Data Integration for Large-Scale Models of Species Distributions

Trends in Ecology & Evolution, Journal Year: 2019, Volume and Issue: 35(1), P. 56 - 67

Published: Nov. 2, 2019

With the expansion in quantity and types of biodiversity data being collected, there is a need to find ways combine these different sources provide cohesive summaries species' potential realized distributions space time. Recently, model-based integration has emerged as means achieve this by combining datasets that retain strengths each. We describe flexible approach using point process models, which convenient way translate across ecological currencies. highlight recent examples large-scale models based on outline conceptual technical challenges opportunities arise.

Language: Английский

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Joint Species Distribution Modelling

Published: May 18, 2020

Joint species distribution modelling (JSDM) is a fast-developing field and promises to revolutionise how data on ecological communities are analysed interpreted. Written for both readers with limited statistical background, those expertise, this book provides comprehensive account of JSDM. It enables integrate abundances, environmental covariates, traits, phylogenetic relationships, the spatio-temporal context in which have been acquired. Step-by-step coverage full technical detail methods provided, as well advice interpreting results analyses broader modern community ecology theory. With advantage numerous example R-scripts, an ideal guide help graduate students researchers learn conduct interpret practice R-package Hmsc, providing fast starting point applying joint their own data.

Language: Английский

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Synthesizing multiple data types for biological conservation using integrated population models

Biological Conservation, Journal Year: 2017, Volume and Issue: 217, P. 240 - 250

Published: Nov. 20, 2017

Language: Английский

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The recent past and promising future for data integration methods to estimate species’ distributions

Methods in Ecology and Evolution, Journal Year: 2019, Volume and Issue: 10(1), P. 22 - 37

Published: Jan. 1, 2019

Abstract With the advance of methods for estimating species distribution models has come an interest in how to best combine datasets improve estimates distributions. This spurred development data integration that simultaneously harness information from multiple while dealing with specific strengths and weaknesses each dataset. We outline general principles have guided review recent developments field. then key areas allow a more framework integrating provide suggestions improving sampling design validation integrated models. Key advances been using point‐process thinking estimators developed different types. Extending this new types will further our inferences, as well relaxing assumptions about parameters are jointly estimated. These along better use regarding effort spatial autocorrelation inferences. Recent form strong foundation implementation Wider adoption can inferences distributions dynamic processes lead distributional shifts.

Language: Английский

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A Systematic Review of Marine-Based Species Distribution Models (SDMs) with Recommendations for Best Practice

Frontiers in Marine Science, Journal Year: 2017, Volume and Issue: 4

Published: Dec. 17, 2017

In the marine environment Species Distribution Models (SDMs) have been used in hundreds of papers for predicting present and future geographic range environmental niche species. We analysed ways which SDMs are being applied to species order recommend best practice studies. This systematic review was registered as a protocol on Open Science Framework: https://osf.io/tngs6/. The literature reviewed (236 papers) published between 1992 July 2016. number significantly increased through time (R2=0.92, p<0.05). studies were predominantly carried out Temperate Northern Atlantic (45%) followed by global scale (11%) Australasia (10%). majority focused theoretical ecology (37%) including investigations biological invasions non-native organisms, conservation planning (19%), climate change predictions (17%). Most ecological, multidisciplinary or biodiversity journals. (94%) failed report amount uncertainty derived from data deficiencies model parameters. Best recommendations proposed here ensure that novice advanced SDM users can (a) understand main elements SDMs, (b) reproduce standard methods analysis, (c) identify potential limitations with their data. suggest future, should key features approaches employed, deficiencies, selection explanatory model, approach taken validate results. addition, based reviewed, we account levels part modelling process.

Language: Английский

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A practical guide for combining data to model species distributions

Ecology, Journal Year: 2019, Volume and Issue: 100(6)

Published: March 30, 2019

Understanding and accurately modeling species distributions lies at the heart of many problems in ecology, evolution, conservation. Multiple sources data are increasingly available for distributions, such as from citizen science programs, atlases, museums, planned surveys. Yet reliably combining can be challenging because vary considerably their design, gradients covered, potential sampling biases. We review, synthesize, illustrate recent developments multiple distribution modeling. identify five ways which typically combined distributions. These approaches ability to accommodate bias, uncertainty when quantifying environmental relationships models. Many challenges solved through prudent use integrated models: models that simultaneously combine different on locations quantify explaining distribution. these using survey 24 birds coupled with opportunistically collected eBird southeastern United States. This example illustrates some benefits integration, increased precision relationships, greater predictive accuracy, accounting sample bias. it also vastly methodologies amounts data. provide one solution this challenge weighted joint likelihoods. Weighted likelihoods a means emphasize based criteria (e.g., size), we find weighting improves predictions all considered. conclude by providing practical guidance

