Trends and gaps in the use of citizen science derived data as input for species distribution models: A quantitative review

PLoS ONE, Год журнала: 2021, Номер 16(3), С. e0234587 - e0234587

Опубликована: Март 11, 2021

Citizen science (CS) currently refers to the participation of non-scientist volunteers in any discipline conventional scientific research. Over last two decades, nature-based CS has flourished due innovative technology, novel devices, and widespread digital platforms used collect classify species occurrence data. For scientists, offers a low-cost approach collecting information at large spatial scales that otherwise would be prohibitively expensive. We examined trends gaps linked use as source data for distribution models (SDMs), order propose guidelines highlight solutions. conducted quantitative literature review 207 peer-reviewed articles measure how representation different taxa, regions, types have changed SDM publications since 2010s. Our shows number papers using SDMs increased approximately double rate overall papers. However, disparities taxonomic geographic coverage remain studies CS. Western Europe North America were regions with most (73%). Papers on birds (49%) mammals (19.3%) outnumbered other taxa. Among invertebrates, flying insects including Lepidoptera, Odonata Hymenoptera received attention. Discrepancies between research interest availability especially important amphibians, reptiles fishes. Compared animal plants rare. Although aims scope are diverse, conservation remained central theme present examples recommendations motivate further research, such combining multiple sources promoting local traditional knowledge. hope our findings will strengthen citizen-researchers partnerships better inform SDMs, less-studied taxa regions. Researchers stand benefit from quantity available improve global predictions distributions.

Язык: Английский

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Current and Future Influence of Environmental Factors on Small Pelagic Fish Distributions in the Northwestern Mediterranean Sea

Frontiers in Marine Science, Год журнала: 2020, Номер 7

Опубликована: Июль 24, 2020

In the Northwestern Mediterranean Sea, European sardine (Sardina pilchardus) and anchovy (Engraulis encrasicolus) are most important small pelagic fish in terms of biomass commercial interest. During last years, these species have experimented changes their abundance trends addition to growth, reproduction body condition. These particularly sensitive environmental fluctuations with possible cascading effects as they play a key role connecting lower upper trophic levels marine food webs. It is therefore essential understand factors that profoundly affect dynamics. This study used two-step approach how environment influences adult stages Sea. First, we explored change over time using Random Forests available datasets occurrence, abundance, landings. We then applied distribution models test impact extreme pessimistic optimistic Intergovernmental Panel on Climate Change (IPCC) pathway scenarios, identify climate refuges: areas where may be able persist under future change. Findings from temporal modelling showed mixed between variables for datasets. Future projections highlight both will undergo reduction spatial distributions due conditions. The refuges waters around Rhone River (France) Ebro (Spain) species. also highlights knowledge gaps our understanding dynamics region, which needed progress towards an ecosystem fisheries management.

Язык: Английский

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MaxEnt Modeling Based on CMIP6 Models to Project Potential Suitable Zones for Cunninghamia lanceolata in China

Forests, Год журнала: 2021, Номер 12(6), С. 752 - 752

Опубликована: Июнь 7, 2021

Cunninghamia lanceolata (Lamb.) Hook. (Chinese fir) is one of the main timber species in Southern China, which has a wide planting range that accounts for 25% overall afforested area. Moreover, it plays critical role soil and water conservation; however, its suitability subject to climate change. For this study, appropriate distribution area C. was analyzed using MaxEnt model based on CMIP6 data, spanning 2041–2060. The results revealed (1) minimum temperature coldest month (bio6), mean diurnal (bio2) were most important environmental variables affected lanceolata; (2) currently suitable areas primarily distributed along southern coastal 55% moderately so, while only 18% highly suitable; (3) projected would likely expand BCC-CSM2-MR, CanESM5, MRI-ESM2-0 under different SSPs increased estimated future ranged from 0.18 0.29 million km2, where total attained maximum value 2.50 km2 SSP3-7.0 scenario, with lowest 2.39 SSP5-8.5 scenario; (4) combination land use farmland protection policies more than 60% could be utilized 2041–2060 SSP scenarios. Although change having an increasing influence distribution, deleterious impacts anthropogenic activities cannot ignored. In future, further attention should paid investigation combined human activities.

Язык: Английский

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Trends in species distribution modelling in context of rare and endemic plants: a systematic review

Ecological Processes, Год журнала: 2022, Номер 11(1)

Опубликована: Июнь 8, 2022

Abstract Background Many research papers have utilized Species Distribution Models to estimate a species’ current and future geographic distribution environmental niche. This study aims (a) understand critical features of SDMs used model endemic rare species (b) identify possible constraints with the collected data. The present systematic review examined how are on plant optimal practices for research. Results evaluated literature (79 articles) was published between January 2010 December 2020. number grew considerably over time. studies were primarily conducted in Asia (41%), Europe (24%), Africa (2%). bulk (38%) focused theoretical ecology, climate change impacts (19%), conservation policy planning (22%). Most publications devoted biodiversity conservation, ecological or multidisciplinary fields. degree uncertainty not disclosed most (81%). Conclusion provides broad overview emerging trends gaps majority failed uncertainties error estimates. However, when performance estimates given, results will be highly effective, allowing more assurance predictions they make. Furthermore, based our review, we recommend that should represent levels errors modelling process.

