1. Recent studies have unveiled drastic declines in the diversity and numbers of insects worldwide, unfolding over the last decades. These results have brought insects to the forefront of conservation attention, ranging from mitigation actions planned by dedicated conservation agencies to efforts undertaken by the general public. Further, the conservation focus has necessarily shifted from single (highly endangered) species to multi-species plans, but such a shift can become a daunting task when dealing with several dozens of species. In the last two decades powerful methods have emerged to describe and analyze populations inhabiting fragmented landscapes that are the dominating landscape type in most highly industrialized countries. Moreover, especially studies of butterfly species have enabled deep metapopulation insights, in theory and practice.
2. The quantitative framework I present consists of two connected components. The first aims at mapping a butterfly species list onto habitat patches in a landscape to be restored, uncovering the structure of the butterfly-specific metapopulations. This mapping relies mainly on larval host plant species and the vegetation types they inhabit, but it can accommodate additional resources that ultimately foster the species’ presence. Further, the mapping produces additional data that can conveniently be analyzed using methods from network theory, yielding ancillary insights for restoration planning. Subsequently, the second component aims at analyzing the metapopulations guided by metapopulation theory, to quantify the effect of available restoration actions on ecological and genetic aspects. The weighted, summarized results at the community level are now amenable to decision analysis that ultimately allows selecting the most effective and efficient strategies in a rational way, based on project objectives, strategies, and constraints. I hope the presented framework – as a whole or parts of it – may help inform and guide butterfly restoration projects.
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... One reason is that NHCs offer the unique opportunity to study long periods of time, e.g. a century or more, thus potentially allowing to study phenomena before massive anthropogenic effects started taking a considerable toll on nature (e.g. Bakker et al., 2020;Bozzuto, 2020;Laussmann et al., 2021;Lister, 2011;Shaffer et al., 1998;Theng et al., 2020). ...
Over the last two decades, many studies have emphasized the value of natural history collections (NHCs) for ecological and evolutionary research. Furthermore, with the current biodiversity crisis worsening by the day, these specimens offer invaluable insights into past changes, directly helping researchers to understand the current status and to propose apt measures to halt the decline of species and communities. In parallel, the digitization of collections continues being an on-going and pressing endeavor at the global scale. Several achievements are already impressive, with global databases meanwhile offering online access to many millions of specimen data. Unfortunately, especially the use of regional data for regional-scale studies is partly hampered by small- to medium-size natural history museums (NHMs) still awaiting digitalization. But even with completed digitization, many NHC data continue being only available by request. As an incentive for NHMs to more actively and rapidly share their data, in this study we present simple spatio-temporal analyses and visualizations helpful to display NHC data in a research-oriented way. The aim is to allow researchers a rapid online assessment of NHC data for their purposes. To this end, we propose the use – alone and combined – of (i) well-known indices from biodiversity research, (ii) cumulative and/or parametric representations of temporal data, and (iii) Voronoi tessellations and Delaunay triangulations for spatial data. As an illustrative example, we analyze butterfly collection data from a Swiss NHM. With today’s possibilities to quickly set up web applications and with the modest attribute requirements per specimen for our methods, we believe the implementation of these ideas will be affordable and quickly realizable, all the more – to the benefit of research – if NHMs share forces. The ideas and methods will also appeal to global initiatives ultimately aiming at offering access to the majority of NHCs. For the time being, our study may serve as a regional incentive encouraging NHMs to aid researchers generating much-needed knowledge on a rapidly changing natural world.
Emerging wildlife diseases are taking a heavy toll on animal and plant species worldwide. Mitigation, particularly in the initial epidemic phase, is hindered by uncertainty about the epidemiology and management of emerging diseases, but also by vague or poorly defined objectives. Here, we use a quantitative analysis to assess how the decision context of mitigation objectives, available strategies and practical constraints influences the decision of whether and how to respond to epidemics in wildlife. To illustrate our approach, we parametrized the model for European fire salamanders affected by Batrachochytrium salamandrivorans, and explored different combinations of conservation, containment and budgetary objectives. We found that in approximately half of those scenarios, host removal strategies perform equal to or worse than no management at all during a local outbreak, particularly where removal cannot exclusively target infected individuals. Moreover, the window for intervention shrinks rapidly if an outbreak is detected late or if a response is delayed. Clearly defining the decision context is, therefore, vital to plan meaningful responses to novel outbreaks. Explicitly stating objectives, strategies and constraints, if possible before an outbreak occurs, avoids wasting precious resources and creating false expectations about what can and cannot be achieved during the epidemic phase.
