Restoration ecology commonly seeks to re-establish species of interest in degraded habitats. Despite a rich understanding of how succession influences re-establishment, there are several outstanding questions that remain unaddressed: are short-term abundances sufficient to determine long-term re-establishment success, and what factors contribute to unpredictable restorations outcomes? In other words, when restoration fails, is it because the restored habitat is substandard, because of strong competition with invasive species, or alternatively due to changing environmental conditions that would equally impact established populations? Here, we re-purpose tools developed from modern coexistence theory to address these questions, and apply them to an effort to restore the endangered Contra Costa goldfields (Lasthenia conjugens) in constructed ("restored") California vernal pools. Using 16 years of data, we construct a population model of L. conjugens, a species of conservation concern due primarily to habitat loss and invasion of exotic grasses. We show that initial, short-term appearances of restoration success from population abundances is misleading, as year-to-year fluctuations cause long-term population growth rates to fall below zero. The failure of constructed pools is driven by lower maximum growth rates compared with reference ("natural") pools, coupled with a stronger negative sensitivity to annual fluctuations in abiotic conditions that yield decreased maximum growth rates. Nonetheless, our modeling shows that fluctuations in competition (mainly with exotic grasses) benefit L. conjugens through periods of competitive release, especially in constructed pools of intermediate pool depth. We therefore show how reductions in invasives and seed addition in pools of particular depths could change the outcome of restoration for L. conjugens. By applying a largely theoretical framework to the urgent goal of ecological restoration, our study provides a blueprint for predicting restoration success, and identifies future actions to reverse species loss.

Transient dynamics have always intrigued ecologists, but current rapid environmental change (inducing transients even in previously undisturbed systems) has highlighted their importance more than ever. Here, I introduce a method for analyzing the sensitivity of transient ecological dynamics to parameter perturbations. The question the method answers is: how would the community dynamics have unfolded for some time horizon had the parameters been slightly different? I apply the method to three empirically parameterized models: competition between native forbs and exotic grasses in California, a host-parasitoid system, and an experimental chemostat predator-prey model. These applications showcase the ecological insights one can gain from models using transient sensitivity analysis. First, one can find parameters and their combinations whose perturbations disproportionately affect a system. Second, one can identify particular windows of time during which the predicted deviation from the unperturbed trajectories is especially large and utilize this information for management purposes. Third, there is an inverse relationship between transient and long-term sensitivities whenever the interacting populations are ecologically similar; paradoxically, the smaller the immediate response of the system, the more extreme its long-term response will be.

Syntrophic cooperation among prokaryotes is ubiquitous and diverse. It relies on unilateral or mutual aid that may be both catalytic and metabolic in nature. Hypotheses of eukaryotic origins claim that mitochondrial endosymbiosis emerged from mutually beneficial syntrophy of archaeal and bacterial partners. However, there are no other examples of prokaryotic syntrophy leading to endosymbiosis. One potential reason is that when externalized products become public goods, they incite social conflict due to selfish mutants that may undermine any mutualistic interactions. To rigorously evaluate these arguments, here we construct a general mathematical framework of the ecology and evolution of different types of syntrophic partnerships. We do so both in a general microbial and in a eukaryogenetic context. Studying the case where partners cross-feed on each others self-inhibiting waste, we show that cooperative partnerships will eventually dominate over selfish mutants. By contrast, systems where producers actively secrete enzymes that cross-facilitate their partners resource consumption are not robust against cheaters over evolutionary time. We conclude that cross-facilitation is unlikely to provide an adequate syntrophic origin for endosymbiosis, but that cross-feeding mutualisms may indeed have played that role.

Spread of transboundary animal diseases can have large impact on animal welfare, public health and economy. The effects of this include economic losses in terms of lower milk production, lower weight gain and culling due to welfare concerns. Disease preparedness is therefore important to be prepared for a possible outbreak, and policies need to be in place in order to take appropriate actions in case of an outbreak. It is also important to be able to take preventive actions to lessen the risk and size of an outbreak. For this, mathematical models are useful to describe the effects of an outbreak and to facilitate informed policy decisions.

Mathematical models of spread of animal diseases, implicitly or explicitly, model the route of infection. One route of particular concern is the shipment of livestock animals since animal shipments have the possibility to move infected animals over long distances and introduce disease in previously unaffected areas. It is therefore important to have underlying data to use as input to models in order to consider possible future scenarios. Such data may however be sparse and not readily available. Based on observed (and sometimes incomplete) data, the underlying process that determines the probabilities of livestock shipments’ origins and destinations can be modeled. By using Bayesian statistics and Markov Chain Monte Carlo methods, it is possible to obtain distributions of the underlying parameters in the model, which in turn allow posterior predictive sets of shipments to be generated. These can further be used in a disease simulation to analyze the course of a potential outbreak. Given a large number of scenarios of interest and substantial stochastic effects, implementation of such models requires fast algorithms to facilitate execution of a sufficient number of replicated simulations, which may be infeasible under naive methods. The topics of this thesis are models of live cattle shipment, the problems of lack of shipment data and the computational challenges of modeling and simulating spread of infectious animal diseases.

In Paper I, the spatio-temporal variations in distance dependence of cattle shipments in Sweden were studied by using real shipment data, Bayesian statistics and Markov Chain Monte Carlo methods. The main results were that the spatial as well as the temporal aspect are important when modeling networks of cattle shipments in Sweden. The spatial variations distance dependence were analyzed at county, land (Norrland, Svealand and Götaland) and national level (i.e. no spatial variation). Similarly, the temporal aspect were investigated at three levels of granularity, using monthly-, quarterly- and annual variations (i.e no temporal variation). The level of granularity at which the spatio-temporal variations in distance dependence was captured better, in terms of Deviance Information Criterion, was identified at the county and quarter level. This results shows that such variations should be acknowledged when modeling networks of cattle shipments in Sweden.

