Optimising long‐term monitoring projects for species distribution modelling: how atlas data may help

Abstract

29how they change over time may provide key information to guide eff ective landscape and conservation planning. Dynamic species distribution mapping may, therefore, be considered as an essential component of a biodiversity monitoring project (Brotons et al. 2007(Brotons et al. , K é ry et al. 2013. In any monitoring project, sampling units are, however, sparsely distributed over the region of interest, which is inconvenient for a straightforward mapping of species distributions.Species distribution modelling is an increasingly used technique (Rodr í guez et al. 2007) that can produce distribution maps based on monitoring data (Brotons et al. 2006). With these models, environmental variables describing the habitat conditions in the sampling units are related to records of species presence. Th ese models are used to predict the species distribution beyond the sampling units in areas where species occurrence is unknown (Ara ú jo and Guisan 2006, Elith et al. 2010. Th e use of models to predict species distributions is of key signifi cance for biodiversity conservation (Guisan et al. 2013). Among several applications, models Long-term biodiversity monitoring data are mainly used to estimate changes in species occupancy or abundance over time, but they may also be incorporated into predictive models to document species distributions in space. Although changes in occupancy or abundance may be estimated from a relatively limited number of sampling units, small sample size may lead to inaccurate spatial models and maps of predicted species distributions. We provide a methodological approach to estimate the minimum sample size needed in monitoring projects to produce accurate species distribution models and maps. Th e method assumes that monitoring data are not yet available when sampling strategies are to be designed and is based on external distribution data from atlas projects. Atlas data are typically collected in a large number of sampling units during a restricted timeframe and are often similar in nature to the information gathered from long-term monitoring projects. Th e large number of sampling units in atlas projects makes it possible to simulate a broad gradient of sample sizes in monitoring data and to examine how the number of sampling units infl uences the accuracy of the models. We apply the method to several bird species using data from a regional breeding bird atlas. We explore the eff ect of prevalence, range size and habitat specialization of the species on the sample size needed to generate accurate models. Model accuracy is sensitive to particularly small sample sizes and levels off beyond a suffi ciently large number of sampling units that varies among species depending mainly on their prevalence. Th e integration of spatial modelling techniques into monitoring projects is a cost-eff ective approach as it off ers the possibility to estimate the dynamics of species distributions in space and over time. We believe our innovative method will help in the sampling design of future monitoring projects ai...

Integrating intraseasonal grassland dynamics in cross‐scale distribution modeling to support waterbird recovery plans

“…Among several applications, SDMs can be used to anticipate the impacts of environmental drivers on species distribution (Elith et al 2010), a critical step for effective conservation. Such models can also help identify priority areas for conserva-tion (Arcos et al 2012) or optimize long-term monitoring protocols (Aizpurua et al 2015), which are especially important for species at risk. Ideally, a good recovery plan requires adaptive management (i.e., information obtained on management effectiveness through monitoring of outcomes) (McCarthy & Possingham 2007), with the aim of updating knowledge and improving decision making over time (Canessa et al 2016).…”

Spatial models to account for variation in observer effort in bird atlases

“…Species distribution models are algorithms that “identify a mathematical or logical function linking species' occurrences and a set of predictors” (Kamino et al., ). A multitude of different SDMs have been developed and applied to ecological data (e.g., Aizpurua, Paquet, Brotons, & Titeux, ; Comte & Grenouillet, ; Elith, Kearney, & Phillips, ; Rocchini et al., ), but accounting for false negatives is a persistent challenge (Chefaoui & Lobo, ; Kéry, ; Rocchini et al., ). A failure to detect a species in a given area could be because the habitat is not suitable, or it could merely be due to insufficient survey effort or inappropriate survey protocols.…”

Reconciling expert judgement and habitat suitability models as tools for guiding sampling of threatened species

“…It is therefore highly important to define the most efficient sampling strategies to minimize costs and maximize gains in knowledge (Aizpurua et al . ). The distribution of species of conservation‐concern may be geographically limited due to their restricted habitat requirements or population sizes.…”