Biased data reduce efficiency and effectiveness of conservation reserve networks

Occurrence-based diversity estimation reveals macroecological and conservation knowledge gaps for global woody plants

Incomplete sampling of species' geographic distributions has challenged biogeographers for many years to precisely quantify global-scale biodiversity patterns. After correcting for the spatial inequality of sample completeness, we generated a global species diversity map for woody angiosperms (82,974 species, 13,959,780 occurrence records). The standardized diversity estimated more pronounced latitudinal and longitudinal diversity gradients than the raw data and improved the spatial prediction of diversity based on environmental factors. We identified areas with potentially high species richness and rarity that are poorly explored, unprotected, and threatened by increasing human pressure: They are distributed mostly at low latitudes across central South America, Central Africa, subtropical China, and Indomalayan islands. These priority areas for botanical exploration can help to efficiently fill spatial knowledge gaps for better describing the status of biodiversity and improve the effectiveness of the protected area network for global woody plant conservation.

Temporal changes in the potential geographic distribution of Histiotus velatus (Chiroptera, Vespertilionidae), the "decade effect"

We investigated how the potential distribution of Histiotus velatus is affected by the addition of new records over decades (decade effect). Assuming that (1: hypothesis of the effect of the decade) the addition of new occurrence records over time increases the potential size of the species distribution; and (2: Wallacean distance hypothesis) over the years, the new points added are increasingly distant from the research centers. Considering the geographic knowledge gap of this species, our objective is to report a new record of this species and estimate its potential distribution in South America through environment niche models (ENMs). For this, we compiled records of occurrence of species, selected from 1900 to 2015. We used 19 bioclimatic variables available in the WorldClim database to estimate the potential distribution of the species, and we used three modeling algorithms: Maximum Entropy (MXT), Random Forest (RDF), and Support Vector Machine. To test the Wallacean distance hypothesis, we calculated the Euclidian distance from occurrences to bat research centers in Brazil, located using a national researchers' information dataset ("Plataforma Lattes"). To test the hypothesis of the decade effect, we used the beta regression analysis, taking conservative and non-conservative approaches. The results showed that the predicted area expanded and retracted with the addition of new occurrences over the decades, with an improvement in the accuracy of models. Most records are located in the southeastern region of Brazil, but algorithms predicted areas in regions where there are no records. Only the conservative approach has had a positive relationship over the decades. The distance from new points does not increase over the years of research centers.

Mapping Africa's Biodiversity: More of the Same Is Just Not Good Enough

Species distribution data are fundamental to the understanding of biodiversity patterns and processes. Yet, such data are strongly affected by sampling biases, mostly related to site accessibility. The understanding of these biases is therefore crucial in systematics, biogeography, and conservation. Here we present a novel approach for quantifying sampling effort and its impact on biodiversity knowledge, focusing on Africa. In contrast to previous studies assessing sampling completeness (percentage of species recorded in relation to predicted), we investigate whether the lack of knowledge of a site attracts scientists to visit these areas and collect samples of species. We then estimate the time required to sample 90% of the continent under a Weibull distributed biodiversity sampling rate and the number of sampling events required to record $ \ge $50% of the species. Using linear and spatial regression models, we show that previous sampling has been strongly influencing the resampling of areas, attracting repeated visits. This bias has existed for over two centuries, has increased in recent decades, and is most pronounced among mammals. It may take between 172 and 274 years, depending on the group, to achieve at least one sampling event per grid cell in the entire continent. Just one visit will, however, not be enough: in order to record $ \ge $50% of the current diversity, it will require at least 12 sampling events for amphibians, 13 for mammals, and 27 for birds. Our results demonstrate the importance of sampling areas that lack primary biodiversity data and the urgency with which this needs to be done. Current practice is insufficient to adequately classify and map African biodiversity; it can lead to incorrect conclusions being drawn from biogeographic analyses and can result in misleading and self-reinforcing conservation priorities. [Amphibians; birds; mammals; sampling bias; sampling gaps; Wallacean shortfall.].