Язык: Английский

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A new avenue for Bayesian inference with INLA

Computational Statistics & Data Analysis, Год журнала: 2023, Номер 181, С. 107692 - 107692

Опубликована: Янв. 10, 2023

Язык: Английский

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Spatially varying coefficients can improve parsimony and descriptive power for species distribution models

Ecography, Год журнала: 2023, Номер 2023(5)

Опубликована: Апрель 10, 2023

Species distribution models (SDMs) are widely used to relate species occurrence and density local environmental conditions, often include a spatially correlated variable account for spatial patterns in residuals. Ecologists have extended SDMs varying coefficients (SVCs), where the response given covariate varies smoothly over space time. However, SVCs see relatively little use perhaps because they remain less known relative other SDM techniques. We therefore review ecological contexts can improve interpretability descriptive power from SDMs, including responses regional indices that represent teleconnections; density‐dependent habitat selection; detectability; context‐dependent interactions with unmeasured covariates. then illustrate three additional examples detail using vector autoregressive spatio‐temporal (VAST) model. First, decadal trends model identifies arrowtooth flounder Atheresthes stomias Bering Sea 1982 2019. Second, trait‐based joint highlights role of body size temperature community assembly Gulf Alaska. Third, an age‐structured walleye pollock Gadus chalcogrammus contrasts cohorts broad distributions (1996 2009) those more constrained (2002 2015). conclude extend address wide variety be better understand range processes, e.g. dependence, population dynamics.

Язык: Английский

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Combining scientific survey and commercial catch data to map fish distribution

ICES Journal of Marine Science, Год журнала: 2022, Номер 79(4), С. 1133 - 1149

Опубликована: Фев. 15, 2022

Abstract Developing Species Distribution Models (SDM) for marine exploited species is a major challenge in fisheries ecology. Classical modelling approaches typically rely on fish research survey data. They benefit from standardized sampling design and controlled catchability, but they usually occur once or twice year may sample relatively small number of spatial locations. Spatial monitoring commercial data (based logbooks crossed with Vessel Monitoring Systems) can provide an additional extensive source to inform distribution. We propose hierarchical framework integrating both sources while accounting preferential (PS) From simulations, we demonstrate that PS should be accounted estimation when actually strong. When far exceed scientific data, the later bring little information predictions areas sampled by low fishing intensity validation dataset assess integrated model consistency. applied three demersal (hake, sole, squids) Bay Biscay emphasize contrasted account several fleets varying catchabilities behaviours.

Язык: Английский

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Species distribution modelling is needed to support ecological impact assessments

Journal of Applied Ecology, Год журнала: 2020, Номер 58(1), С. 21 - 26

Опубликована: Окт. 26, 2020

Abstract Legislation commonly mandates the mitigation of impacts to biodiversity in planning and development processes, with potential identified through some form ecological impact assessment. Yet, protections for are frequently undermined because distributions priority species poorly known most locations at spatial scales required inform decisions (i.e. c . 1–100 ha). Planning applications often screened against opportunistic records determine species. However, raw occurrence provide information only on where a has been detected cannot be used indicate if is likely absent from site. Inferences drawn these data likelihood being present site can correctly interpreted an appropriate distribution modelling (SDM) framework that ensures assumptions about models formalised documented. We argue SDM frameworks must integrated into assessments improve support within processes. Biases uncertainties create challenges, but recent methodological advances minimise their predictions. advocate co‐production practitioners tools, mapping products best‐practice guidelines specific Policy implications The integration will strengthen processes by ensuring transparency rigour interpretation data.

Язык: Английский

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Using a Bayesian modelling approach (INLA-SPDE) to predict the occurrence of the Spinetail Devil Ray (Mobular mobular)

Scientific Reports, Год журнала: 2020, Номер 10(1)

Опубликована: Ноя. 2, 2020

Abstract To protect the most vulnerable marine species it is essential to have an understanding of their spatiotemporal distributions. In recent decades, Bayesian statistics been successfully used quantify uncertainty surrounding identified areas interest for bycatch species. However, conventional simulation-based approaches are often computationally intensive. address this issue, in study, alternative approach (Integrated Nested Laplace Approximation with Stochastic Partial Differential Equation, INLA-SPDE) predict occurrence Mobula mobular eastern Pacific Ocean (EPO). Specifically, a Generalized Additive Model implemented analyze data from Inter-American Tropical Tuna Commission’s (IATTC) tropical tuna purse-seine fishery observer database (2005–2015). The INLA-SPDE had potential both importance EPO, that already known species, and more marginal hotspots, such as Gulf California Equatorial area which not using other habitat models. Some drawbacks were database, including difficulties dealing categorical variables triangulating effectively spatial data. Despite these challenges, we conclude INLA method useful complementary and/or traditional ones when modeling inform accurately management decisions.

Язык: Английский

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Can We Use Machine Learning for Agricultural Land Suitability Assessment?

Agronomy, Год журнала: 2021, Номер 11(4), С. 703 - 703

Опубликована: Апрель 7, 2021

It is vital for farmers to know if their land suitable the crops that they plan grow. An increasing number of studies have used machine learning models based on use data as an efficient means mapping suitability. This approach relies assumption grow in best-suited areas, but no systematically tested this assumption. We aimed test specialty Denmark. First, we mapped suitability 41 using learning. Then, compared predicted suitabilities with mechanistic model ECOCROP (Ecological Crop Requirements). The results showed there was little agreement between and ECOCROP. Therefore, argue methods represent different phenomena, which label socioeconomic ecological suitability, respectively. In most cases, predicts ambiguity term can lead misinterpretation. highlight need awareness distinction a way forward agricultural assessment.

Язык: Английский

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