Forest dynamics at regional scales: predictive models constrained with inventory data

Forest ecosystems store more carbon than the atmosphere and harbour the majority of the world's biodiversity, yet their response to changing climate is uncertain. Forest simulation models make landscape-level predictions of forest dynamics by scaling from key tree-level processes, but models typically have no climate dependency. In this thesis I demonstrate how large-scale national inventories combined with improvements in computational methods mean that models that incorporate the climate dependency of demographic processes may be parameterised at regional scales. In Chapter One I outline historical approaches to modelling forest dynamics and present a discussion of competing methods of parameterisation and model selection. In Chapter Two I present a model of individual tree mortality in the eastern United States which incorporates species, climatic and competitive effects parameterised using Markov Chain Monte Carlo methods. The remainder of the thesis concentrates on modelling Spanish forest dynamics, so in Chapter Three I present a brief introduction to Spanish forest ecology. In Chapter Four I examine how aboveground allometry - the scaling of tree height and crown shape - varies with climate and competition in Spain for 26 species. Hierarchical modelling suggests that scaling theories based on wood properties do not explain differences between species, but climatic factors, and in particular hydraulic limitations, do. In Chapter Five I parameterise a model of recruitment in Spanish forests using Approximate Bayesian Computation, a novel computational method which allows parameterisation of individual-based models without individual-based data, and demonstrate that it produces ecologically reasonable results. Chapter Six presents a forest dynamics model parameterised for the major native species in Spain and tests whether it is able to reproduce observed species-climate distributions. Finally, in Chapter Seven I discuss the main findings of the thesis and avenues for extending this research.This work was supported by a grant from Microsoft Research to the University of Cambridg

Studies to assess the effect of pet training aids specifically remote static pulse systems on the welfare of domestic dogs