Spatial conservation prioritisation in data-poor countries: a quantitative sensitivity analysis using multiple taxa

Background

Spatial conservation prioritisation (SCP) is a set of computational tools designed to support the efficient spatial allocation of priority areas for conservation actions, but it is subject to many sources of uncertainty which should be accounted for during the prioritisation process. We quantified the sensitivity of an SCP application (using software Zonation) to possible sources of uncertainty in data-poor situations, including the use of different surrogate options; correction for sampling bias; how to integrate connectivity; the choice of species distribution modelling (SDM) algorithm; how cells are removed from the landscape; and two methods of assigning weights to species (red-list status or prediction uncertainty). Further, we evaluated the effectiveness of the Egyptian protected areas for conservation, and spatially allocated the top priority sites for further on-the-ground evaluation as potential areas for protected areas expansion.

Results

Focal taxon (butterflies, reptiles, and mammals), sampling bias, connectivity and the choice of SDM algorithm were the most sensitive parameters; collectively these reflect data quality issues. In contrast, cell removal rule and species weights contributed much less to overall variability. Using currently available species data, we found the current effectiveness of Egypt’s protected areas for conserving fauna was low.

Conclusions

For SCP to be useful, there is a lower limit on data quality, requiring data-poor countries to improve sampling strategies and data quality to obtain unbiased data for as many taxa as possible. Since our sensitivity analysis may not generalise, conservation planners should use sensitivity analyses more routinely, particularly relying on more than one combination of SDM algorithm and surrogate group, consider correction for sampling bias, and compare the spatial patterns of predicted priority sites using a variety of settings. The sensitivity of SCP to connectivity parameters means that the responses of each species to habitat loss are important knowledge gaps.

Addressing distribution equity in spatial conservation prioritization for small-scale fisheries

Spatial conservation prioritization is used worldwide for designing marine protected areas (MPA) that achieve set conservation objectives with minimal impacts to marine users. People involved in small-scale fisheries (SSF) may incur negative and disproportionate impacts from implementing MPAs, yet limited available data often restricts their representation in MPA planning. Using a Philippines case study, we focus here on the systematic design of a MPA network that aims to minimize and distribute costs equitably for SSF whilst achieving representation targets for biodiversity conservation. The objectives of the study are to: (1) document a participatory mapping approach for collecting SSF data for prioritization using the local knowledge of fishers; and (2) examine how the completeness and resolution of SSF data may affect prioritization outputs in terms of biodiversity representation, spatial efficiency, and distribution equity. In the data-poor region, we conducted participatory mapping workshops with fishers in 79 communities to collect data on the spatial distribution patterns of different SSF fisheries and communities, and employed remote sensing techniques to define coastal habitats, which were targeted for inclusion in MPAs. The datasets were integrated within the decision-support tool Marxan with Zones to develop three scenarios. The SSF data incorporated in each scenario varied based on their completeness (considered all fishing methods or only dominant methods) and resolution (fishing methods itemized by community or municipality). All scenarios derived MPA plans that met representation targets with similar area coverage. The outputs, however, varied in terms of distribution equity, measured by the distribution of opportunity costs (loss of fishing grounds) across different fisheries and communities. Scenarios that did not include minority fisheries or variations between communities, led to inequitable costs. These results highlight the need to incorporate detailed data on SSF at appropriate resolutions, and how this can be achieved through participatory approaches.

Modelling Highly Biodiverse Areas in Brazil

Traditional conservation techniques for mapping highly biodiverse areas assume there to be satisfactory knowledge about the geographic distribution of biodiversity. There are, however, large gaps in biological sampling and hence knowledge shortfalls. This problem is even more pronounced in the tropics. Indeed, the use of only a few taxonomic groups or environmental surrogates for modelling biodiversity is not viable in mega-diverse countries, such as Brazil. To overcome these limitations, we developed a comprehensive spatial model that includes phylogenetic information and other several biodiversity dimensions aimed at mapping areas with high relevance for biodiversity conservation. Our model applies a genetic algorithm tool for identifying the smallest possible region within a unique biota that contains the most number of species and phylogenetic diversity, as well as the highest endemicity and phylogenetic endemism. The model successfully pinpoints small highly biodiverse areas alongside regions with knowledge shortfalls where further sampling should be conducted. Our results suggest that conservation strategies should consider several taxonomic groups, the multiple dimensions of biodiversity, and associated sampling uncertainties.