Recent reports of dramatic declines in insect abundance suggest grave consequences for global ecosystems and human society. Most evidence comes from Europe, however, leaving uncertainty about insect population trends worldwide. We used >5,300 time series for insects and other arthropods, collected over 4–36 years at monitoring sites representing 68 different natural and managed areas, to search for evidence of declines across the United States. Some taxa and sites showed decreases in abundance and diversity while others increased or were unchanged, yielding net abundance and biodiversity trends generally indistinguishable from zero. This lack of overall increase or decline was consistent across arthropod feeding groups and was similar for heavily disturbed versus relatively natural sites. The apparent robustness of US arthropod populations is reassuring. Yet, this result does not diminish the need for continued monitoring and could mask subtler changes in species composition that nonetheless endanger insect-provided ecosystem services.
The metacommunity concept has the potential to integrate local and regional dynamics within a general community ecology framework. To this end, the concept must move beyond the discrete archetypes that have largely defined it (e.g. neutral vs. species sorting) and better incorporate local scale species interactions and coexistence mechanisms. Here, we present a fundamental reconception of the framework that explicitly links local coexistence theory to the spatial processes inherent to metacommunity theory, allowing for a continuous range of competitive community dynamics. These dynamics emerge from the three underlying processes that shape ecological communities: (1) density‐independent responses to abiotic conditions, (2) density‐dependent biotic interactions and (3) dispersal. Stochasticity is incorporated in the demographic realisation of each of these processes. We formalise this framework using a simulation model that explores a wide range of competitive metacommunity dynamics by varying the strength of the underlying processes. Using this model and framework, we show how existing theories, including the traditional metacommunity archetypes, are linked by this common set of processes. We then use the model to generate new hypotheses about how the three processes combine to interactively shape diversity, functioning and stability within metacommunities. Here, we present a fundamental reconception of the metacommunity framework that explicitly links local coexistence theory to the spatial processes inherent to metacommunity theory, allowing for a continuous range of competitive community dynamics. These dynamics emerge from the three underlying processes that shape ecological communities: (1) density‐independent responses to abiotic conditions, (2) density‐dependent biotic interactions and (3) dispersal. Using a simulation model, we show how classic theories in community ecology are linked by the three common processes in our framework.
Local biodiversity trends over time are likely to be decoupled from global trends, as local processes may compensate or counteract global change. We analyze 161 long-term biological time series (15–91 years) collected across Europe, using a comprehensive dataset comprising ~6,200 marine, freshwater and terrestrial taxa. We test whether (i) local long-term biodiversity trends are consistent among biogeoregions, realms and taxonomic groups, and (ii) changes in biodiversity correlate with regional climate and local conditions. Our results reveal that local trends of abundance, richness and diversity differ among biogeoregions, realms and taxonomic groups, demonstrating that biodiversity changes at local scale are often complex and cannot be easily generalized. However, we find increases in richness and abundance with increasing temperature and naturalness as well as a clear spatial pattern in changes in community composition (i.e. temporal taxonomic turnover) in most biogeoregions of Northern and Eastern Europe. The global biodiversity decline might conceal complex local and group-specific trends. Here the authors report a quantitative synthesis of longterm biodiversity trends across Europe, showing how, despite overall increase in biodiversity metric and stability in abundance, trends differ between regions, ecosystem types, and taxa.
