Progress in grassland cover conservation in southern European mountains by 2020: a transboundary assessment in the Iberian Peninsula with satellite observations (2002–2019)

Conservation and policy agendas, such as the European Biodiversity strategy, Aichi biodiversity (target 5) and Common Agriculture Policy (CAP), are overlooking the progress made in mountain grassland cover conservation by 2020, which has significant socio-ecological implications to Europe. However, because the existing data near 2020 is scarce, the shifting character of mountain grasslands remains poorly characterized, and even less is known about the conservation outcomes because of different governance regimes and map uncertainty. Our study used Landsat satellite imagery over a transboundary mountain region in the northwestern Iberian Peninsula (Peneda-Gerês) to shed light on these aspects. Supervised classifications with a multiple classifier ensemble approach (MCE) were performed, with post classification comparison of maps established and bias-corrected to identify the trajectory in grassland cover, including protected and unprotected governance regimes. By analysing class-allocation (Shannon entropy), creating 95% confidence intervals for the area estimates, and evaluating the class-allocation thematic accuracy relationship, we characterized uncertainty in the findings. The bias-corrected estimates suggest that the positive progress claimed internationally by 2020 was not achieved. Our null hypothesis to declare a positive progress (at least equality in the proportion of grassland cover of 2019 and 2002) was rejected (X2 = 1972.1, df = 1, p p = 0.0001, n = 708) suggesting a relationship between the quality of pixel assignment and thematic accuracy. We therefore encourage a post-2020 conservation and policy action to safeguard mountain grasslands by enhancing the role of protected governance regimes. To reduce uncertainty, grassland gain mapping requires additional remote sensing research to find the most adequate spatial and temporal data resolution to retrieve this process.This work was supported by the Portuguese FCT—Fundação para a Ciência e Teconologia in the framework of the ATM Junior researcher contract DL57/2016/CP1442/CP0005 and funding attributed to CEG-IGOT Research Unit (UIDB/00295/2020 and UIDP/00295/2020). Claudia Carvalho-Santos is supported by the “Contrato-Programa” UIDP/04050/2020 funded by FCT. We also acknowledge ECOPOTENTIAL (Improving Future Ecosystem Benefits Through Earth Observations)— European framework programme H2020 for research and innovation- grant agreement Nº 641762

Bio-Ecological Diversity vs. Socio-Economic Diversity: A Comparison of Existing Measures

This paper aims to enrich the standard toolbox for measuring diversity in economics. In so doing, we compare the indicators of diversity used by economists with those used by biologists and ecologists. Ecologists and biologists are concerned about biodiversity: the diversity of organisms that inhabit a given area. Concepts of species diversity such as alpha (diversity within community), beta (diversity across communities) and gamma (diversity due to differences among samples when they are combined into a single sample) have been developed (Whittaker, 1960). Biodiversity is more complex than just the species that are present, it includes species richness and species evenness. Those various aspects of diversity are measured by biodiversity indices such as Simpson’s Diversity Indices, Species Richness Index, Shannon Weaver Diversity Indices, Patil and Taillie Index, Modified Hill’s Ratio. In economics, diversity measures are multi-faceted ranging from inequality (Lorenz curve, Gini coefficient, quintile distribution), to polarisation (Esteban and Ray, 1994; Wolfon, 1994, D’Ambrosio (2001)) and heterogeneity (Alesina, Baqir and Hoxby, 2000). We propose an interdisciplinary comparison between indicators. We review their theoretical background and applications. We provide an assessment of their possible use according to their specific properties.Diversity, Growth, Knowledge

Can soundscape indices be used to reflect biodiversity in an Ecuadorian Andean tropical montane habitat?