This project assessed the welfare of dogs trained with pet training aids, specifically remote static pulse collar systems (e-collars). Previous work has focused on a very limited number of devices in a very limited range of contexts and the evidence of the impact of such devices on dog's overall quality of life is inconclusive. Project AW1402 aimed to assess the physical characteristics of the e-collars and the physiological, behavioural and psychological consequences of their use in dog training in four objectives. 1. Investigate the resistance in the neck skin of a range of dogs 2. Measure the physical output properties of the devices under investigation 3. Evaluate methods for recording behavioural/psychological measures of emotional state in the context of dog training. 4. Investigate the long term behavioural, physiological and psychological effects of using training devices in the domestic dog A representative selection of e-collars was purchased to allow the assessment of electrical properties in laboratory tests and the evaluation of manuals (Objective 2). As the electrical output of the e-collars depended on the impedance presented by the dogs' necks, this was measured first on a sample of dogs of a number of breed and cross-breeds under dry and wet conditions (Objective 1). This was done under supervision of an animal welfare specialist and did not cause pain or distress as indicative from the dogs' behaviours. The impedance of dogs can be modelled as a passive resistance with a value of about 10kΩ (10th -90th percentile range 4 – 150kΩ) for wet dogs and 600kΩ (22 – 950kΩ) for dry dogs. The momentary stimulus generated by the e-collars comprised a sequence of identical short voltage pulses. The continuous stimulus comprised a much longer sequence of the same voltage pulses. There were considerable differences between tested e-collar models in the voltages, the number of pulses in, and length of each stimulus, but little variation within individual models of e-collars. The peak voltage delivered by e-collars varied significantly with the resistance of the dog, from as much as 6000V at 500kΩ to 100V at 5kΩ. The highest voltages were generated for only a few millionths of a second. To allow meaningful comparisons between e-collars (taking into account the differences in electrical characteristics), a stimulus strength ranking indicator (SSRI) was developed. This showed differences between the selected e-collars, as well as differences in the relationship between momentary and continuous stimuli. Manuals were clear on operation, but gave varying levels of information on using the e-collar in training. Generally they did not adequately explain their full potential, for instance with respect to using the tone or vibrate functions. Advice in manuals was not always taken up by end-users as evident from responses in owner questionnaire collected as part of objectives 3 and 4. A pilot study involving 10 dogs with prior experience of e-collars and 10 control dogs (matched by age, sex, breed and where possible behavioural problem) was conducted to develop and evaluate protocols for assessing dog welfare in home and training environments (Objective 3). This was followed by a larger field study (Objective 4) involving 65 dogs with prior experience of e-collar training and 65 matched controls. Cases and matched controls for Objective 4 were initially recruited from a separate training methods survey distributed to dog owners to reduce sampling bias, but this was later supplemented by other recruitment methods. Data collection in Objective 4 included:- 1. An owner questionnaire to collect demographic data on dogs and owners; and owner-reports of behaviour during training and efficacy of training methods. 2. First passage urine to measure cortisol, creatinine, and metabolites of the neuro-transmitters serotonin (5-HIAA) and dopamine (HVA). 3. Saliva for assay of cortisol prior to and during training. 4. Observations of dog behaviour during fitting of inactivated e-collar 5. Observation of dog behaviours during a series of standard training tasks (“stay”, “leave” and “recall” and the situation for which the focal device was used) given by both owner and a researcher and conducted in the context where the focal device had been originally used for training. Each set of tests were repeated both without (Test 1) and with (Test 2) the wearing of a dummy or inactivated e-collar to enable comparisons to be made between measures for the same dogs when wearing an e-collar (which may predict the application of stimulus for the dog) and not. 6. A spatial discrimination task designed to use judgement bias to assess underlying affective state. Questionnaire data included type of device used, time since use, owner perceptions of the success of training, and owner reports of behavioural responses to use. Training methods used by owners in the control group could be sub-divided into those mainly using positive reinforcement (reward based) training, and those using methods based largely on punishment or negative reinforcement. Most owners (68%) purchased e-collars new, mainly from the internet, though some owners borrowed or purchased second hand collars. Problems with recall (40%) and livestock worrying (33%) accounted for the majority of reasons for e-collars use, although some manuals included information on use for basic obedience. Owner reports on operation of devices suggested they were often unclear as to how best use e-collars in training and some appeared not to have followed manual advice (if available). 36% of owners reported vocalisations on first use, and 26% on subsequent use of e-collars. This suggested that operating levels may not have been set in accordance with manufacturer’s instruction (where available), though due to owners often being unable to recall how they used the device this could not always be verified. Owners reported the addressed behaviours to be more severe in e-collar trained dogs than the controls. Owners showed a high degree of satisfaction with the effectiveness of all the training approaches used, though owners from the e-collar group were more likely to state they would prefer to try other forms of training in the future. No significant differences between groups were identified for behaviours shown during collar fitting, although a wide range of behavioural responses among dogs were noted. These differences were considered likely to reflect response to novelty in the control group, and the specific events that usually followed collar fitting in the e-collar group such no consequence, going for a walk or stimulus application. Because of high variability between dogs, it was considered that differences in measures between the first series of training tasks (Test 1; conducted with no collar) and the second series of training tasks (Test 2; conducted with dogs wearing a dummy collar) would be more reliable than absolute differences between groups. There was a significant increase in salivary cortisol between tests in the e-collar group compared to the sub-group of dogs trained using positive reinforcement. A behavioural scale incorporating proportion of training period tense, an inverse of proportion of training time relaxed, and proportion of time with attention directed at owner (whoever was training) significantly increased in the e-collar group, as compared to both the whole control group and the sub-set of dogs predominantly trained using positive reinforcement. These differences may reflect increased emotional arousal in e-collar dogs as a result of previous learned associations with the collar. Data was collected for a further 11 control dogs who experienced both sets of standard training tasks but wearing no collar to test for potential order effects. Their behavioural and physiological responses were consistent with control dogs who wore the e-collar for the second set of tasks. There was some evidence of higher baseline cortisol in control dogs compared with e-collar dogs in both the urinary cortisol: creatinine (reflecting cortisol production overnight before researcher arrival) and baseline salivary cortisol (taken after the arrival of the researcher and likely to be influenced by the events associated with visitor arrival and greeting) particularly when considering just the positive reinforcement sub-group. However these differences were small and found not to be significant when a multiple comparison Bonferroni correction was applied. There were no significant differences in neurotransmitter metabolites between the two groups. Neither were there significant differences between control and e-collar dogs with respect to speed to ambiguous probes in the judgement bias task. However, in the latter case, group effects were confounded by strong effects of arena size where different test spaces had been used. Overall, this project has highlighted the very variable outcomes between individual dogs when trained using e-collars. The combination of differences in individual dog’s perception of stimuli, different stimulus strength and characteristics from collars of different brands, differences between momentary and continuous stimuli, differences between training advice in manuals, differences in owner understanding of training approaches and how owners use the devices in a range of different circumstances are likely to lead to a wide range of training experiences for pet dogs. This variability in experience is evidenced in the data from trained dogs such as owner reports of their dogs’ response to e-collar use. Significant differences were, however, found in data collected from e-collar and control dogs undergoing standard training tests with and without dummy e-collars. These included a difference in the change in salivary cortisol between tests with e-collar dogs showing an increase and positive reinforcement dogs showing a lowering of salivary cortisol between the tests. There were also behavioural changes that were consistent with changes in emotional state, with e-collar dogs showing an increase in a behavioural scale incorporating time spent tense and the inverse of time relaxed between the two situations. These training tasks were designed as far as possible to replicate the context where e-collar training had occurred in the past, and indicate a shift towards higher levels of physiological and behavioural arousal in the e-collar dogs as well as a tendency to focus more on the owner than when they had not been wearing a collar. Thus it seems reasonable to conclude that the previous use of e-collars in training is associated with behavioural and physiological responses that are consistent with negative emotional states. It is therefore suggested that the use of e-collars in training pet dogs leads to a negative impact on welfare, at least in a proportion of animals trained using this technique

