding and wintering areas of birds that migrate within South America by estimating the size and elevations of their future breeding and wintering areas. We used occurrence data from species distribution databases (VertNet and GBIF), published studies, and eBird for 3 thrush species (Turdidae; Turdus nigriceps, T. subalaris, and T. flavipes) that breed and winter in different regions of South America and built ecological niche models using ensemble forecasting approaches to infer current and future potential distributions throughout the breeding and wintering periods of each species. Our findings point to future shifts in wintering and breeding areas, mainly through elevational and longitudinal changes. Future breeding areas for T. nigriceps, which migrates along the Andes Mountains, will be displaced to the west, while breeding displacements to the east are expected for the other 2 species. An overall loss in the size of future wintering areas was also supported for 2 of the species, especially for T. subalaris, but an increase is anticipated for T. flavipes. Our results suggest that future climate change in South America will require that species shift their breeding and wintering areas to higher elevations in addition to changes in their latitudes and longitude. Our findings are the first to show how future climate change may affect migratory birds in South America throughout the year and suggest that even closely related migratory birds in South America will be affected in different ways, depending on the regions where they breed and overwinter.

SDM-CropProj – A model-assisted framework to forecast crop environmental suitability and fruit production

The effects of climate change (CC) will impact species ranges, and crops are no exception. Anticipating these changes through forecasting the environmentally suitable area of crops would help to reduce or mitigate the impact and adapt ecological and economic strategies. To forecast the CC effects on crops, we describe here a model-assisted framework (hereafter SDM-CropProj) that combines two modelling steps to be implemented in sequence: i) a multi-technique calibration process and ensemble-forecasting approach to predict the current and future environmental suitability of target crops; ii) a parsimonious univariate log-log linear model to relate the average total annual production to the current SDM-based suitable area. Different metrics for assessing the model's predictive performance showed that:•Crop production is related to model-predicted suitable area, thus allowing to obtain future projections of total fruit production based on climate scenarios.•The SDM-CropProj framework can assess potential pathways and trends in annual production due to changes in the environmental suitability and the distribution of multiple crop varieties/types as a consequence of CC, offering insights to other areas and crop types.

A global analysis of the susceptibility of river basins to invasion of a freshwater zooplankton (Daphnia lumholtzi)

Abstract Climate change and species invasions pose serious threats to biodiversity. Daphnia lumholtzi Sars, 1885, a cladoceran species native to Africa, Asia, and Oceania, has successfully invaded large sections of North and South America, there is evidence that the increase in the Earth's temperature and the number of reservoirs have facilitated this process. In this context, our study evaluated the effects of climate change on the invasive potential of D. lumholtzi, identifying the environmental suitability of non‐native river basins around the world to invasion by this species. Using ecological niche modelling, we predicted potential distribution areas of D. lumholtzi in non‐native continents at the present time, in 2050 and 2080. For future scenarios we considered two hypotheses of greenhouse gas emission: extreme (8.5 representative concentration pathways) and moderate (4.5 representative concentration pathways). We used different algorithms in niche models within the ensemble forecast approach to estimate the potential distribution of D. lumholtzi river basins worldwide. Furthermore, we developed a susceptibility index, that uses the presence of D. lumholtzi, its suitability information, area, and number of reservoirs to evaluate the susceptibility to invasion for each river basin. Our predictions showed that future climate change will have a strong influence on the invasive potential of D. lumholzti, potentially expanding its distribution in eastern North America and boosting invasion across Europe, especially at the end of the century. The susceptibility index also showed that many river basins that presently are not vulnerable to invasion by this species will become susceptible to invasion, these basins lie mainly in Europe. Our results will be useful for monitoring and mitigating the spread of this species, especially for river basins identified as highly susceptible to invasion. Thus, we suggested that D. lumholtzi should be a high priority for monitoring and preventive actions against invasion of these river basins. We also suggest that better risk assessments, that include invasive species and reservoirs, should be a priority in national energy development plans.