Paper II considered cattle shipments in the U.S. It addressed the problem of intrastate shipments being absent in available data and included responses from a survey taken by experts to estimate the proportion of shipments moving intrastate. The results showed that data from experts had minor effects on the estimations of proportion of intrastate shipments, mainly because of disparate estimates provided by the experts. This paper also investigated three types of functional forms of the distance dependence, and it was shown that the type used in Paper I, was the least preferred of the three. The preferred functional form had a plateau-shape at short distances as well as a fat tail, describing high probability of long-distance shipments.

Paper III addressed the computational challenges of simulating spread of livestock diseases. In Paper III, infections were modeled to spread locally from farm to farm without modeling§ each pathway individually (this may include pathways such as airborne spread, wildlife etc.). To avoid evaluating infection probability of all pairs of infected and susceptible premises, spread of disease was simulated by partitioning the landscape into grids and thereby letting farms belong to a specific cell in this grid. An algorithm was introduced that make use of overestimations of the probability of infection to discard entire cells from further consideration as they are considered as uninfected in the current time frame. Despite introducing estimations of probabilities, the algorithm does not introduce estimations to the spread of disease, and does not compromise the integrity of the simulation. This algorithm was compared to the naive algorithm of evaluating the farms pairwise as well as to two other published algorithms developed for increased computational efficiency. It was shown that the algorithm presented in Paper III was as fast as or faster than other considered methods.

Paper IV expanded the methods of Paper II and used the methodology from Paper III to simulate spread of disease via cattle shipments and via local spread across the U.S. In Paper IV, additional data at state- and county level were included that aimed at capturing shipment patterns related to the infrastructure of the production system not captured by the distance dependence. The model also considered three types of premises: farm, feedlot and market. This approach allows for different parameters across premises types, acknowledging their different roles in the production system. The result showed that these types of data were important to include when modeling the system and increased model performance in terms of WAIC, suggesting that industry structure should be accounted for when modeling cattle shipments. The spread of disease simulation included control scenarios such as culling of specific premises and also included a SEIR-model to model the infection status of each premises, referred to as partial transition. The results showed that while the inclusion of partial transition slowed the outbreak, the spatial pattern of the outbreak did not change.

This thesis provides insights to what factors are important when predicting animal shipments networks for usage in spread of disease simulations and how these factors can be modeled. It also stresses the importance of efficient algorithms when using simulations and presents an algorithm suited for simulating spread of disease between farms where pathways of the pathogen are not modeled explicitly. How to accurately estimate the spread of disease via shipments and how to simulate a large number of outbreak scenarios within reasonable time are two major challenges a modeler faces when trying to predict the impact of a potential outbreak.

Recycling phosphorus-rich organic wastes and manures is critical for phosphorus sustainability and a transition to a more circular economy for phosphorus. Beyond agronomic benefits, the win-wins are numerous, with benefits to society, environment, economy, and business growth. However, to significantly increase phosphorus recycling, education, awareness-raising, investment in technology and infrastructure, and policy support are urgently needed.

Loss of species in food webs can set in motion a cascade of additional (secondary) extinctions. A species' position in a food web (e.g. its trophic level or number of interactions) is known to affect its ability to persist following disturbance. These simple measures, however, offer only a coarse description of how species fit into their community. One would therefore expect that more detailed structural measures such as participation in three-species motifs (meso-scale structures which provide information on a species' direct and indirect interactions) will also be related to probability of persistence. Disturbances affecting the basal resources have particularly strong effects on the rest of the food web. However, how disturbances branch out and affect consumer persistence depends on the structural pattern of species interactions in several steps. The magnitude, for example, the proportion of basal resources lost, will likely also affect the outcome. Here, we analyse whether a consumer's risk of secondary extinction after the removal of basal resources depends on the consumer's motif participation and how this relationship varies with the severity of disturbance. We show that consumer species which participate more frequently in the direct competition motif and less frequently in the omnivory motif generally have higher probability of persistence following disturbance to basal resources. However, both the strength of the disturbance and the overall network structure (i.e. connectance) affect the strength and direction of relationships between motif participation and persistence. Motif participation therefore captures important trends in species persistence and provides a rich description of species' structural roles in their communities, but must be considered in the context of network structure as a whole and of the specific disturbance applied. Like degree and trophic level, a species' participation in meso-scale motifs can affect its persistence after disturbance. We show that these relationships also depend strongly on the strength of disturbance.image

Unplanned, high-density settlements in low-middle income countries often lack functional wastewater management systems. Nature-based solutions, such as constructed wetlands, are an option for the treatment of greywater, provided they are used by and useful to the community. We explored the effectiveness of iterative design for two pilot constructed wetlands in an informal settlement in Johannesburg, South Africa. Using ergonomics and human factors (E/HF) design approaches, this study evaluated the usability and postural risks associated with interactions with the constructed wetlands to motivate (and evaluate) design changes to increase use and reduce the risk of musculoskeletal pain. An elevated work area reduced musculoskeletal risks and a larger work area allowed more users at one time. The raised work areas provided other benefits such as stormwater and sewerage protection. The value of E/HF as part of a broader transdisciplinary team was demonstrated by embedding the design in the activities of the community.

Environmental change research is plagued by the curse of dimensionality: the number of communities at risk and the number of environmental drivers are both large. This raises the pressing question if a general understanding of ecological effects is achievable. Here, we show evidence that this is indeed possible. Using theoretical and simulation-based evidence for bi- and tritrophic communities, we show that environmental change effects on coexistence are proportional to mean species responses and depend on how trophic levels on average interact prior to environmental change. We then benchmark our findings using relevant cases of environmental change, showing that means of temperature optima and of species sensitivities to pollution predict concomitant effects on coexistence. Finally, we demonstrate how to apply our theory to the analysis of field data, finding support for effects of land use change on coexistence in natural invertebrate communities.