Familien in der Coronapandemie: Was hat belastet, was hat geholfen und was kann man für zukünftige Krisenstrategien lernen?

In der Pandemie ist die Lebenszufriedenheit deutlich gesunken, vor allem in Zeiten von Kontaktbeschränkungen. Nach Lockerung der Restriktionen ist die Lebenszufriedenheit wieder etwas angestiegen. Um gut durch Krisen zu kommen, sind u.a. drei Dinge wichtig: Gute Beziehungen in der Familie, finanzielle Sicherheit und die Fähigkeit zu Optimismus. Familie zu haben, und dabei vor allem eine gute Beziehungsqualität in der Partnerschaft und zu den Kindern, war in der Pandemie zentral für das Wohlbefinden. Familienpolitik ist in Krisenzeiten besonders wichtig. Dies umfasst verlässliche, ganztägige Kita- und Schulbetreuung, niederschwellige psychosoziale Beratungsangebote für Kinder und Jugendliche sowie familienfreundlichere Arbeitsplätze. Etwa ein Drittel der Menschen im mittleren Alter hatte ernsthafte finanzielle Sorgen in der Pandemie, was mit einer niedrigeren Lebenszufriedenheit verbunden war. Politische Maßnahmen wie Kurzarbeit (oder bei der Energiekrise die Gaspreisbremse), die finanzielle Risiken schnell abfedern, sind hier hilfreich. Die Fähigkeit, auch in Krisen positive Seiten zu sehen, hängt eng mit einer höheren Lebenszufriedenheit zusammen. Eine Prise Optimismus und ein Blick auf andere Lebensaspekte können in Krisen zu Resilienz beitragen. Politik sollte die Chancen, die sich aus Krisen ergeben, angehen und unterstützende Maßnahmen breit kommunizieren