The measurement of biodiversity presents a challenging task for conservationists. Traditional ecological descriptors such as species richness, Shannon and Simpson diversities have long been relied on by ecologists to provide an indication of biodiversity, however, the generation of these indices requires manual assessments of local fauna through methods which can often be expensive, time consuming and invasive. Analysis of the Soundscape, the collection of sounds generated by organisms (biophony), humans (anthrophony) and non-biological sounds such as wind and water (geophony), presents new potential for biodiversity sampling. To date, a range of indices have been developed that aim to provide a value relating to acoustic characteristics of an environment, based on the spectral and temporal properties within sound recordings. Several studies have yielded promising results when using the acoustic indices for biodiversity assessments in temperate, and to some extent, tropical habitats. We here aimed to further the current understanding of soundscape indices by investigating how they behave in a tropical habitat of high avifaunal diversity. Point count data for bird species was collected from the Santa Lucia Cloud Forest Reserve, NW Ecuador (0°17'30"N, 78°40'30"W), during Summer 2014, while acoustic recordings were taken simultaneously using a handheld audio recorder. We first analysed the change in bird species composition and diversity along two environmental gradients of habitat disturbance and altitude, and investigated how the acoustic indices behaved along these same gradients. We then explored whether the acoustic indices can be used to estimate biodiversity based on their correlation with traditional ecological indices. From the point count data we found a high turnover in species composition across sample sites. Mean species richness was found to negatively correlate with increasing altitude (p=1.485e-05) and was found to be significantly lower in sites of primary forest than in both secondary forest and silvopasture (p=0.0165). Although each of the acoustic indices showed varying levels of change across the environmental gradients, we found that only four of the eight acoustic indices, the Normalized Difference Soundscape Index (NDSI, p=0.048), spectral entropy Hs, p=0.033), temporal entropy (Ht, p=0.031), and total entropy (H, p=0.031), were found to significantly correlate with species composition. We found that spectral entropy (Hs) and total entropy (H) were the only acoustic indices to significantly correlate with ecological indices, however since the total entropy (H) is developed as a product of the spectral entropy (Hs) we suggest that only the H index should be utilised for biodiversity measurements. We found that while the acoustic indices have the potential to characterise certain features of a habitat or to assist in analysing bird species composition, their use for biodiversity assessment is limited. We would suggest that more work to develop and understand the indices is needed before we can reliably make use of them for measuring biodiversity

Quantitative Comparison of Abundance Structures of Generalized Communities: From B-Cell Receptor Repertoires to Microbiomes

The \emph{community}, the assemblage of organisms co-existing in a given space and time, has the potential to become one of the unifying concepts of biology, especially with the advent of high-throughput sequencing experiments that reveal genetic diversity exhaustively. In this spirit we show that a tool from community ecology, the Rank Abundance Distribution (RAD), can be turned by the new MaxRank normalization method into a generic, expressive descriptor for quantitative comparison of communities in many areas of biology. To illustrate the versatility of the method, we analyze RADs from various \emph{generalized communities}, i.e.\ assemblages of genetically diverse cells or organisms, including human B cells, gut microbiomes under antibiotic treatment and of different ages and countries of origin, and other human and environmental microbial communities. We show that normalized RADs enable novel quantitative approaches that help to understand structures and dynamics of complex generalize communities

Evaluating CAP alternative policy scenarios through a system dynamics approach in rural areas of Greece

Current considerations for the post-2013 CAP create the need for the investigation and evaluation of alternative CAP scenarios and their effects on agriculture, environment and regional development in EU rural areas. To this end, a system-dynamics model is developed and utilized to evaluate the impacts of alternative CAP scenarios in a Greek rural area (prefecture of Trikala). This particular model features four basic subsystems (agriculture, environment, regional economy and human resources) specified and analyzed through a linear programming model, a dynamic input-output model and an age-cohort demographic model, respectively. Four alternative policy scenarios are specified, dealing with possible developments on Pillars 1 and 2. Model simulations produce scenario-specific effects for the 2007-2013 period, and up to 2020 in the form of changes in land use and farm output, environmental indicators associated with farm activity, economy-wide impacts and impacts on local population. Results show that different future orientations for the CAP are associated with different impacts on agricultural activity, the environment and total economic activity in this area. A reduction of Pillar 1 funds and a dedication of Pillar 2 spending on Axis 2 generate negative effects on local agriculture, but benefit the local environment and economy-wide incomes. On the other hand, a more “productive” orientation of Pillar 2 positively affects local employment (compared to the current CAP) but does not create any positive or negative effects on the environment of this regionCAP, policy impact assessment, rural development, system dynamics, Agricultural and Food Policy, C61, C67, Q18, R58,

Sounding out ecoacoustic metrics: avian species richness is predicted by acoustic indices in temperate but not tropical habitats

Affordable, autonomous recording devices facilitate large scale acoustic monitoring and Rapid Acoustic Survey is emerging as a cost-effective approach to ecological monitoring; the success of the approach rests on the de- velopment of computational methods by which biodiversity metrics can be automatically derived from remotely collected audio data. Dozens of indices have been proposed to date, but systematic validation against classical, in situ diversity measures are lacking. This study conducted the most comprehensive comparative evaluation to date of the relationship between avian species diversity and a suite of acoustic indices. Acoustic surveys were carried out across habitat gradients in temperate and tropical biomes. Baseline avian species richness and subjective multi-taxa biophonic density estimates were established through aural counting by expert ornithol- ogists. 26 acoustic indices were calculated and compared to observed variations in species diversity. Five acoustic diversity indices (Bioacoustic Index, Acoustic Diversity Index, Acoustic Evenness Index, Acoustic Entropy, and the Normalised Difference Sound Index) were assessed as well as three simple acoustic descriptors (Root-mean-square, Spectral centroid and Zero-crossing rate). Highly significant correlations, of up to 65%, between acoustic indices and avian species richness were observed across temperate habitats, supporting the use of automated acoustic indices in biodiversity monitoring where a single vocal taxon dominates. Significant, weaker correlations were observed in neotropical habitats which host multiple non-avian vocalizing species. Multivariate classification analyses demonstrated that each habitat has a very distinct soundscape and that AIs track observed differences in habitat-dependent community composition. Multivariate analyses of the relative predictive power of AIs show that compound indices are more powerful predictors of avian species richness than any single index and simple descriptors are significant contributors to avian diversity prediction in multi-taxa tropical environments. Our results support the use of community level acoustic indices as a proxy for species richness and point to the potential for tracking subtler habitat-dependent changes in community composition. Recommendations for the design of compound indices for multi-taxa community composition appraisal are put forward, with consideration for the requirements of next generation, low power remote monitoring networks