Language: Английский

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Analytical guidelines to increase the value of community science data: An example using eBird data to estimate species distributions

Diversity and Distributions, Journal Year: 2021, Volume and Issue: 27(7), P. 1265 - 1277

Published: May 7, 2021

Abstract Aim Ecological data collected by the general public are valuable for addressing a wide range of ecological research and conservation planning, there has been rapid increase in scope volume available. However, from eBird or other large‐scale projects with volunteer observers typically present several challenges that can impede robust inferences. These include spatial bias, variation effort species reporting bias. Innovation We use example estimating distributions eBird, community science citizen (CS) project. estimate two widely used metrics distributions: encounter rate occupancy probability. For each metric, we critically assess impact processing steps either degrade refine analyses. CS density varies across globe, so also test whether differences model performance to sample size. Main conclusions Model improved when analytical methods addressed arising data; however, degree improvement varied density. The largest gains observed were achieved 1) complete checklists (where report all they detect identify, allowing non‐detections be inferred) 2) covariates describing detectability checklist. Occupancy models more lack checklists. Improvements refinement evident larger sizes. In general, found value situation encourage researchers benefits scenarios. approaches will enable effectively harness vast knowledge exists within basic research.

Language: Английский

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Addressing data integration challenges to link ecological processes across scales

Frontiers in Ecology and the Environment, Journal Year: 2021, Volume and Issue: 19(1), P. 30 - 38

Published: Feb. 1, 2021

Data integration is a statistical modeling approach that incorporates multiple data sources within unified analytical framework. Macrosystems ecology – the study of ecological phenomena at broad scales, including interactions across scales increasingly employs techniques to expand spatiotemporal scope research and inferences, increase precision parameter estimates, account for uncertainty in estimates multiscale processes. We highlight four common challenges macrosystems research: scale mismatches, unbalanced data, sampling biases, model development assessment. explain each problem, discuss current approaches address issue, describe potential areas overcome these hurdles. Use has increased rapidly recent years, given inferential value such approaches, we expect continued wider application disciplines, especially ecology.

Language: Английский

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Improving the reliability of eDNA data interpretation

Molecular Ecology Resources, Journal Year: 2021, Volume and Issue: 21(5), P. 1422 - 1433

Published: March 3, 2021

Abstract Global declines in biodiversity highlight the need to effectively monitor density and distribution of threatened species. In recent years, molecular survey methods detecting DNA released by target‐species into their environment (eDNA) have been rapidly on rise. Despite providing new, cost‐effective tools for conservation, eDNA‐based are prone errors. Best field laboratory practices can mitigate some, but risks errors cannot be eliminated accounted for. Here, we synthesize advances data processing that increase reliability interpretations drawn from eDNA data. We review occupancy models consider spatial data‐structures simultaneously assess rates false positive negative results. Further, introduce process‐based integration metabarcoding as complementing approaches assessments. These will most effective when capitalizing multi‐source sets collating with classical citizen‐science approaches, paving way more robust decision‐making processes conservation planning.

Language: Английский

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An overview of the history, current contributions and future outlook of iNaturalist in Australia

Wildlife Research, Journal Year: 2021, Volume and Issue: 48(4), P. 289 - 303

Published: March 18, 2021

Citizen science initiatives and the data they produce are increasingly common in ecology, conservation biodiversity monitoring. Although quality of citizen has historically been questioned, biases can be detected corrected for, allowing these to become comparable professionally collected data. Consequently, is being integrated with professional science, collection at unprecedented spatial temporal scales. iNaturalist one most popular platforms globally, more than 1.4 million users having contributed over 54 observations. Australia top contributing nation southern hemisphere, four nations 1.6 observations 36 000 identified species by almost 27 users. Despite platform’s success, there few holistic syntheses contributions iNaturalist, especially for Australia. Here, we outline history from an Australian perspective, summarise, taxonomically, temporally spatially, platform. We conclude discussing important future directions maximise usefulness ecological research, policy.

Language: Английский

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