Worldclim 2.1 versus Worldclim 1.4: Climatic niche and grid resolution affect between‐version mismatches in Habitat Suitability Models predictions across Europe

Ecology and Evolution, Год журнала: 2022, Номер 12(2)

Опубликована: Фев. 1, 2022

The influence of climate on the distribution taxa has been extensively investigated in last two decades through Habitat Suitability Models (HSMs). In this context, Worldclim database represents an invaluable data source as it provides worldwide surfaces for both historical and future time horizons. Thousands HSMs-based papers have published taking advantage 1.4, first online version repository. 2017, 2.1 was released. Here, we evaluated spatially explicit prediction mismatch at continental scale, focusing Europe, between HSMs fitted using from versions (between-version differences). To aim, simulated occurrence probability presence-absence across Europe four virtual species (VS) with differing climate-occurrence relationships. For each VS, upon uncorrelated bioclimatic variables derived three grid resolutions. factor combination, attaining sufficient discrimination performance independent test were projected under current conditions various scenarios, importance scores single computed. failed accurately retrieving relationships climate-tolerant VS one occurring a narrow combination climatic conditions. Under climate, noticeable between-version emerged most these VSs, whose suitability mainly depended diurnal or yearly variability temperature; differently, differences more clustered toward areas showing extreme values, like mountainous massifs southern regions, VSs responding to average temperature precipitation trends. chosen emission scenarios Global Climate did not evidently discrepancies, while resolution synergistically interacted VSs' niche characteristics determining extent such differences. Our findings could help re-evaluating previous biodiversity-related works relying geographical predictions Worldclim-based HSMs.

Язык: Английский

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Flexible species distribution modelling methods perform well on spatially separated testing data

Global Ecology and Biogeography, Год журнала: 2023, Номер 32(3), С. 369 - 383

Опубликована: Янв. 27, 2023

Abstract Aim To assess whether flexible species distribution models that perform well at nearby testing locations still strongly when evaluated on spatially separated data. Location Australian Wet Tropics (AWT), Ontario, Canada (CAN), north‐east New South Wales, Australia (NSW), Zealand (NZ), five countries of America (SA), and Switzerland (SWI). Time period Most data were collected between 1950 2000. Major taxa studied Birds, mammals, plants reptiles. Methods We compared 10 modelling methods with varying flexibility in terms the allowed complexity their fitted functions [boosted regression trees (BRT), generalized additive model (GAM), multivariate adaptive splines (MARS), maximum entropy (MaxEnt), support vector machine (SVM), variants linear (GLM) random forest (RF), an Ensemble model]. used established practices for selection to avoid overfitting, including parameter tuning learning methods. Models trained presence–background 171 tested presence–absence Training using both spatial partitioning, latter based 75‐km blocks. calculated average performance mean rank (focussing area under receiver operating characteristic precision‐recall gain curves, correlation) assessed statistical significance differences them. Results The ranking did not change strongest predictive nonparametric known be flexible. An ensemble formed by averaging predictions pre‐selected was best followed MaxEnt a variant forest. Main conclusions Whilst some modellers expect limited simple smooth predict better data, we found no evidence blocks 75 km. conclude are tuned enough overfitting effective predicting distinct areas.

Язык: Английский

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Glacier retreat reorganizes river habitats leaving refugia for Alpine invertebrate biodiversity poorly protected

Nature Ecology & Evolution, Год журнала: 2023, Номер 7(6), С. 841 - 851

Опубликована: Май 4, 2023

Язык: Английский

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Meta-analysis reveals less sensitivity of non-native animals than natives to extreme weather worldwide

Nature Ecology & Evolution, Год журнала: 2023, Номер 7(12), С. 2004 - 2027

Опубликована: Ноя. 6, 2023

Язык: Английский

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Can ecological niche models be used to accurately predict the distribution of invasive insects? A case study of Hyphantria cunea in China

Ecology and Evolution, Год журнала: 2024, Номер 14(3)

Опубликована: Март 1, 2024

Abstract In recent decades, ecological niche models (ENMs) have been widely used to predict suitable habitats for species. However, invasive organisms, the prediction accuracy is unclear. this study, we employed most maximum entropy (MaxEnt) model and ensemble (EM) Biomod2 verified practical effectiveness of ENM in predicting distribution areas insects based on true occurrence Hyphantria cunea China. The results showed that when only limited data were used, two ENMs could not effectively H. , although use global can greatly improve ENMs. When analyzing same data, Biomod2's was significantly better than MaxEnt. For long‐term predictions, area habitat predicted by much greater area; short‐term improved. Under current conditions, China 118 × 10 4 km 2 which 59.32% moderately or highly habitat. Future climate change increase China, all scenarios exceeded 355 accounting 36.98% total land This study demonstrates provides a reference management

Язык: Английский

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Estimating reference crop evapotranspiration using improved convolutional bidirectional long short-term memory network by multi-head attention mechanism in the four climatic zones of China