Optimizing sampling effort and information content of biodiversity surveys: a case study of alpine grassland

Aims: Current rates of biodiversity loss do not allow for inefficient monitoring. Optimized monitoring maximizes the ratio between information and sampling effort (i.e., time and costs). Sampling effort increases with the number and size of sampling units. We hypothesize that an optimal size and number of sampling units can be determined providing maximal information via minimal effort. We apply an approach that identifies the optimal size and number of sampling quadrats. The approach can be adapted to any study system. Here we focus on alpine grassland, a diverse but threatened ecosystem. Location: Gran Paradiso National Park, Italy. Methods: We sampled nine 20 m 7 20 m-plots. Each plot consisted of 100 2 m 7 2 m-subplots. Species richness and Shannon diversity were quantified for different sizes and quantities of subplots. We simulated larger subplot sizes by unifying adjacent 2 m 7 2 m-subplots. Shannon's information entropy was used to quantify information content among richness and diversity values resulting from different subplot sizes and quantities. The optimal size and number of subplots is the lowest size and number of subplots returning maximal information. This optimal subplot size and number was determined by Mood's median test and segmented linear regression, respectively. Results: The information content among richness values increased with subplot size, irrespective of the number of subplots. Therefore, the largest subplot size available is the optimal size for information about richness. Information content among diversity values increased with subplot size if 18 or less subplots were considered, and decreased if at least 27 subplots were sampled. The subplot quantity consequently determined whether the smallest or largest subplot size available is the optimal size, and whether the optimal size can be generalized across richness and diversity. Given a 2 m 7 2 m size, we estimated an optimal quantity of 54. Given a size of 4 m 7 4 m, we estimated an optimal number of 36. The optimal number of plots can be generalized across both indices because it barely differed between the indices given a fixed subplot size. Conclusions: The information content among richness and diversity values depends on the sampling scale. Shannon's information entropy can be used to identify the optimal number and size of plots that return most information with least sampling effort. Our approach can be adapted to other study systems to create an efficient in-situ sampling design, which improves biodiversity monitoring and conservation under rapid environmental change

Development of indicators for assessment of green infrastructure for a territorial network of ecological stability

Landscape structure and biotic indicators have a significant role in assessing the green infrastructure of a landscape and design of a territorial ecological network. In this contribution, a methodological approach has been developed for assessing and defining indicators of current land use and biota that can be used for designing a territorial network of ecological stability. We used the assessment of ecological stability of the elements of the current landscape structure, an index of the ecological stability of a representative geo-ecosystem, the cumulative effect of high ecological stability landscape elements, and the Shannon Diversity Index (SHDI) to measure the degree of entropy, or landscape diversity. The assessment of biota was based on qualitative habitat field data and an evaluation of their overall nature conservation importance based on the type of land cover and habitats, the importance of habitats, their current conservation status, how many rare habitats are in a region, and how many vulnerable species are present in habitats. The assessment was applied on a local level, using the example of the Dolný Lopašov study area. The spatial distribution of green infrastructure is not balanced within the study area. The most significant elements of the ecological network consist of natural and semi-natural habitats that have a favourable conservation status. The Malé Karpaty Mountains, situated in the northern region, are forest-covered and have the highest ecological stability. Intensively cultivated fields are dominant in the central and southern parts of the study areas and are characterised by a low proportion of green infrastructure and low ecological stability. The results of the modelling of the cumulative impact of landscape elements on ecological stability by distance show that the cumulative impact of woodland elements positively affects the ecological stability of the area, especially in the area of intensively cultivated fields, an element with a low degree of ecological stability. Using selected indicators of current landscape structure and biota helps to assess the overall ecological stability of the area, identify the most stable areas, as well as areas with the lowest ecological stability, where it is necessary to complete and design new elements of green infrastructure to increase the function of the ecological network