Metacommunity ecology combines local (e.g., environmental filtering and biotic interactions) and regional (e.g., dispersal and heterogeneity) processes to understand patterns of species abundance, occurrence, composition, and diversity across scales of space and time. As such, it has a great potential to generalize and synthesize our understanding of many ecological problems. Here, we give an overview of how a metacommunity perspective can provide useful insights for conservation biology, which aims to understand and mitigate the effects of anthropogenic drivers that decrease population sizes, increase extinction probabilities, and threaten biodiversity. We review four general metacommunity processes—environmental filtering, biotic interactions, dispersal, and ecological drift—and discuss how key anthropogenic drivers (e.g., habitat loss and fragmentation, and nonnative species) can alter these processes. We next describe how the patterns of interest in metacommunities (abundance, occupancy, and diversity) map onto issues at the heart of conservation biology, and describe cases where conservation biology benefits by taking a scale‐explicit metacommunity perspective. We conclude with some ways forward for including metacommunity perspectives into ideas of ecosystem functioning and services, as well as approaches to habitat management, preservation, and restoration. Metacommunity ecology combines local processes to understand patterns of species abundance, occurrence, composition, and diversity across scales of space and time. Here, we give an overview of how a metacommunity perspective can provide useful insights for conservation biology, which aims to understand and mitigate the effects of anthropogenic drivers that decrease population sizes, increase extinction probabilities, and threaten biodiversity.
Heterogeneity in quantity and quality of resources provided in the urban matrix may mitigate adverse effects of urbanization intensity on the structure of biotic communities. To assess this we quantified the spatial variation in butterfly richness and abundance along an impervious surface gradient using three measures of urban matrix quality: floral resource availability and origin (native vs exotic plants), tree cover, and the occurrence of remnant habitat patches. Butterfly richness and abundance were surveyed in 100 cells (500 x 500‐m), selected using a random‐stratified sampling design, across a continuous gradient of imperviousness in Melbourne, Australia. Sampling occurred twice during the butterfly flight season. Occurrence data were analyzed using generalized linear models at local and meso‐ scales. Despite high sampling completeness we did not detect 75% of species from the regional species pool in the urban area, suggesting that urbanization has caused a large proportion of the region’s butterflies to become absent or extremely rare within Melbourne’s metro‐area. Those species that do remain are largely very generalist in their choice of larval host plants. Butterfly species richness and abundance declined with increasing impervious surface cover and, contrary to evidence for other taxa, there was no evidence that richness peaked at intermediate levels of urbanization. Declines in abundance appeared to be more noticeable when impervious surface cover exceeded 25%, while richness declined linearly with increasing impervious surface cover. We find evidence that the quality of the urban matrix (floral resources and remnant vegetation) influenced butterfly richness and abundance although the effects were small. Total butterfly abundance responded negatively to exotic floral abundance early in the sampling season and positively to total floral abundance later in the sampling season. Butterfly species richness increased with tree cover. Negative impacts of increased urbanization intensity on butterfly species richness and abundance may be mitigated to some extent by improving the quality of the urban matrix by enhancing tree cover and the provision of floral resources – with some evidence that native plants are more effective.
Adult flower‐visiting insects feed on nectar and pollen and partly collect floral resources to feed their larvae. The reduction in food availability has therefore been proposed as one of the main causes for the drastic decline in flower‐visiting insects in Central Europe. We compared the current (2012–2017) abundances of food plants of different groups of flower‐visiting insects to that of 1900–1930 in the canton of Zurich, Switzerland. Comparisons were done separately for different vegetation types, flowering months, and groups of diurnal flower‐visiting insects, such as bees, bumblebees, wasps, butterflies, hoverflies, flies, and beetles. We found a general decrease in food plant abundance for all groups of flower‐visiting insects and in all vegetation types except ruderal areas. Reductions of food plant abundance were most pronounced for wetlands and agricultural fields, reflecting the massive transformation of wetlands into other habitat types and the intensified management of agricultural fields. Food plant abundance for specialized flower visitors (bees, bumblebees, butterflies) of wetlands decreased most strongly in May and for generalized flower visitors (wasps, hoverflies, flies, beetles) in July. Specialized plant species, i.e. species with few groups of flower visitors, decreased more strongly in abundance than species with many groups of flower visitors. Finally, we found a homogenization of food plant assemblages in all vegetation types except ruderal areas, where the opposite pattern emerged. Our results suggest a significant reduction in the diversity and abundance of food plants for flower‐visiting insects over the past century, which has been most severe for the more specialized insect groups. The trend of insect decline, in particular those specialized on few plant species, can only be stopped by extending suitable habitats, i.e. by increasing food availability and re‐establish selected plant populations.