Biogas production through anaerobic digestion is an integral part of the transition toward a biobased and circular economy and its expansion is foreseen in many parts of the world as well as in Europe. In Sweden, a governmental inquiry suggested biogas production to be increased from about 2 TWh today to 7 TWh by 2030. This rapid expansion would require installation of several new biogas plants across the country. However, the location of biogas plants can greatly affect its business performance and there are several geographic and socio-political factors that would limit the choice of location. Through dialogue with existing biogas producing companies and a few other related actors, we identified 12 factors that are commonly considered in the site-selection of biogas plants in Sweden or are considered to be important in the years to come. These factors are grouped into those related to supply and demand (feedstock supply, biogas demand, digestate demand, and carbon dioxide demand), infrastructure and synergies (available infrastructure, adjacent existing industries), land-use and zoning (nearby housing, zoning, and historic preservation sites), and socio-political context (political strategies and goals, organizational capability, and local social acceptance). We discuss how these factors can be used under rapidly transforming conditions in Sweden through different site-selection logics and highlight the importance of spatially explicit analysis for individual or coordinated decision making in future. Our method of enquiry and analysis, and to a certain degree the factors, can be also relevant for other countries, particularly in Europe. This study paves the way for more in-depth investigation of the question of site-selection of biogas plants in Sweden; both in the direction of detailed analysis at the local level, or screening analysis on the regional or national level for improved coordinated actions.

Recent work indicates that feralisation is not a simple reversal of domestication, and therefore raises questions about the predictability of evolution across replicated feral populations. In the present study we compare genes and traits of two independently established feral populations of chickens (Gallus gallus) that inhabit archipelagos within the Pacific and Atlantic regions to test for evolutionary parallelism and/or divergence. We find that feral populations from each region are genetically closer to one another than other domestic breeds, despite their geographical isolation and divergent colonisation histories. Next, we used genome scans to identify genomic regions selected during feralisation (selective sweeps) in two independently feral populations from Bermuda and Hawaii. Three selective sweep regions (each identified by multiple detection methods) were shared between feral populations, and this overlap is inconsistent with a null model in which selection targets are randomly distributed throughout the genome. In the case of the Bermudian population, many of the genes present within the selective sweeps were either not annotated or of unknown function. Of the nine genes that were identifiable, five were related to behaviour, with the remaining genes involved in bone metabolism, eye development and the immune system. Our findings suggest that a subset of feralisation loci (i.e. genomic targets of recent selection in feral populations) are shared across independently established populations, raising the possibility that feralisation involves some degree of parallelism or convergence and the potential for a shared feralisation 'syndrome'.

Transboundary animal diseases, such as foot and mouth disease (FMD) pose a significant and ongoing threat to global food security. Such diseases can produce large, spatially complex outbreaks. Mathematical models are often used to understand the spatio-temporal dynamics and create response plans for possible disease introductions. Model assumptions regarding transmission behavior of premises and movement patterns of livestock directly impact our understanding of the ecological drivers of outbreaks and how to best control them. Here, we investigate the impact that these assumptions have on model predictions of FMD outbreaks in the U.S. using models of livestock shipment networks and disease spread. We explore the impact of changing assumptions about premises transmission behavior, both by including within-herd dynamics, and by accounting for premises type and increasing the accuracy of shipment predictions. We find that the impact these assumptions have on outbreak predictions is less than the impact of the underlying livestock demography, but that they are important for investigating some response objectives, such as the impact on trade. These results suggest that demography is a key ecological driver of outbreaks and is critical for making robust predictions but that understanding management objectives is also important when making choices about model assumptions.

Ecologists have long sought to understand variation in food chain length (FCL) among natural ecosystems. Various drivers of FCL, including ecosystem size, resource productivity and disturbance, have been hypothesised. However, when results are aggregated across existing empirical studies from aquatic ecosystems, we observe mixed FCL responses to these drivers. To understand this variability, we develop a unified competition-colonisation framework for complex food webs incorporating all of these drivers. With competition-colonisation tradeoffs among basal species, our model predicts that increasing ecosystem size generally results in a monotonic increase in FCL, while FCL displays non-linear, oscillatory responses to resource productivity or disturbance in large ecosystems featuring little disturbance or high productivity. Interestingly, such complex responses mirror patterns in empirical data. Therefore, this study offers a novel mechanistic explanation for observed variations in aquatic FCL driven by multiple environmental factors.

Advances in restoration ecology are needed to guide ecological restoration in a variable and changing world. Coexistence theory provides a framework for how variability in environmental conditions and species interactions affects species success. Here, we conceptually link coexistence theory and restoration ecology. First, including low-density growth rates (LDGRs), a classic metric of coexistence, can improve abundance-based restoration goals, because abundances are sensitive to initial treatments and ongoing variability. Second, growth-rate partitioning, developed to identify coexistence mechanisms, can improve restoration practice by informing site selection and indicating necessary interventions (e.g., site amelioration or competitor removal). Finally, coexistence methods can improve restoration assessment, because initial growth rates indicate trajectories, average growth rates measure success, and growth partitioning highlights interventions needed in future.