Opportunistic citizen science data transform understanding of species distributions, phenology, and diversity gradients for global change research

Opportunistic citizen science (CS) programs allow volunteers to report species observations from anywhere, at any time, and can assemble large volumes of historic and current data at faster rates than more coordinated programs with standardized data collection. This can quickly provide large amounts of species distributional data, but whether this focus on participation comes at a cost in data quality is not clear. Although automated and expert vetting can increase data reliability, there is no guarantee that opportunistic data will do anything more than confirm information from professional surveys. Here, we use eButterfly, an opportunistic CS program, and a comparable dataset of professionally collected observations, to measure the amount of new distributional species information that opportunistic CS generates. We also test how well opportunistic CS can estimate regional species richness for a large group of taxa (>300 butterfly species) across a broad area. We find that eButterfly contributes new distributional information for >80% of species, and that opportunistically submitting observations allowed volunteers to spot species ~35 days earlier than professionals. Although eButterfly did a relatively poor job at predicting regional species richness by itself (detecting only about 35-57% of species per region), it significantly contributed to regional species richness when used with the professional dataset (adding ~3 species that had gone undetected in professional surveys per region). Overall, we find that the opportunistic CS model can provide substantial complementary species information when used alongside professional survey data. Our results suggest that data from opportunistic CS programs in conjunction with professional datasets can strongly increase the capacity of researchers to estimate species richness, and provide unique information on species distributions and phenologies that are relevant to the detection of the biological consequences of global change.

Conservation priorities for terrestrial mammals in Dobrogea Region, Romania

Based on species occurrence records of museum collections, published literature, and unpublished records shared by mammalian experts, we compiled a distribution database for 59 terrestrial mammals populating the extensively protected Dobrogea Region of Romania. The spatial patterns of mammal distribution and diversity was evaluated and systematic conservation planning applied to identify priority areas for their conservation. The spatial analyses revealed that intensive sampling was not directly correlated to mammal diversity but rather to accessibility for inventory. The spatial prioritisation analysis indicated a relatively aggregated pattern of areas with a high or low conservation value with virtually no connecting corridors between them. The significant overlap between Natura 2000 sites and national protected areas induced an over-optimistic vision of the effectiveness and representativeness of existing Natura 2000 network for species found in Annexes II and IV of the Habitats Directive. These results represent a key step in identifying core areas for the protection of mammal diversity and dispersal corridors for improved connectivity, and to guide future conservation efforts in increasing the effectiveness of the existing protected areas in the context of environmental changes.

Understanding the Processes Underpinning Patterns of Phylogenetic Regionalization

A key step in understanding the distribution of biodiversity is the grouping of regions based on their shared elements. Historically, regionalization schemes have been largely species centric. Recently, there has been interest in incorporating phylogenetic information into regionalization schemes. Phylogenetic regionalization can provide novel insights into the mechanisms that generate, distribute, and maintain biodiversity. We argue that four processes (dispersal limitation, extinction, speciation, and niche conservatism) underlie the formation of species assemblages into phylogenetically distinct biogeographic units. We outline how it can be possible to distinguish among these processes, and identify centers of evolutionary radiation, museums of diversity, and extinction hotspots. We suggest that phylogenetic regionalization provides a rigorous and objective classification of regional diversity and enhances our knowledge of biodiversity patterns.

Deficiencies in Natura 2000 for protecting recovering large carnivores: A spotlight on the wolf Canis lupus in Poland

