Optimising occurrence data in species distribution models: sample size, positional uncertainty, and sampling bias matter

Ecography, Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 2, 2024

Species distribution models (SDMs) have proven valuable in filling gaps our knowledge of species occurrences. However, despite their broad applicability, SDMs exhibit critical shortcomings due to limitations occurrence data. These include, particular, issues related sample size, positional uncertainty, and sampling bias. In addition, it is widely recognised that the quality as well approaches used mitigate impact aforementioned data depend on ecology. While numerous studies evaluated effects these SDM performance, a synthesis results lacking. without comprehensive understanding individual combined effects, ability predict influence modelled species–environment associations remains largely uncertain, limiting value model outputs. this paper, we review bias, ecology We build upon findings provide recommendations for assessment intended use SDMs.

Language: Английский

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Assessing the invasion risk of the cnidaria Blackfordia virginica Mayer, 1910: a threat to the Baltic Sea ecosystem?

Biological Invasions, Journal Year: 2025, Volume and Issue: 27(4)

Published: March 22, 2025

Language: Английский

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Prioritizing management actions for invasive non-native plants through expert-based knowledge and species distribution models

Ecological Indicators, Journal Year: 2024, Volume and Issue: 166, P. 112279 - 112279

Published: July 2, 2024

Given the high number of non-native plants that are being introduced worldwide and time required to process formal pest risk analyses, a framework for prioritization management actions is urgently required. We therefore propose replicable standardized (eradication, control monitoring) invasive plants, combining expert knowledge, current future climatic suitability estimated by species distribution models (SDMs), clustering ordination techniques. Based on consultation using Italy as case study, plant were selected three categories identified: eradication, containment, monitoring. Finally, two further classes priorities proposed each actions: "high" "low" priority. Overall, SDMs highlighted very Continental Mediterranean bioregions most plants. Cluster analysis revealed distinct clusters with varying levels Italian bioregions. 1 exhibited higher across all bioregions, whereas grouped in 2 predominantly featured areas. 3 showed lowest values. Two variability bioclimatic within cluster, well their pattern. Lastly, third ordination, integrating spatial patterns, has allowed differentiation at both national bioregional scales. Specifically, seven earmarked eradication action, six monitoring while remaining deemed suitable containment. Our results methodology meet demand new early warning tools; predict location outbreaks, establish monitor species.

Language: Английский

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Assessing the applicability of binary land-cover variables to species distribution models across multiple grains

Landscape Ecology, Journal Year: 2024, Volume and Issue: 39(3)

Published: March 4, 2024

Abstract Context Species distribution models are widely used in ecology. The selection of environmental variables is a critical step SDMs, nowadays compounded by the increasing availability data. Objectives To evaluate interaction between grain size and binary (presence or absence water) proportional (proportion water within cell) representation cover variable when modeling bird species distribution. Methods eBird occurrence data with an average number records 880,270 per across North American continent were for analysis. Models (via Random Forest) fitted 57 species, two seasons (breeding vs. non-breeding), at four grains (1 km 2 to 2500 ) using as variable. Results models’ performances not affected type adopted (proportional binary) but significant decrease was observed importance form. This especially pronounced coarser during breeding season. Binary useful finer sizes (i.e., 1 ). Conclusions At more detailed ), simple presence certain land-cover can be realistic descriptor occurrence. particularly advantageous collecting habitat field simply recording significantly less time-consuming than its total area. For grains, we recommend variables.

Language: Английский

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Species distribution models affected by positional uncertainty in species occurrences can still be ecologically interpretable

Ecography, Journal Year: 2023, Volume and Issue: 2023(6)

Published: April 27, 2023

Species distribution models (SDMs) have become a common tool in studies of species–environment relationships but can be negatively affected by positional uncertainty underlying species occurrence data. Previous work has documented the effect on model predictive performance, its consequences for inference about remain largely unknown. Here we use over 12 000 combinations virtual and real environmental variables species, as well case study, to investigate how accurately SDMs recover after applying known errors We explored range predictors with various spatial heterogeneity, species' niche widths, sample sizes magnitudes error. Positional decreased performance all modeled scenarios. The absolute relative importance shape species–environmental co‐varied level uncertainty. These differences were much weaker than those observed overall especially homogenous predictor variables. This suggests that, at least example conditions analyzed, negative did not extend strongly ecological interpretability models. Although findings are encouraging practitioners using reveal generative mechanisms based spatially uncertain data, they suggest greater applications utilizing distributions predicted from positionally such conservation prioritization biodiversity monitoring.

Language: Английский

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Predicting the Global Potential Suitable Distribution of Fall Armyworm and Its Host Plants Based on Machine Learning Models

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(12), P. 2060 - 2060

Published: June 7, 2024

The fall armyworm (Spodoptera frugiperda) (J. E. Smith) is a widespread, polyphagous, and highly destructive agricultural pest. Global climate change may facilitate its spread to new suitable areas, thereby increasing threats host plants. Consequently, predicting the potential distribution for plants under current future scenarios crucial assessing outbreak risks formulating control strategies. This study, based on remote sensing assimilation data plant protection survey data, utilized machine learning methods (RF, CatBoost, XGBoost, LightGBM) construct prediction models 120 Hyperparameter stacking ensemble method (SEL) were introduced optimize models. results showed that SEL demonstrated optimal performance in armyworm, with an AUC of 0.971 ± 0.012 TSS 0.824 0.047. Additionally, LightGBM 47 30 plants, respectively. Overlay analysis suggests overlap areas interaction links between will generally increase future, most significant rise RCP8.5 scenario, indicating threat further intensify due change. findings this study provide support planning implementing global intercontinental long-term pest management measures aimed at mitigating impact food production.