Restoring nutrient circularity across scales is important for ecosystem integrity as well as nutrient and food security. As such, research and development of technologies to recover plant nutrients from various organic residues has intensified. Yet, this emerging field is diverse and difficult to navigate, especially for newcomers. As an increasing number of actors search for circular solutions to nutrient management, there is a need to simplify access to the latest knowledge. Since the majority of nutrients entering urban areas end up in human excreta, we have chosen to focus on human excreta and domestic wastewater. Through systematic mapping with stakeholder engagement, we compiled and consolidated available evidence from research and practice. In this paper, we present `Egestabase' - a carefully curated open-access online evidence platform that presents this evidence base in a systematic and accessible manner. We hope that this online evidence platform helps a variety of actors to navigate evidence on circular nutrient solutions for human excreta and domestic wastewater with ease and keep track of new findings.

DNA methylation is a key regulator of eukaryote genomes, and is of particular relevance in the regulation of gene expression on the sex chromosomes, with a key role in dosage compensation in mammalian XY systems. In the case of birds, dosage compensation is largely absent, with it being restricted to two small Male Hyper-Methylated (MHM) regions on the Z chromosome. To investigate how variation in DNA methylation is regulated on the Z chromosome we utilised a wild x domestic advanced intercross in the chicken, with both hypothalamic methylomes and transcriptomes assayed in 124 individuals. The relatively large numbers of individuals allowed us to identify additional genomic MHM regions on the Z chromosome that were significantly differentially methylated between the sexes. These regions appear to down-regulate local gene expression in males, but not remove it entirely (unlike the lncRNAs identified in the initial MHM regions). These MHM regions were further tested and the most balanced genes appear to show decreased expression in males, whilst methylation appeared to be far more correlated with gene expression in the less balanced, as compared to the most balanced genes. In addition, trans effect hotspots were also identified that were based on the autosomes but affected the Z, and also that were based on the Z chromosome but that affected autosomal DNA methylation regulation. In addition, quantitative trait loci (QTL) that regulate variation in methylation on the Z chromosome, and those loci that regulate methylation on the autosomes that derive from the Z chromosome were mapped. Trans-effect hotspots were also identified that were based on the autosomes but affected the Z, and also one that was based on the Z chromosome but that affected both autosomal and sex chromosome DNA methylation regulation. We show that both cis and trans loci that originate from the Z chromosome never exhibit an interaction with sex, whereas trans loci originating from the autosomes but affecting the Z chromosome always display such an interaction. Our results highlight how additional MHM regions are actually present on the Z chromosome, and they appear to have smaller-scale effects on gene expression in males. Quantitative variation in methylation is also regulated both from the autosomes to the Z chromosome, and from the Z chromosome to the autosomes. DNA methylation is a key regulator of eukaryote genomes, and is of particular relevance in the regulation of gene expression on the sex chromosomes, with a key role in dosage compensation in mammalian XY systems. In the case of birds, dosage compensation is largely absent, with it being restricted to two small Male Hyper-Methylated (MHM) regions on the Z chromosome. We utilised a wild x domestic advanced intercross in the chicken, with both hypothalamic methylomes and transcriptomes assayed in 124 individuals, to investigate the role that methylation plays in regulating gene expression on the Z chromosome. Our results highlight how additional MHM regions are actually present on the Z chromosome, and they appear to have smaller-scale effects on gene expression in males. Quantitative variation in methylation is also regulated both from the autosomes to the Z chromosome, and from the Z chromosome to the autosomes. In addition, these MHM regions were further tested and the most balanced genes appear to show decreased expression in males, whilst methylation appeared to be far more correlated with gene expression in the less balanced, as compared to the most balanced genes.

There is a significant amount of evidence highlighting the health, wellbeing and social benefits of gardening during previous periods of crises. These benefits were also evident during the COVID-19 pandemic. This paper presents a narrative review exploring gardening during the early stages of the COVID-19 pandemic to understand the different forms of gardening that took place during this crisis and key elements of this activity. Research about gardening during the pandemic focused on food (in)security and disrupted food systems, the health and wellbeing benefits of gardening, and the social dimensions of gardening. We offer three vignettes of our own research to highlight key insights from local, national and international perspectives of gardening during the pandemic. The papers conclusion outlines how researchers, policy makers and public health practitioners can harness what has been learned from gardening during the pandemic to ensure these benefits are more widely available and do not exacerbate already entrenched health inequalities in society.

In this  study we have investigated the role of biogas solutions to support increased resource efficiency on the island Gotland,  including recovery and redistribution of nitrogen (N) and phosphorus (P) within the agricultural sector. First, we  analyzed the potential for  expanding energy and nutrient recovery from organic residues using biogas solutions. Our findings suggest that the biogas production could expand to 165 GWh, from the current 36 GWh (2020), with manure accounting for a potential  110 GWh biogas annually if all were digested. Comparing the nutrients contained in organic feedstock with the crop nutrient demand on Gotland showed that for N the  demand is 2.4 times higher than the supply. In contrast, the calculations showed a 137 tonnes P surplus, with distinct excess areas in the center and southern part of the island.

We then compared scenarios with different numbers (3 - 15) of biogas plants with respect to   efficient nutrient redistribution and transport costs. Spatial constraints for new plants, e.g. need for roads with a certain capacity  and permit issues, were accounted for by  adding local information to a national data set. We identified  104 potential locations (1 km$^2$ grid cells) and used an optimization model to identify the most suitable locations for minimized transport costs. Optimal  (meeting the crop demand with no excess) redistribution of all nutrients contained in the feedstock, as raw digestate from biogas plants, would result in an export of 127 tonnes of P from the island. The model results indicated that if all potential feedstock would be digested in three additional biogas plants and nutrients redistributed for optimal reuse, the total transport  cost would be 2.6 million SEK annually, excluding the costs for nutrient export from the island (3.7 million SEK). If instead 10 or 15 smaller plants would be built, the transport cost would drop to 1.8  million SEK, with the same amount of P being exported. Comparing the scenarios with different number of biogas plants (3 - 15), showed that some locations are more suitable than others in terms of distance to feedstock and

to fields with fertilizer demands. Finally, a preliminary analysis of the amount of crop residues indicated that this type of feedstock could add a substantial amount of biogas production, but more extensive analyses are needed to assess  the feasibility to realize part of that potential.