If protected areas are to remain relevant in our dynamic world they must be adapted to changes in species ranges. In the EU one of the most notable such changes is the recent recovery of large carnivores, which are protected by Natura 2000 at the national and population levels. However, the Natura 2000 network was designed prior to their recent recovery, which raises the question whether the network is sufficient to protect the contemporary ranges of large carnivores. To investigate this question we evaluated Natura 2000 coverage of the three wolf Canis lupus populations in Poland. Wolf tracking data showed that wolves have recolonised almost all suitable habitat in Poland (as determined by a recent habitat suitability model), so we calculated the overlap between the Natura 2000 network and all wolf habitat in Poland. On the basis of published Natura 2000 criteria, we used 20% as the minimum required coverage. At the national level, wolves are sufficiently protected (22% coverage), but at the population level, the Baltic and Carpathian populations are far better protected (28 and 47%, respectively) than the endangered Central European Lowland population (12%). As Natura 2000 insufficiently protects the most endangered wolf population in Poland, we recommend expansion of Natura 2000 to protect at least an additional 8% of wolf habitat in western Poland, and discuss which specific forests are most in need of additional coverage. Implementation of these actions will have positive conservation implications and help Poland to fulfil its Habitats Directive obligations. As it is likely that similar gaps in Natura 2000 are arising in other EU member states experiencing large carnivore recoveries, particularly in Central Europe, we make the case for a flexible approach to Natura 2000 and suggest that such coverage evaluations may be beneficial elsewhere.

Satellite tagging of Mediterranean fin whales: working towards the identification of critical habitats and the focussing of mitigation measures

Mediterranean fin whales comprise a genetically distinct population, listed as Vulnerable (VU) in the IUCN Red List. Collisions with vessels are believed to represent the main cause of human-induced mortality. The identification of critical habitats (including migration routes) incorporating satellite telemetry data is therefore crucial to develop focussed conservation efforts. Between 2012 and 2015 thirteen fin whales were equipped with satellite transmitters, 8 in the Pelagos Sanctuary (although two ceased within two days) and 5 in the Strait of Sicily, to evaluate movements and habitat use. A hierarchical switching state-space model was used to identify transiting and area-restricted search (ARS) behaviours, believed to indicate foraging activities. All whales undertook mid- to long-distance migrations, crossing some of the world's busiest maritime routes. Areas where the animals predominantly engaged in ARS behaviour were identified in both study areas. The telemetry data were compared with results from ecosystem niche modelling, and showed that 80% of tagged whale positions was near (<7 km) the closest suitable habitat. The results contribute to the view that precautionary management should include establishment of a coordinated and dynamic basin-wide management scheme; if appropriate, this may include the establishment of protected areas by specific regional Conventions.

Trade-offs between data resolution, accuracy, and cost when choosing information to plan reserves for coral reef ecosystems

Conservation planners must reconcile trade-offs associated with using biodiversity data of differing qualities to make decisions. Coarse habitat classifications are commonly used as surrogates to design marine reserve networks when fine-scale biodiversity data are incomplete or unavailable. Although finely-classified habitat maps provide more detail, they may have more misclassification errors, a common problem when remotely-sensed imagery is used. Despite these issues, planners rarely consider the effects of errors when choosing data for spatially explicit conservation prioritizations. Here we evaluate trade-offs between accuracy and resolution of hierarchical coral reef habitat data (geomorphology and benthic substrate) derived from remote sensing, in spatial planning for Kubulau District, Fiji. For both, we use accuracy information describing the probability that a mapped habitat classification is correct to design marine reserve networks that achieve habitat conservation targets, and demonstrate inadequacies of using habitat maps without accuracy data. We show that using more detailed habitat information ensures better representation of biogenic habitats (i.e. coral and seagrass), but leads to larger and more costly reserves, because these data have more misclassification errors, and are also more expensive to obtain. Reduced impacts on fishers are possible using coarsely-classified data, which are also more cost-effective for planning reserves if we account for data collection costs, but using these data may under-represent reef habitats that are important for fisheries and biodiversity, due to the maps low thematic resolution. Finally, we show that explicitly accounting for accuracy information in decisions maximizes the chance of successful conservation outcomes by reducing the risk of missing conservation representation targets, particularly when using finely classified data.