Competitive drivers of interspecific deviations of crown morphology from theoretical predictions measured with Terrestrial Laser Scanning

Abstract: Tree crown morphology is a key driver of forest dynamics, determining not only the competitiveness of an individual but also the competitive effect exerted on neighbouring trees. Multiple ecological theories, including metabolic scaling theory (MST), predict crown morphology from first principles, but typically lack consideration of competition. The accurate quantification of crown morphology to test theoretical predictions, and the canopy interactions that could alter them, has historically been limited by the simplicity and associated error of traditional crown measurements. In this study, we calculate high‐resolution two‐ and three‐dimensional crown metrics from Terrestrial Laser Scanning data for 1,441 Pinus sylvestris, P. nigra, Quercus faginea and Q. ilex trees from a water‐limited forest community in central Spain and test height‐crown metric scaling relationships. We demonstrate new TLS methods to define symmetric and asymmetric neighbourhood metrics based on tree height, crown size and neighbour projected crown area, and test the importance of neighbourhood genus diversity on crown morphology by separating competition from congeneric and heterogeneric neighbours. Competition negatively impacted all crown metrics except crown depth where only P. nigra showed sensitivity. Asymmetric competition was the strongest driver of pine crown morphology, but oaks were more sensitive to symmetric competition, in line with shade tolerance expectations. Congeneric competition reduced Q. faginea crown size and changed its shape, but we found no significant effects of heterogeneric neighbours. Most species and crown dimensions had height‐crown scaling exponents below those predicted by MST, which may be due to water limitation effects. Pines and oaks showed large differences in crown depth to height scaling, with the former shallower and the latter deeper, in contrast to theoretical predictions. Synthesis. Our study is the first to demonstrate the ability of TLS to characterise crown morphology from leaf‐wood separated clouds and competitive neighbour effects in a water‐limited forest community, and to use TLS metrics to test ecological crown scaling theory. Most crown metrics scaled below theoretical predictions. Pines were more sensitive to competition by larger neighbours and oaks to crowding from all neighbours, with competition from neighbours of the same genus having a consistent negative effect

Bioclimatic envelope models predict a decrease in tropical forest carbon stocks with climate change in Madagascar

Recent studies have underlined the importance of climatic variables in determining tree height and biomass in tropical forests. Nonetheless, the effects of climate on tropical forest carbon stocks remain uncertain. In particular, the application of process-based dynamic global vegetation models has led to contrasting conclusions regarding the potential impact of climate change on tropical forest carbon storage. Using a correlative approach based on a bioclimatic envelope model and data from 1771 forest plots inventoried during the period 1996–2013 in Madagascar over a large climatic gradient, we show that temperature seasonality, annual precipitation and mean annual temperature are key variables in determining forest above-ground carbon density. Taking into account the explicative climate variables, we obtained an accurate (R2 = 70% and RMSE = 40 Mg ha−1) forest carbon map for Madagascar at 250 m resolution for the year 2010. This national map was more accurate than previously published global carbon maps (R2 ≤ 26% and RMSE ≥ 63 Mg ha−1). Combining our model with the climatic projections for Madagascar from 7 IPCC CMIP5 global climate models following the RCP 8.5, we forecast an average forest carbon stock loss of 17% (range: 7–24%) by the year 2080. For comparison, a spatially homogeneous deforestation of 0.5% per year on the same period would lead to a loss of 30% of the forest carbon stock. Synthesis. Our study shows that climate change is likely to induce a decrease in tropical forest carbon stocks. This loss could be due to a decrease in the average tree size and to shifts in tree species distribution, with the selection of small-statured species. In Madagascar, climate-induced carbon emissions might be, at least, of the same order of magnitude as emissions associated with anthropogenic deforestation

The Interplay of the Tree and StandLevel Processes Mediate DroughtInduced Forest Dieback: Evidence from Complementary Remote Sensing and Tree-Ring Approaches