Aerial web-spinning spiders (including large orb-weavers), as a group, depend almost entirely on flying insects as a food source. The recent widespread loss of flying insects across large parts of western Europe, in terms of both diversity and biomass, can therefore be anticipated to have a drastic negative impact on the survival and abundance of this type of spider. To test the putative importance of such a hitherto neglected trophic cascade, a survey of population densities of the European garden spider Araneus diadematus—a large orb-weaving species—was conducted in the late summer of 2019 at twenty sites in the Swiss midland. The data from this survey were compared with published population densities for this species from the previous century. The study verified the above-mentioned hypothesis that this spider’s present-day overall mean population density has declined alarmingly to densities much lower than can be expected from normal population fluctuations (0.7% of the historical values). Review of other available records suggested that this pattern is widespread and not restricted to this region. In conclusion, the decline of this once so abundant spider in the Swiss midland is evidently revealing a bottom-up trophic cascade in response to the widespread loss of flying insect prey in recent decades.
Local drivers of decline matter
Recent studies have reported alarming declines in insect populations, but questions persist about the breadth and pattern of such declines. van Klink et al. compiled data from 166 long-term surveys across 1676 globally distributed sites and confirmed declines in terrestrial insects, albeit at lower rates than some other studies have reported (see the Perspective by Dornelas and Daskalova). However, they found that freshwater insect populations have increased overall, perhaps owing to clean water efforts and climate change. Patterns of variation suggest that local-scale drivers are likely responsible for many changes in population trends, providing hope for directed conservation actions.
Science , this issue p. 417 ; see also p. 368
![| Changes in butterfly species richness in the Swiss canton of Thurgau over a century. Panel (a) shows the number of butterfly species (species richness) by family (see legend panel b, except «all»), for the years 1913, 1985, and 2018. Panel (b) shows the same data as in panel (a), but as proportions of the respective species richness in 1913. Additionally, the overall changes are shown («all», legend): the number of species declined from 100 in 1913 (100%), to 76 in 1985 (76%), and finally to 72 in 2018 (72%). The connecting lines in panels (a-b) are intended as a visual aid, they should not be interpreted as linear trends over time. Panel (c) summarizes the number of species in 2018 that either was absent in 1985 or disappeared since then. «disappeared»: these species were either only present in 1985, but not in 1913 (x = 1 year), or present in both years (x = 2 years); «(re-)appeared»: these species were either present in 1913, but not in 1985 (x = 1 year), or absent in both years (x = 2 years). Panel (d) shows the species list in 2018 in terms of the red list categories (n = 72). Panel (e) shows the current red list categories of species that disappeared between 1985 and 2018 (n = 18), while panel (f) shows the current red list categories of species that (re-)appeared between 1985 and 2018 (n = 14); cf. panel (c). NA: no RL status, LC: least concern, NT: near threatened, VU: vulnerable, EN: endangered, CR: critically endangered. See section S1 (Supplementary material) for additional information. [Figure from: Bozzuto C (2020): «A quantitative framework to guide restoration of butterfly communities in fragmented landscapes». Technical Report Wildlife Analysis GmbH, Zurich, Switzerland. DOI: 10.13140/RG.2.2.28616.14080]](https://www.researchgate.net/profile/Claudio-Bozzuto/publication/346561944/figure/fig9/AS:964214785060869@1606898127938/Changes-in-butterfly-species-richness-in-the-Swiss-canton-of-Thurgau-over-a-century_Q320.jpg)