Vulnerability of ecosystems to climate change moderated by habitat intactness

The combined effects of climate change and habitat loss represent a major threat to species and ecosystems around the world. Here, we analyse the vulnerability of ecosystems to climate change based on current levels of habitat intactness and vulnerability to biome shifts, using multiple measures of habitat intactness at two spatial scales. We show that the global extent of refugia depends highly on the definition of habitat intactness and spatial scale of the analysis of intactness. Globally, 28% of terrestrial vegetated area can be considered refugia if all natural vegetated land cover is considered. This, however, drops to 17% if only areas that are at least 50% wilderness at a scale of 48×48 km are considered and to 10% if only areas that are at least 50% wilderness at a scale of 4.8×4.8 km are considered. Our results suggest that, in regions where relatively large, intact wilderness areas remain (e.g. Africa, Australia, boreal regions, South America), conservation of the remaining large-scale refugia is the priority. In human-dominated landscapes, (e.g. most of Europe, much of North America and Southeast Asia), focusing on finer scale refugia is a priority because large-scale wilderness refugia simply no longer exist. Action to conserve such refugia is particularly urgent since only 1 to 2% of global terrestrial vegetated area is classified as refugia and at least 50% covered by the global protected area network.

A novel method to handle the effect of uneven sampling effort in biodiversity databases

How reliable are results on spatial distribution of biodiversity based on databases? Many studies have evidenced the uncertainty related to this kind of analysis due to sampling effort bias and the need for its quantification. Despite that a number of methods are available for that, little is known about their statistical limitations and discrimination capability, which could seriously constrain their use. We assess for the first time the discrimination capacity of two widely used methods and a proposed new one (FIDEGAM), all based on species accumulation curves, under different scenarios of sampling exhaustiveness using Receiver Operating Characteristic (ROC) analyses. Additionally, we examine to what extent the output of each method represents the sampling completeness in a simulated scenario where the true species richness is known. Finally, we apply FIDEGAM to a real situation and explore the spatial patterns of plant diversity in a National Park. FIDEGAM showed an excellent discrimination capability to distinguish between well and poorly sampled areas regardless of sampling exhaustiveness, whereas the other methods failed. Accordingly, FIDEGAM values were strongly correlated with the true percentage of species detected in a simulated scenario, whereas sampling completeness estimated with other methods showed no relationship due to null discrimination capability. Quantifying sampling effort is necessary to account for the uncertainty in biodiversity analyses, however, not all proposed methods are equally reliable. Our comparative analysis demonstrated that FIDEGAM was the most accurate discriminator method in all scenarios of sampling exhaustiveness, and therefore, it can be efficiently applied to most databases in order to enhance the reliability of biodiversity analyses.

Global coverage of cetacean line-transect surveys: status quo, data gaps and future challenges

Knowledge of abundance, trends and distribution of cetacean populations is needed to inform marine conservation efforts, ecosystem models and spatial planning. We compiled a geo-spatial database of published data on cetacean abundance from dedicated visual line-transect surveys and encoded >1100 abundance estimates for 47 species from 430 surveys conducted worldwide from 1975-2005. Our subsequent analyses revealed large spatial, temporal and taxonomic variability and gaps in survey coverage. With the exception of Antarctic waters, survey coverage was biased toward the northern hemisphere, especially US and northern European waters. Overall, <25% of the world's ocean surface was surveyed and only 6% had been covered frequently enough (≥ 5 times) to allow trend estimation. Almost half the global survey effort, defined as total area (km(2)) covered by all survey study areas across time, was concentrated in the Eastern Tropical Pacific (ETP). Neither the number of surveys conducted nor the survey effort had increased in recent years. Across species, an average of 10% of a species' predicted range had been covered by at least one survey, but there was considerable variation among species. With the exception of three delphinid species, <1% of all species' ranges had been covered frequently enough for trend analysis. Sperm whales emerged from our analyses as a relatively data-rich species. This is a notoriously difficult species to survey visually, and we use this as an example to illustrate the challenges of using available data from line-transect surveys for the detection of trends or for spatial planning. We propose field and analytical methods to fill in data gaps to improve cetacean conservation efforts.

Complete, accurate, mammalian phylogenies aid conservation planning, but not much

In the face of unprecedented global biodiversity loss, conservation planning must balance between refining and deepening knowledge versus acting on current information to preserve species and communities. Phylogenetic diversity (PD), a biodiversity measure that takes into account the evolutionary relationships between species, is arguably a more meaningful measure of biodiversity than species diversity, but cannot yet be applied to conservation planning for the majority of taxa for which phylogenetic trees have not yet been developed. Here, we investigate how the quality of data on the taxonomy and/or phylogeny of species affects the results of spatial conservation planning in terms of the representation of overall mammalian PD. The results show that the better the quality of the biodiversity data the better they can serve as a basis for conservation planning. However, decisions based on incomplete data are remarkably robust across different levels of degrading quality concerning the description of new species and the availability of phylogenetic information. Thus, given the level of urgency and the need for action, conservation planning can safely make use of the best available systematic data, limited as these data may be.