Assessing the future conservation potential of the Amazon and Andes Protected Areas: Using the woolly monkey (Lagothrix lagothricha) as an umbrella species

Tropical forests are facing constant degradation and destruction as a result of multiple threats, mostly from land-use, and increasingly from climate change. Understanding the effects of these threats, and the role of Protected Areas in buffering their impacts, is becoming an essential task to prevent the loss of species. In this study, we used the woolly monkey, a keystone species with seed dispersal functions, as a surrogate to assess the future conservation potential of the Protected Areas in Colombia, a megadiverse country where important tropical forests remain. We modelled the ecological niche of the woolly monkey using an ensemble forecasting approach to assess potential distribution changes resulting from climate change and deforestation. Distribution changes in 2050 were estimated for two Representative Concentration Pathways (RCPs), three Global Circulation Models (GCMs) and one deforestation projection. Additionally, we evaluated the current and future suitable area for the species located within Protected Areas. For the year 2016, we estimated a potential distribution area for the woolly monkey of 413,533 km2 after accounting for deforestation. The future synergistic effect of climate change and deforestation could cause a reduction of up to ∼13 % in the species potential distribution in Colombia, mainly in the Andean-Amazonian foothills and the Amazon region. The distribution area lost due to climate change was exceeded by losses due to deforestation in all scenarios, where up to 65,356 km2 could be lost as a result of forest clearance. The current Protected Area system will not be able to mitigate the impacts of climate and land-use changes, since ∼26 % of the woolly monkey’s range is currently located within National Parks and Indigenous Reserves. This range could decrease by ∼18 % in the most drastic future scenario. Based on these results, we emphasize the need of an integrated approach of land-use planning that encompasses the creation, expansion and connectivity of Protected Areas, along with the strengthening of law enforcement capacity and restoration efforts to halt increasing deforestation. The effective implementation of these strategies is necessary to assist species to track their suitable climatic conditions, especially through the Andean-Amazonian foothills, and will contribute to the conservation of tropical biodiversity.

Ecological niche models predict the potential distribution of the exotic rotifer Kellicottia bostoniensis (Rousselet, 1908) across the globe

The invasion of non-native species is one of the major factors influencing the loss of biodiversity. The rotifer Kellicottia bostoniensis, which is native to North America, has been registered in several other countries and on other continents. We investigated the main climatic-environmental variables suitable for the potential distribution of K. bostoniensis and, hence, the areas of non-native regions where it could spread. We used different statistical niche models within an ensemble forecasting approach to estimate environmental suitability and the potential global distribution area of K. bostoniensis. Precipitation in the driest month and temperature in the coldest month were the most important variables in predicting the distribution of the species. Our predictions indicated that countries with high invasive potential included Argentina and Brazil (South America), Croatia, France, and Germany (Europe). The results also revealed that many areas with high invasive potential, such as East Asia, have no recorded occurrences yet. Our study provides important information for implement management measures to deal with the problems of the spread of K. bostoniensis through ballast water and aquaculture in countries and regions highly susceptible to invasion (mainly areas that the species has not yet reached), thus providing useful information for decision making.

Characterization of Background Temperature Dynamics of a Multitemporal Satellite Scene through Data Assimilation for Wildfire Detection

Detection of an active fire in an image scene relies on an accurate estimation of the background temperature of the scene, which must be compared to the observed temperature, to decide on the presence of fire. The expected background temperature of a pixel is commonly derived based on spatial-contextual information that can overestimate the background temperature of a fire pixel and therefore results in the omission of a fire event. This paper proposes a method that assimilates brightness temperatures acquired from the Geostationary Earth Orbit (GEO) sensor MSG-SEVIRI into a Diurnal Temperature Cycle (DTC) model. The expected brightness temperatures are observational forecasts derived using the ensemble forecasting approach. The threshold on the difference between the observed and expected temperatures is derived under a Constant False Alarm Rate (CFAR) framework. The detection results are assessed against a reference dataset comprised of MODIS MOD14/MYD14 and EUMETSAT FIR products, and the performance is presented in terms of user’s and producer’s accuracies, and Precision-Recall and Receiver Operating Characteristic (ROC) graphs. The method has a high detection rate when the data assimilation is implemented with an Ensemble Kalman Filter (EnKF) and a Sampling Importance Resampling (SIR) particle filter, while the weak-constraint Four-Dimensional Variational Assimilation (4D-Var) has comparatively lower detection and false alarm rates according to the reference dataset. Consideration of the diurnal variation in the background temperature enables the proposed method to detect even low-power fires.