Agricultural Water Management, Год журнала: 2024, Номер 292, С. 108665 - 108665

Опубликована: Янв. 9, 2024

Accurate reference crop evapotranspiration (ET0) estimation is essential for agricultural water management, productivity, and irrigation systems. As the standard ET0 method, Penman-Monteith equation has been widely recommended worldwide. However, its application still restricted to comprehensive meteorological data deficiency, making exploration of alternative simpler models acceptable highly meaningful. Concerning aforementioned requirement, this study developed novel deep learning model (MA-CNN-BiLSTM), which incorporates Multi-Head Attention mechanism (MA), Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory network (BiLSTM) as intricate relationship processor, feature extractor, regression component, estimate based on radiation-based (Rn-based), humidity-based (RH-based), temperature-based (T-based) input combinations at 600 stations during 1961–2020 throughout China under internal external cross-validation strategies. Besides, through a comparative evaluation among MA-CNN-BiLSTM, CNN-BiLSTM, BiLSTM, LSTM, Multivariate Adaptive Regression Splines (MARS), empirical models, result indicated that MA-CNN-BiLSTM achieved superior precision, with values Determination Coefficient (R2), Nash–Sutcliffe efficiency coefficient (NSE), Relative Root Mean Square Error (RRMSE), (RMSE), Absolute (MAE) ranging 0.877–0.972, 0.844–0.962, 0.129–0.292, 0.294–0.644 mm d−1, 0.244–0.566 d−1 strategy 0.797–0.927, 0.786–0.920, 0.162–0.335, 0.409–0.969 0.294–0.699 strategy. Specifically, Rn-based excelled in temperate continental zone (TCZ) mountain plateau (MPZ), while RH-based yielded best precision others. Furthermore, was by 2.74–106.04% R2, 1.11–120.49% NSE, 1.41–40.27% RRMSE, 1.68–45.53% RMSE, 1.21–38.87% MAE, respectively. In summary, main contribution present proposal LSTM-type (MA-CNN-BiLSTM) cope various data-missing scenarios China, can provide effective support decision-making regional agriculture management.

Язык: Английский

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A super SDM (species distribution model) ‘in the cloud’ for better habitat-association inference with a ‘big data’ application of the Great Gray Owl for Alaska

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Март 27, 2024

The currently available distribution and range maps for the Great Grey Owl (GGOW; Strix nebulosa) are ambiguous, contradictory, imprecise, outdated, often hand-drawn thus not quantified, based on data or scientific. In this study, we present a proof of concept with biological application technical workflow progress latest global open access 'Big Data' sharing, Open-source methods R geographic information systems (OGIS QGIS) assessed six recent multi-evidence citizen-science sightings GGOW. This proposed can be applied quantified inference any species-habitat model such as typically species models (SDMs). Using Random Forest-an ensemble-type Machine Learning following Leo Breiman's approach from predictions-we Super SDM GGOWs in Alaska running Oracle Cloud Infrastructure (OCI). These SDMs were best publicly (410 occurrences + 1% new assessment sightings) over 100 environmental GIS habitat predictors ('Big Data'). compiled associated overcome first time limitations traditionally used PC laptops. It breaks ground has real-world implications conservation land management GGOW, Alaska, other worldwide 'new' baseline. As research field remains dynamic, have limits, ultimate final statement associations yet, but they summarize all topic testable fashion allowing fine-tuning improvements needed. At minimum, allow low-cost rapid great leap forward to more ecological inclusive at-hand. GGOWs, here aim correct perception towards inclusive, holistic, scientifically urban-adapted owl Anthropocene, rather than mysterious wilderness-inhabiting (aka 'Phantom North'). Such was never created bird before opens perspectives impact policy sustainability.

Язык: Английский

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Integrating species distribution and piecewise linear regression model to identify functional connectivity thresholds to delimit urban ecological corridors

Computers Environment and Urban Systems, Год журнала: 2024, Номер 113, С. 102177 - 102177

Опубликована: Авг. 22, 2024

Язык: Английский

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Comprehensive prediction of potential spatiotemporal distribution patterns, priority planting regions, and introduction adaptability of Elymus sibiricus in the Chinese region

Frontiers in Plant Science, Год журнала: 2025, Номер 15

Опубликована: Янв. 8, 2025

The natural grassland in China is facing increasingly serious degradation. Elymus sibiricus L., as an important native alpine grass, widely used the restoration and improvement of grassland. In this study, geographical distribution environmental data E. were collected, potential spatiotemporal pattern, planting introduction adaptability comprehensively predicted by using ensembled ecological niche model Marxan model. results show that (1) spatial mainly spans 33°-42°N 95°-118°E. It was distributed Qilian Mountains (northeast Qinghai-Tibet Plateau), Taihang (junction Loess Plateau Inner Mongolia Tianshan Mountains; (2) with passage time, suitable regions generally showed a collapse trend, but its main did not obvious change, (centroid) migrated to southwest 2.93 km; (3) current period significantly affected annual range monthly near-surface relative humidity, mean air temperature, evapotranspiration, climate moisture index, elevation, exchangeable Ca2+, available P, H+, precipitation amount, respectively; (4) area cover 2.059 × 105 km2, which (southeast middle part Mountains, southeast Altai (5) six germplasm (LM01-LM06) all high-elevation western China. study aims provide effective theoretical basis for collection, preservation, utilization resources

Язык: Английский

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Modeling the effects of climate change scenarios on the potential distribution of Vespa crabro Linnaeus, 1758 (Hymenoptera: Vespidae) in a Mediterranean biodiversity hotspot

Ecological Informatics, Год журнала: 2025, Номер unknown, С. 103006 - 103006

Опубликована: Янв. 1, 2025

Язык: Английский

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