Harvest estimation is a central part of adaptive management of wildlife. In the absence of complete reporting, statishods are required to extrapolate from the available data. We developed a Hierarchical Bayesian framework tailored for partial reporting where hunting areas covered by reporting hunting teams are available. The framework accounts for autocorrelation at the national, county, and hunting management precinct levels. We derived and evaluated an approximation for the probability of harvest on non-reported areas under a non-linear relationship between harvest area per team and harvest rate. We applied the framework to reports of red fox (Vulpes vulpes), wild boar (Sus scrofa), common eider (Somateria mollissima), and grey partridge (Perdix perdix) harvest in Sweden from the hunting years 1997/1998-2019/2020. The approximation was evaluated and determined to be sufficiently accurate. We showed that accounting for autocorrelation in harvest reduced uncertainty and increased predictive accuracy, particularly for game hunted in low numbers and variably between teams. The analysis also revealed that hunting rate has a sub-linear relationship with a teams area for all considered species. Further, the framework revealed substantial differences across regions in terms of parameters modeling the distribution of huntable land across teams as well as parameters modeling harvest rates. We conclude that the framework is useful to detect shifts in hunting rates and/or practices.

Metabolic theories in ecology interpret ecological patterns at different levels through the lens of metabolism, typically applying allometric power scaling laws to describe rates of energy use. This requires a sound theory for metabolism at the individual level. Commonly used mechanistic growth models lack some potentially important aspects and fail to accurately capture a growth pattern often observed in insects. Recently, a new model (MGM–the Maintenance-Growth Model) was developed for ontogenetic and post-mature growth, based on an energy balance that expresses growth as the net result of assimilation and metabolic costs for maintenance and feeding. The most important contributions of MGM are: 1) the division of maintenance costs into a non-negotiable and a negotiable part, potentially resulting in maintenance costs that increase faster than linearly with mass and are regulated in response to food restriction; 2) differentiated energy allocation strategies between sexes and 3) explicit description of costs for finding and processing food. MGM may also account for effects of body composition and type of growth at the cellular level. The model was here calibrated and evaluated using empirical data from an experiment on house crickets growing under ad libitum conditions. The procedure involved parameter estimations from the literature and collected data, using statistical models to account for individual variation in parameter values. It was found that ingestion rate cannot be generally described by a simple allometry, here requiring a more complex description after maturity. Neither could feeding costs be related to ingestion rate in a simplistic manner. By the unusual feature of maintenance costs increasing faster than linearly with body mass, MGM could well capture the differentiated growth patterns of male and female crickets. Some other mechanistic growth models have been able to provide good predictions of insect growth during early ontogeny, but MGM may accurately describe the trajectory until terminated growth.

Safe recycling of nutrients found in human excreta back to agriculture is an important component of a circular economy that can protect waterways and stabilize food prices. Although many technological advances for the recovery of these nutrients exist, large-scale implementation is lacking. A commonly cited barrier is a lack of acceptance of fertilizers from human excreta and for food products grown with such fertilizers. The food retail sector, as an intermediary between producers and consumers, is an important actor with power to influence opinions and purchasing practices. In this study, we surveyed 127 food retailers (stores) and reviewed publicly available retailer sustainability policies to assess acceptance of the use of recycled fertilizers. We gauged acceptance of three products relevant for the Swedish market – struvite, phosphorus from ash, and dehydrated urine. Most respondents felt that all three recovery techniques were unlikely to be harmful either to themselves or to the environment. It was more acceptable to use products further away from human consumption. In general, struvite and phosphorus from ash were perceived more positively. Acceptance of wastewater-derived fertilizers was largely dependent on perceived risks, especially the fate of pharmaceutical residues. While retailers in Sweden are not negative to reuse, they seem unlikely to provide strong support for nutrient recirculation from human excreta unless it becomes a greater concern for the public.

Supporting low levels of animal product (meat, dairy, and eggs) consumption and food waste can significantly reduce the impacts of unsustainable phosphorus use. In addition, consuming products grown with good on-farm nutrient management practices, including phosphorus recycling can further reduce impacts. These changes can contribute to achieving multiple United Nations’ Sustainable Development Goals related to improving human and environmental health.

This document accompanies an art & science collaborative exhibit exploring how urban agriculture can contribute to sustainable resource flows to support food production, recreation, and water quality.

Denna broschyr har tagits fram i anslutning till en utställning där en forskargrupp samverkar med en konstnär. Den utforskar hur stadsodling kan bidra till hållbara resursflöden och därigenom stödja produktion av livsmedel, rekreation och god vattenkvalitet.

Cities are central to improving natural resource management globally. Instead of reinventing the wheel for each interlinked sustainability priority, we suggest synergising with, and learning from existing net-zero carbon initiatives to explicitly tackle another vital element: phosphorus. To achieve net-zero phosphorus actors must work together to (1) minimise loss flows out of the city, (2) maximise recycling flows from the city to agricultural lands, and (3) minimise the need for phosphorus in food production.

In ecosystems across the world, species co-exist, compete, and consume, all while adapting to environmental conditions and disruptions. An important key to the puzzle of understanding how species will respond to changes in the ecosystems, caused by for example climate change, pollution, habitat destruction, and overexploitation, is what current roles species have in a larger context. Species interactions are the basis for many ecological processes, for example, describing who eats whom in food webs. Finding groups of species that have similar interactions can provide insight into what roles species have in a food web, as well as identify core structures and functions of said food webs. 