Regression analysis of spatial data

Many of the most interesting questions ecologists ask lead to analyses of spatial data. Yet, perhaps confused by the large number of statistical models and fitting methods available, many ecologists seem to believe this is best left to specialists. Here, we describe the issues that need consideration when analysing spatial data and illustrate these using simulation studies. Our comparative analysis involves using methods including generalized least squares, spatial filters, wavelet revised models, conditional autoregressive models and generalized additive mixed models to estimate regression coefficients from synthetic but realistic data sets, including some which violate standard regression assumptions. We assess the performance of each method using two measures and using statistical error rates for model selection. Methods that performed well included generalized least squares family of models and a Bayesian implementation of the conditional auto-regressive model. Ordinary least squares also performed adequately in the absence of model selection, but had poorly controlled Type I error rates and so did not show the improvements in performance under model selection when using the above methods. Removing large-scale spatial trends in the response led to poor performance. These are empirical results; hence extrapolation of these findings to other situations should be performed cautiously. Nevertheless, our simulation-based approach provides much stronger evidence for comparative analysis than assessments based on single or small numbers of data sets, and should be considered a necessary foundation for statements of this type in future.

Identifying conservation areas on the basis of alternative distribution data sets

Distribution data on biodiversity features is a major component of conservation planning that are often inaccurate; thus, the true distribution of each feature is commonly over- or underrepresented. The selection of distribution data sets may therefore lead to variability in the spatial configuration and size of proposed reserve networks and uncertainty regarding the extent to which these networks actually contain the biodiversity features they were identified to protect. Our goals were to investigate the impact on reserve selection of choosing different distribution data sets and to propose novel methods to minimize uncertainty about target attainment within reserves. To do so, we used common prioritization methods (richness mapping, systematic reserve design, and a novel approach that integrates multiple types of distribution data) and three types of data on the distribution of mammals (predicted distribution models, occurrence records, and a novel combination of the two) to simulate the establishment of regional biodiversity reserves for the state of Arizona (U.S.A.). Using the results of these simulations, we explored variability in reserve placement and size as a function of the distribution data set. Spatial overlap of reserve networks identified with only predicted distribution data or only occurrence distribution data never exceeded 16%. In pairwise comparisons between reserves created with all three types of distribution data, overlap never achieved 50%. The reserve size required to meet conservation targets also varied with the type of distribution data used and the conservation goal; the largest reserve system was 10 times the smallest. Our results highlight the impact of employing different types of distribution data and identify novel tools for application to existing distribution data sets that can minimize uncertainty about target attainment.

Utility of measuring abundance versus consistent occupancy in predicting biodiversity persistence

The primary goals of reserve selection are to represent all chosen units of biodiversity and to ensure their long-term persistence while minimizing costs. We considered two simple proxies of species persistence: a time series of point-count data to calculate abundance and a time series of presence-absence data to calculate permanence (a measure of consistent occupancy over time). Using two 10-year intervals of data from the North American Breeding Bird Survey, we compared the performance of each measure at predicting persistence 18 years later. For nonrare species, abundance and permanence predicted persistence similarly well. We performed complementarity-based reserve selections with data on species abundance and permanence (from 1970 to 1979) and then evaluated the effectiveness of the reserve networks at maintaining species populations and efficiency in land use (data from 1997 to 2006). Abundance proved a better predictor of future local persistence than permanence, which justifies the relatively larger financial and temporal costs of collecting a time series of point-count data to estimate abundance. If future extinction events were used as a measure of reserve-network effectiveness, the performance of abundance and permanence did not differ markedly. Nevertheless, when future abundance, which is a more sensitive measure of network effectiveness, was used, abundance was significantly better than permanence at selecting longer-term, high-quality, species-specific habitat but required larger reserves to do so.