Anticipating species distributions: Handling sampling effort bias under a Bayesian framework

Rocchini, Duccio; Garzón‐López, Carol X.; Marcantonio, Matteo; Amici, Valerio; Bacaro, Giovanni; Bastin, Lucy; Brummitt, Neil; Chiarucci, Alessandro; Foody, Giles M.; Hauffe, Heidi C.; He, Kate S.; Ricotta, Carlo; Rizzoli, Annapaola; Rosà, Roberto

doi:10.1016/j.scitotenv.2016.12.038

Cited by 21 publications

(12 citation statements)

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“…The relationship between the number of replicates and the completeness of information is wellknown in data analysis (e.g. Rocchini et al 2017), and this pattern clearly emerged from our results. Although placed in the most representative location of the community, one single plot proved to be inadequate to thoroughly describe the features of the habitat types (e.g.…”

Section: Discussionsupporting

confidence: 81%

Trade-offs between sampling effort and data quality in habitat monitoring

et al. 2018

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The transect method has been widely used to monitor habitat conservation status and has been recently recommended as the best tool to monitor steep ecological gradients, such as those in coastal systems. Despite that, the effectiveness of the transect approach can be limited when considering the sampling effort in terms of time needed for sampling. Our work aimed at evaluating the efficacy of the transect approach in a Mediterranean coastal system. Specifically we aimed at evaluating the sampling effort versus the completeness of datasets obtained by performing belt transects in different ways specifically designed to progressively reduce the sampling effort: (i) sampling plots adjacently ("adjacent-plot transect"); (ii) sampling plots alternately ("alternate-plot transect"); (iii) sampling one plot at each plant community along the vegetation zonation ("zonation-plot transect"). We evaluated method efficiency in terms of number and type of habitats identified, spatial extent, species richness and composition, through multivariate analyses, null models and rarefaction curves. The sampling effort was measured in terms of time needed for sampling. The zonation-plot transect had the lowest sampling effort, but provided only an approximation of the state of the dunal communities. The alternate-plot transect showed the best trade-off between the sampling effort and the completeness of information obtained, and may be considered as a efficient option in very wide coastal systems. Our research provides guidelines that can be used in other coastal systems to choose the most cost-effective monitoring method thereby maximising the efficient use of monitoring resources. Keywords Coastal sand dunes • Habitat risk assessment • Data collection • Sampling efficiency • Belt transect Communicated by Daniel Sanchez Mata. This article belongs to the Topical Collection:Biodiversity protection and reserves.

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Section: Discussionsupporting

confidence: 81%

Trade-offs between sampling effort and data quality in habitat monitoring

et al. 2018

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“…This is reflected in the finding that new ecological research sites in the Amazon tend to be located near existing sites [15] and that the scientific productivity of protected areas is strongly associated with years since the first article based in the area was published [17]. More specifically, detailed ecological knowledge is needed to parameterize ecological niche models, key tools in our attempts to understand how species responded to past changes and to predict how they might cope with future climatic changes [18–20]. Thirdly, the presence of researchers in an area may have a safeguarding effect, independent of the research they are performing [21, 22].…”

Section: Introductionmentioning

confidence: 99%

Known unknowns: Filling the gaps in scientific knowledge production in the Caatinga

et al. 2019

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The Caatinga is an ecologically unique semi-arid region of northeast Brazil characterized by high levels of endemism and severe anthropogenic threats from agricultural development and climate change. It is also one of the least known biomes in Brazil due to a combination of inadequate investment, low regional research capacity and difficult working conditions. However, while the lack of scientific knowledge of the Caatinga is well known, the spatial and temporal distribution of knowledge production has not been investigated. This is important because such biases undermine the development of effective conservation policy and practice and increase the uncertainty associated with conservation actions. Here, we map the geography of conservation knowledge production in the Caatinga and use an innovative hurdle model to identify the presumptive factors driving these patterns. Our analysis revealed strong geographic patterns, with research sites concentrated in the east of the region and in areas close to roads and research centres. There was also a positive association between conservation knowledge production and risk of desertification, indicating that conservation scientists are responding to conservation challenges faced by Caatinga’s fauna and flora arising from climate change. Our results also highlight the pivotal role of pioneer scientists (those who develop research sites in previously unstudied/understudied areas) in determining the future geographic patterns of knowledge production. We conclude our article with a brief discussion of potential policies for increasing the spatial representativeness of conservation research in this remarkable ecosystem.

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“…As an example, shrinkage regression, recently applied in biodiversity conservation (Authier, Saraux, & Peron, ) could allow a direct focus on habitat modelling, which is one of the major strengths of remote sensing (Gillespie, Foody, Rocchini, Giorgi, & Saatchi, ). Moreover, such analysis might be performed in a Bayesian framework allowing to (1) model multidimensional covariates with non‐stationary variation over space (Randell, Turnbull, Ewans, & Jonathan, ), such as the bands of satellite images, and (2) model the errors in the output and their variation over space (Rocchini et al., ).…”

Section: Resultsmentioning

confidence: 99%

Measuring β‐diversity by remote sensing: A challenge for biodiversity monitoring

et al. 2018

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Biodiversity includes multiscalar and multitemporal structures and processes, with different levels of functional organization, from genetic to ecosystemic levels. One of the mostly used methods to infer biodiversity is based on taxonomic approaches and community ecology theories. However, gathering extensive data in the field is difficult due to logistic problems, especially when aiming at modelling biodiversity changes in space and time, which assumes statistically sound sampling schemes. In this context, airborne or satellite remote sensing allows information to be gathered over wide areas in a reasonable time. Most of the biodiversity maps obtained from remote sensing have been based on the inference of species richness by regression analysis. On the contrary, estimating compositional turnover (β‐diversity) might add crucial information related to relative abundance of different species instead of just richness. Presently, few studies have addressed the measurement of species compositional turnover from space. Extending on previous work, in this manuscript, we propose novel techniques to measure β‐diversity from airborne or satellite remote sensing, mainly based on: (1) multivariate statistical analysis, (2) the spectral species concept, (3) self‐organizing feature maps, (4) multidimensional distance matrices, and the (5) Rao's Q diversity. Each of these measures addresses one or several issues related to turnover measurement. This manuscript is the first methodological example encompassing (and enhancing) most of the available methods for estimating β‐diversity from remotely sensed imagery and potentially relating them to species diversity in the field.

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Anticipating species distributions: Handling sampling effort bias under a Bayesian framework

Cited by 21 publications

References 53 publications

Trade-offs between sampling effort and data quality in habitat monitoring

Trade-offs between sampling effort and data quality in habitat monitoring

Known unknowns: Filling the gaps in scientific knowledge production in the Caatinga

Measuring β‐diversity by remote sensing: A challenge for biodiversity monitoring

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