![| Illustrative example. (a) The first four layers, from top to bottom, show three butterfly species, five larval host plant species, five vegetation types, and nine patches; further, all links between layers are shown. In the fifth layer at the bottom of panel (a), the three butterfly species-specific metapopulations are sketched, resulting from mapping the butterfly species onto the patches. (b) The three, partly overlapping metapopulations from panel (a): patch areas and among-patch distances are drawn to scale (Table S1). The colors correspond to the butterfly colors in panel (a). Two-colored patches and lines are used by two different butterfly species, and the lines represent the connections through dispersal between patches; the spatial unit is arbitrary. The patches will be referred to by the number labels shown. [Bozzuto C (2020): «A quantitative frame- work to guide restoration of butterfly communities in fragmented landscapes». Wildlife Analysis
GmbH. DOI: 10.13140/RG.2.2.28616.14080]](https://www.researchgate.net/profile/Claudio-Bozzuto/publication/346561944/figure/fig10/AS:964214931861507@1606898162755/Illustrative-example-a-The-first-four-layers-from-top-to-bottom-show-three_Q320.jpg)
![| Unipartite networks (real-world data). Panel (a) shows how many butterfly species (potentially) share a certain number of vegetation types, derived from !! !!,! !! . Panel (b) uses the same unipartite
network as for panel (a), now translated into a dendrogram showing the similarity between butterfly species (Jaccard index), based on (potentially) shared vegetation types. [See original publication for panels (c-d); Bozzuto C (2020): «A quantitative framework to guide restoration of butterfly communities in fragmented landscapes». Technical Report Wildlife Analysis GmbH, Zurich, Switzerland. DOI: 10.13140/RG.2.2.28616.14080]](https://www.researchgate.net/profile/Claudio-Bozzuto/publication/346561944/figure/fig11/AS:964215221264384@1606898231581/Unipartite-networks-real-world-data-Panel-a-shows-how-many-butterfly-species_Q320.jpg)
![| Examples of vegetation-specific butterfly-host plant modules (real-world data). Panel (a) shows butterfly-host plant clusters for a vegetated bank as vegetation type, while panel (b) shows the analogous results for a wet meadow. Networks plotted using BiMat (Flores et al. 2016). [Figure from Bozzuto C (2020): «A quantitative framework to guide restoration of butterfly communities in fragmented landscapes». Technical Report Wildlife Analysis GmbH, Zurich, Switzerland. DOI: 10.13140/RG.2.2.28616.14080]](https://www.researchgate.net/profile/Claudio-Bozzuto/publication/346561944/figure/fig12/AS:964215347109888@1606898261014/Examples-of-vegetation-specific-butterfly-host-plant-modules-real-world-data-Panel_Q320.jpg)
![| Metrics at the community level (illustrative example). (a) Patch importance values at the community level, computed as weighted sum of butterfly-specific patch importance values (cf. Fig. 4a) based on red list categories. The results have been rescaled so that the highest value (patch importance) has color white and value 1. (b) Effect of small changes to patch areas and among-patch connections on !! (elasticity analysis, cf. Fig. 5) at the community level, computed analogously to panel (a): for simplicity, all patches and connections have been «increased» by 10%. Further, all resulting relative changes in !! have been rescaled so that the highest value (patch importance) has color white and value 1. (c)–(d) Relative changes in !!, at the community level, when adding a patch of size ! = 1 suitable for butterfly species 1 and 2 (panel c) or butterfly species 2 and 3 (panel d) (cf. Fig. 7). Both panels use the same color scale, and results are thus comparable. Parameter values in Table S1. [Figure from Bozzuto C (2020): «A quantitative framework to guide restoration of butterfly communities in fragmented landscapes». Technical Report Wildlife Analysis GmbH, Zurich, Switzerland.
DOI: 10.13140/RG.2.2.28616.14080]](https://www.researchgate.net/profile/Claudio-Bozzuto/publication/346561944/figure/fig13/AS:964215447752710@1606898285817/Metrics-at-the-community-level-illustrative-example-a-Patch-importance-values-at_Q320.jpg)