Language: Английский

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Modelling wild boar abundance at high resolution

EFSA Supporting Publications, Journal Year: 2024, Volume and Issue: 21(7)

Published: July 1, 2024

Abstract By using the latest available data, we provide estimates of wild boar (Sus scrofa) distribution and abundance pre‐African Swine Fever (ASF) based on occurrence data in Europe. Secondly, as a basis for calibration model output into densities, used predictions relative abundance, hunting yield‐based (hunted individuals per km2), at 2x2 km (by ENETWILD Consortium) local densities (individuals km2) considered reliable obtained framework European Observatory Wildlife (EOW), well some from recent literature (2015 onwards). Hunting yield were different spatial scales namely 5, 10 15 radii buffer around localities with density estimations. The are better fit radius significant relationship between values level. This will offer possibility to predict boar. be useful incorporate risk factor analyses African selected range. is first time that absolute have been made these two approaches Europe, which demonstrates added value observatory approach (a number study areas where obtained, such EOW) generate novel information high epidemiological assessment. During an ASF outbreak effort change dramatically take few years return similar pre‐ASF levels, so post‐ASF would limited has present while. However, there relatively effect sighting rely actors, many whom may expected normal activities soon after arrives. Thus, more short term. These calculated but chosen locations uncertainty was high. We advocate developing this nework wildlife monitoring across general, harmioized programs, ensuring standardisation consistency generated collected, essential assessing management risks related not only other diseases.

Language: Английский

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Forecasting Range Shifts in Terrestrial Alpine Insects Under Global Warming

Ecology and Evolution, Journal Year: 2025, Volume and Issue: 15(1)

Published: Jan. 1, 2025

ABSTRACT Anthropogenic planetary heating is disrupting global alpine systems, but our ability to empirically measure and predict responses in species distributions impaired by a lack of comprehensive data technical limitations. We conducted comprehensive, semi‐quantitative review empirical studies on contemporary range shifts insects driven climate heating, drawing attention methodological issues potential biotic abiotic factors influencing variation responses. highlight case showing how dynamics may affect standing genetic adaptive potential, discuss integration frameworks can improve forecasts. Although influence individual responses, most studied so far are shifting higher elevations. Upslope often accompanied contractions that expected diminish increasing extinction risk. Endemic islands predicted be especially vulnerable. Inferences drawn from the insects, also have relevance other montane habitats. Correlative niche modelling keystone tool its limited consider biological processes underpinning species' complicates interpretation. Alpine exhibit some respond rising temperatures via change or phenotypic plasticity. Thus, future efforts should incorporate using flexible hybrid approaches enhance realism predictions. Boosting scientific capability envisage environments their associated biota imperative given speed intensity high‐mountain ecosystems surpass collect required guide effective conservation planning management decisions.

Language: Английский

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Paleobiogeographic insights gained from ecological niche models: progress and continued challenges

Paleobiology, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 21

Published: March 11, 2025

Abstract The spatial distribution of individuals within ecological assemblages and their associated traits behaviors are key determinants ecosystem structure function. Consequently, determining the species, how distributions influence patterns species richness across ecosystems today in past, helps us understand what factors act as fundamental controls on biodiversity. Here, we explore niche modeling has contributed to understanding spatiotemporal past biodiversity evolutionary processes. We first perform a semiquantitative literature review capture studies that applied models (ENMs) identifying 668 studies. coded each study according focal taxonomic group, whether used fossil evidence, it relied evidence or methods addition ENMs, scale study, temporal intervals included ENMs. trends publication categories anchor discussion recent technical advances modeling, focusing paleobiogeographic ENM applications. then explored contributions ENMs paleobiogeography, with particular focus examining drivers range dynamics; phylogeography within-lineage macroevolutionary processes, including change, speciation, extinction; community assembly; conservation paleobiogeography. Overall, powerful tools for elucidating patterns. most commonly Quaternary dynamics, but an increasing number use gain important insight into both processes pre-Quaternary times. Deeper integration phylogenies may further extend those insights.

Language: Английский

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Looking for reliable species distribution models for low‐density cetacean species: Compared effectiveness of SDMs for G. griseus, G. melas, Z. cavirostris in the Mediterranean Sea based on long‐term fixed‐transect data

Aquatic Conservation Marine and Freshwater Ecosystems, Journal Year: 2024, Volume and Issue: 34(3)

Published: Feb. 28, 2024

Abstract The EU regulative framework for the protection of marine biodiversity and habitats requires assessment species' conservation status identification core to design adequate management plans. However, distribution range habitat‐use pelagic large‐range, migratory species, such as cetaceans, is challenging. Species models (SDMs) are increasingly used in planning identify species priority areas. quality SDMs varies widely depending on representativeness data appropriateness modelling techniques. Since 2007, Fixed Line Transect Mediterranean Monitoring Network (FLT Med Net) has been continuously monitoring cetaceans throughout year basin using passenger ferries observation platforms that perform repetitive surveys along fixed trans‐border transects. With aim defining a standard analytical approach, collected by FLT Net rarer cetacean (i.e., Grampus griseus , Globicephala melas Ziphius cavirostris ) here assess performance commonly SDMs, including GLM, GAM, GAM‐Negative Binomial, GAM‐tweedy, MaxEnt Random Forest. Models were built evaluated total 296 sighting cross‐validated 145 independent points. Under testing conditions, almost all methods exhibited good performance, with Forest being best model several cases. when dataset, many yielded inconsistent results or notably low performance. Only demonstrated consistent efficiency reliability both cases, showing less affected unequal sampling small sample size. Results confirm importance robust SDM approaches based representative reliable areas long‐term coherence effectiveness spatial measures.

Language: Английский

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