Food webs are often based on data aggregates of large areas. Consequently, there is a possibility of blurring local aspects of the food web structure, thus blurring locally realized species roles. In Paper I, I used the group model to analyze local and regional group structures of a food web in the Barents Sea. The group model identifies groups linked to their niche, in which species eat, and are eaten by similar species. I found the large, regional food web diverged from the local group structures, indicating that locally scaled food webs may be required to find more accurately realized species roles. On a local scale, similar group structures were generally spatially clustered and environmentally similar. This was to some extent explained by similarities in species compositions, but more fine-grained patterns related to species identities further impacted the group structures. 

In essence, the group model is a type of community detection based on stochastic block models. Generated groups contain groups of species with similar sets of prey and predators. Groups are related to both trophic similarity and modularity, but the process itself is, as the name implies, stochastic. Various methodologies to determine a "best fit" group structure out of multiple iterations exist. Arguments can however be made, that discarded, alternative group structures may still hold ecological relevance. In Paper II, I investigated five food webs by creating a solution landscape from their respective alternative group structures. My results showed, that the core group structure remained intact across alternative solutions, while potential changes in the group structure were generally limited to smaller subsets of groups or species. 

Expanding on the analysis of Paper II, Paper III, accounts for the inherent un-certainty of interactions in food webs. Food webs are based on data sets, potentially covering hundreds of species and thousands of interactions. How-ever, spatial and temporal aspects, and the dynamical nature of whether interactions are realized, can impact the food web structure. Here, I investigated how group structures responded to disturbing interactions (i.e., random removal of different fractions). The key findings showed how in general, core group structures remained intact, and already unstable groups turned increasingly unstable. Species traits distinctly defined group identities, but I found no particular species traits ubiquitously linked to unstable group structures. 

How species interact is intrinsic to their traits; basic trait-matching constraints must be fulfilled for an interaction to be realized, such as a predator being large enough to eat a specific prey. Traits are however also subject to change, with potentially strong selective pressures from for example environmental change or overexploitation. If traits change sufficiently, species interactions can also change, potentially putting affected species in a new ecological context with new predators, prey, and competitive relationships. Possibly related, the cod population in the Baltic Sea, has failed to recover even after ceasing fishing. In Paper IV, I formulated an eco-evolutionary model, which considered cod’s changed ecological role after the collapse, highlighting how competition with flounder species can contribute to blocking the cod population in the Baltic Sea from recovering. 

With this thesis, I aimed to improve understanding of how species groups based on interactions relate to food web structure. My results highlight how the group model can generate robust groups, which are generally resilient to even moderate disturbances while providing a coarse-grained representation of species roles in a food web. The spatial context of the food web, with its included species and interactions, needs to be considered to get a more accurate representation of locally realized species roles. I have further modeled how species traits may be altered by eco-evolutionary dynamics under strong selective pressure, with subsequent shifts in ecological roles. These aspects are pivotal in understanding how species in our ecosystems will be affected by today’s multitude of environmental impacts. 

Broad-scale interaction patterns among species in food webs can be identified using the group model, which identifies groups of species, sharing similar sets of predators and prey from other groups. These shared relationships are relevant for the functionality of species. The group model originates from stochastic block models, meaning the obtained group structures of the same food web can differ in multiple runs. A single best partition may miss relevant information, and a consensus solution may blur complementary communities. Hence, it is highly relevant to analyze the full solution landscape while searching for the optimal partitioning of species. In particular, a narrow solution landscape would highlight the reliability of the identified groups. Here, using five empirical food webs, we analyze their respective solution landscape based on multiple group model runs of the same network. By analyzing the solution landscapes, we aim to explain the differences between solutions and what they entail, structurally and ecologically. Our results show that the overall general group structures remain intact across different iterations. While some food webs vary more, differences are commonly limited to a smaller number of groups with seemingly similar species roles. Our results suggest that while the stochastic process of the group model can generate alternate solutions for the same food web, these differences generally involve weaker distinctions of species in a small number of groups rather than a large structural turnover.

Individual-level trait diversity has been identified as an essential component of trait diversity (TD), influencing community assembly and structure. Traditionally, one employs trait diversity indices to measure facets of individual-level trait diversity (divergence, richness and evenness). However, the application of species-level trait diversity indices to individual-level traits data and their implications have not been adequately studied. Thus, we examined the possible challenges of using four commonly used multi-trait TD indices: Raos quadratic entropy (Rao), functional dispersion (FDis), functional evenness (FEve) and functional richness (FRic); two indices primarily developed to measure individual-level trait diversity: trait evenness distribution (TED-for evenness) and trait onion peeling (TOP-for richnness); and a modified version of TED (TEDM-for evenness). Additionally, we considered an index that integrates both evenness and richness by generalizing ordinary Hill indices for traits (coined HIT). We measured individual-level trait diversity with these indices using simulated traits data and experimental data from a growth experiment with cyanobacteria. Comparing the observed trends from the indices with the expected trends, we observed that only the trait divergence indices (FDis and Rao) produced the expected trends in the simulation scenarios and experimental data. TED and TEDM are not robust against the number of individuals used, and FEve is not sensitive to some changes in the location of individuals in the trait space. Also, TOP proved to be a discontinuous function dependent on the number of individuals, and FRic did not produce the anticipated trend when changes in the trait space did not affect the edges of the trait space. HIT did produce the anticipated changes, but it was only reliable when many individuals were sampled. In summary, applying these individual-level trait diversity indices to quantify anything except trait divergence may lead to misinterpretation of the original situation of trait distribution in the trait space if their specific properties are not adequately considered.

The CO( 2)flux (FCO2) from lakes to the atmosphere is a large component of the global carbon cycle anddepends on the air-water CO2concentration gradient (Delta CO2) and the gas transfer velocity (k). Both Delta CO2 and k can vary on multiple timescales and understanding their contributions toFCO(2)is important for explaining var-iability influxes and developing optimal sampling designs. We measuredFCO2 and Delta CO(2 )and derivedkforone full ice-free period in 18 lakes usingfloating chambers and estimated the contributions of Delta CO2 and k to FCO2 variability. Generally, kcontributed more than Delta CO2to short-term (1-9d) FCO2 variability. With in creased temporal period, the contribution of k to FCO2 variability decreased, and in some lakes resulted in Delta CO2 contrib-uting more thank to FCO2 variability over the full ice-free period. Increased contribution of Delta CO2 to FCO2 vari-ability over time occurred across all lakes but was most apparent in large-volume southern-boreal lakes and indeeper (>2m) parts of lakes, whereaskwas linked to FCO(2 )variability in shallow waters. Accordingly, knowing the variability of bothk and Delta CO(2 )over time and space is needed for accurate modeling of F CO2 from these vari-ables. We conclude that priority in FCO(2 )assessments should be given to direct measurements of FCO2 at multiplesites when possible, or otherwise from spatially distributed measurements of Delta CO(2 )combined with k- models that incorporate spatial variability of lake thermal structure and meteorology.

The spread of infectious livestock diseases is a major cause for concern in modern agricultural systems. In the dynamics of the transmission of such diseases, movements of livestock between herds play an important role. When constructing mathematical models used for activities such as forecasting epidemic development, evaluating mitigation strategies, or determining important targets for disease surveillance, including between -premises shipments is often a necessity. In the United States (U.S.), livestock shipment data is not routinely collected, and when it is, it is not readily available and mostly concerned with between-state shipments. To bridge this gap in knowledge and provide insight into the complete livestock shipment network structure, we have developed the U.S. Animal Movement Model (USAMM). Previously, USAMM has only existed for cattle shipments, but here we present a version for domestic swine. This new version of USAMM consists of a Bayesian model fit to premises demography, county-level livestock industry variables, and two limited data sets of between-state swine movements. The model scales up the data to simulate nation-wide networks of both within-and between-state shipments at the level of individual premises. Here we describe this shipment model in detail and subsequently explore its usefulness with a rudimentary predictive model of the prevalence of porcine epidemic diarrhea virus (PEDv) across the U.S. Additionally, in order to promote further research on livestock disease and other topics involving the movements of swine in the U.S., we also make 250 synthetic premises-level swine shipment networks with complete coverage of the entire conterminous U.S. freely available to the research community as a useful surrogate for the absent shipment data.

Livestock movements between agricultural premises is an important pathway for the spread of infectious disease. Data providing details about the origin and destination of shipments, as well as information about the shipment size is an important component of computer models used to formulate mitigation strategies and design surveillance programs. The United States (U.S.) currently lacks a comprehensive database of farm animal shipments, which hinders such efforts. With the U.S. Animal Movement Model (USAMM), earlier work has successfully scaled up from limited data based on interstate certificates of veterinary inspection (CVI) to comprehensive county-level shipment networks at the national scale. In this work, we present three major improvements to earlier versions of USAMM: (1) increased resolution of the model and simulated networks to the level of individual premises; (2) predictions of shipment sizes; (3) taking into account the types and herd sizes of the premises. We fitted parameters in a Bayesian framework to two sets of CVI data consisting of sub-samples of one years between-state beef and dairy shipments. Through posterior predictive simulation, we then created 1,000 synthetic beef and dairy networks, which we make publicly available to support livestock disease modeling. The simulated networks were validated against summary statistics of the training data as well as out-of-sample CVI data from subsequent years. This new development opens up the possibility of using USAMM in a broader spectrum of applications where information about shipment size and premises identity is necessary and gives novel insights into the U.S. cattle shipment network.

Finding alternative phosphorus sources is imperative to address negative environmental and societal impacts caused by its current inefficient use. However, the direct use of phosphorus in sewage sludge in agriculture is controversial. This paper uses Denmark, Germany, and Spain as case examples to assess relevant legislation and boundary conditions in agricultural production to identify opportunities and barriers for the utilisation of recycled phosphorus from wastewater in agriculture on a regional level. Only five out of 22 phosphorus recycling technologies considered were in full compliance with legislation across all three countries, and these five were then assessed for their potential to supply phosphorus to major crops within countries. We considered the application of technologies across four scenarios: 1) struvite; 2) vivianite as iron supply; 3) vivianite for calcium phosphate precipitation; and 4) ashes for calcium phosphate precipitation. The most suitable scenario identified for Denmark was vivianite for calcium phosphate precipitation, whereas in Spain vivianite as iron supply was identified as most suitable, and ashes for calcium phosphate in Germany. We found that in 2018, the potential phosphorus supply from recycling technologies was on average 0.38, 0.29 and 0.05 kg of phosphorus per capita for Danish, German, and Spanish regions. These quantities could meet 9.1, 21.7, and 10.0 percent of the phosphorus required to produce major cereals in each country (specifically wheat, barley, and rye). Given current legal constraints, wastewater treatment plant connections and agronomic context, the potential contribution of recycled phosphorus is non-negligible in many sub-national regions. Still, to access the full potential of phosphorus circularity clear product specifications and transport and logistics among regions will be necessary.

Urban agriculture, as most agriculture, can potentially contribute to eutrophication via losses to ground and surface water. Few published studies have empirically measured nitrogen and phosphorus losses (including leaching) from urban agriculture, and even fewer have examined losses in real-world settings throughout the year. Here we investigated year-round (May 2020-2021) weekly nitrogen and phosphorus leaching from allot-ment gardens in Linko center dot ping, southern Sweden. We installed eight lysimeters (8 plots) and collected water 0.3 m below the soil surface in four gardens (2 plots per garden), each with their own gardening practices (organic fertilizers, irrigation, and crops). The gardens exhibited large nutrient leaching per area cultivated compared to observed nutrient leachate in rural agriculture in similar climates. There was a large variability among studied plots, where nitrogen leaching reached 39-191 kg ha-1 y-1 and phosphorus 0.9-2.4 kg ha-2 y-1. Importantly, the non-growing season, especially snowmelt, was a key period for leaching. Most of the nitrogen (78-91 %) and phosphorus (45-97 %) leaching occurred from November to April when the soil was bare, suggesting that mineralization of organic matter was important. Three of the gardens received high amounts of organic fertil-izers, though no clear relation between inputs and leaching could be discerned. One plot deviated from the pattern, with less than 40 % of the nutrient leaching occurring in the non-growing season. This gardener had a fine net covering the plot to deter insects. This protected from precipitation as the water volume collected was the lowest, with only 26 % collected in the non-growing season, and nitrogen leaching was also the lowest. Our results illustrate that additional monitoring studies should occur year-round and in several gardens to account for high temporal and spatial heterogeneity and avoid under-estimating leaching losses from urban agriculture. Providing guidance on fertilization, irrigation, and soil covering may be a way to minimize leaching.

Urban gardens and farms typically use compost as a source of nutrients, often at levels that exceed crop nutrient demands. Although land dedicated to agriculture is a small fraction of urban land use, high input rates coupled with low nutrient use efficiencies suggest that export of nitrogen (N) and phosphorus (P) from this land could be potentially important contributors to urban nutrient budgets. We used the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) Nutrient Delivery Ratio model to examine the potential impact of garden density, compost input rates, and nutrient retention efficiency on N and P export from stormwater runoff for a 737-ha urban residential area in Saint Paul, Minnesota. Although gardens and farms accounted for 0.1-0.5% of land area in our scenarios, compost inputs accounted for as much as 33% of N inputs and 85% of P inputs to the urban landscape. The contribution of gardens to urban nutrient export through stormwater runoff is highly dependent on modeled maximum retention efficiency values. If retention efficiency is high, gardens with low compost inputs are similar to other vegetated land uses in contributions to nutrient export, but gardens become significant contributors to watershed P export if compost inputs are high, or if retention efficiency drops to 75% or lower. These results underscore mass-balance constraints inherent in urban nutrient recycling and highlight the importance of understanding the long-term fate of excess nutrients applied to urban landscapes.

Addressing wastewater infrastructure needs in urban informal settlements must simultaneously address legacies of past failures, current aspirations and constraints, as well as increasingly changing needs related to global environmental change. This study applied the Sustainable System-of-Systems framework for ergonomics and human factors to gain a better understanding of how small in-situ constructed wetlands could be a form of greywater treatment infrastructure in an informal settlement. Using 24 months of interviews, surveys, workshops and photo-ethnographic observations, we identified that the rapidly changing nature of parent (e.g. residency transience and land ownership) and sibling (e.g. housing and drinking water) systems put pressure on the target wetland system to adapt, often decreasing its capacity to deliver the service of water cleaning. Greywater treatment was not a common goal among stakeholders involved in the nested hierarchy system which likely contributed to the constructed wetlands needing to adapt to remain relevant. Practitioner summary: The value of the Sustainable Systems-of-Systems framework for ergonomics/human factors professionals in determining the sustainability of an ergonomics/human factors intervention is demonstrated using a greywater treatment system case study for an urban informal settlement. Understanding the variety of stakeholder goals and the pace of change in related systems was key to a sustainable intervention.

Foot-and-mouth disease (FMD) is a fast-spreading viral infection that can produce large and costly outbreaks in livestock populations. Transmission occurs at multiple spatial scales, as can the actions used to control outbreaks. The US cattle industry is spatially expansive, with heterogeneous distributions of animals and infrastructure. We have developed a model that incorporates the effects of scale for both disease transmission and control actions, applied here in simulating FMD outbreaks in US cattle. We simulated infection initiating in each of the 3049 counties in the contiguous US, 100 times per county. When initial infection was located in specific regions, large outbreaks were more likely to occur, driven by infrastructure and other demographic attributes such as premises clustering and number of cattle on premises. Sensitivity analyses suggest these attributes had more impact on outbreak metrics than the ranges of estimated disease parameter values. Additionally, although shipping accounted for a small percentage of overall transmission, areas receiving the most animal shipments tended to have other attributes that increase the probability of large outbreaks. The importance of including spatial and demographic heterogeneity in modelling outbreak trajectories and control actions is illustrated by specific regions consistently producing larger outbreaks than others. © 2019 The Author(s) Published by the Royal Society. All rights reserved.

It has typically been assumed that habitat destruction, characterized by habitat loss and fragmentation, has consistently negative effects on biodiversity. While numerous empirical studies have shown the detrimental effects of habitat loss, debate continues as to whether habitat fragmentation has universally negative effects. To explore the effects of habitat fragmentation, we developed a simple model for site-occupancy dynamics in fragmented landscapes. With the model, we demonstrate that a competition-colonization trade-off can result in nonlinear oscillatory responses in biodiversity to both habitat loss and fragmentation. However, the overall pattern of habitat loss reducing species richness is still established, in line with empirical observations. Interestingly, the existence of localized oscillations in biodiversity can explain the mixed responses of species richness to habitat fragmentation per se observed in nature, thereby reconciling the debate on the fragmentation-diversity relationship. Therefore, this study offers a parsimonious mechanistic explanation for empirically observed biodiversity patterns in response to habitat destruction.