Moreno-Fernández, D., Camarero, J.J., García, M. et al. The Interplay of the Tree and Stand-Level Processes Mediate Drought-Induced Forest Dieback: Evidence from Complementary Remote Sensing and Tree-Ring Approaches. Ecosystems 25, 1738-1753 (2022).Drought-induced forest dieback can lead to a tipping point in community dominance, but the coupled response at the tree and stand-level response has not been properly addressed. New spatially and temporally integrated monitoring approaches that target different biological organization levels are needed. Here, we compared the temporal responses of dendrochronological and spectral indices from 1984 to 2020 at both tree and stand levels, respectively, of a drought-prone Mediterranean Pinus pinea forest currently suffering strong dieback. We test the influence of climate on temporal patterns of tree radial growth, greenness and wetness spectral indices; and we address the influence of major drought episodes on resilience metrics. Tree-ring data and spectral indices followed different spatio-temporal patterns over the study period (1984?2020). Combined information from tree growth and spectral trajectories suggests that a reduction in tree density during the mid-1990s could have promoted tree growth and reduced dieback risk. Additionally, over the last decade, extreme and recurrent droughts have resulted in crown defoliation greater than 40% in most plots since 2019. We found that tree growth and the greenness spectral index were positively related to annual precipitation, while the wetness index was positively related to mean annual temperature. The response to drought, however, was stronger for tree growth than for spectral indices. Our study demonstrates the value of long-term retrospective multiscale analyses including tree and stand-level scales to disentangle mechanisms triggering and driving forest dieback.Ministerio de Ciencia, Innovacion y UniversidadesUniversidad de AlcaláMinisterio de Ciencia e InnovaciónComunidad de MadridUK Research and Innovatio

Nature-based solutions for climate change in the UK: a report by the British Ecological Society

Nature-based solutions (NbS) address societal problems in ways that benefit both people and nature. The main focus of this report is the joint role of NbS for addressing the climate and biodiversity crises we currently face. Natural habitats act as NbS for climate if they sequester carbon (contributing to Net Zero targets) or provide adaptation to climate change effects (for example, reducing flooding, protecting coastline against sea-level rise or creating cool spaces in cities). As well as these climate benefits, they can enhance biodiversity, create improved and more resilient ecosystem functioning, enhance human wellbeing and provide economic benefits, in terms of monetary value and job creation. Despite the huge range of benefits NbS have, they should be seen as complementary to other climate and conservation actions, not as a replacement to them. This Executive Summary provides five key themes which emerge across the report, across the multiple habitats and multiple NbS studied. Six ‘priority’ habitats for NbS are given at the end of the summary. However, we emphasise that all habitats covered in the report can act as NbS and all can play a role in addressing the climate and biodiversity crises

Available and missing data to model impact of climate change on European forests

Climate change is expected to cause major changes in forest ecosystems during the 21st century and beyond. To assess forest impacts from climate change, the existing empirical information must be structured, harmonised and assimilated into a form suitable to develop and test state-of-the-art forest and ecosystem models. The combination of empirical data collected at large spatial and long temporal scales with suitable modelling approaches is key to understand forest dynamics under climate change. To facilitate data and model integration, we identified major climate change impacts observed on European forest functioning and summarised the data available for monitoring and predicting such impacts. Our analysis of c. 120 forest-related databases (including information from remote sensing, vegetation inventories, dendroecology, palaeoecology, eddy-flux sites, common garden experiments and genetic techniques) and 50 databases of environmental drivers highlights a substantial degree of data availability and accessibility. However, some critical variables relevant to predicting European forest responses to climate change are only available at relatively short time frames (up to 10-20 years), including intra-specific trait variability, defoliation patterns, tree mortality and recruitment. Moreover, we identified data gaps or lack of data integration particularly in variables related to local adaptation and phenotypic plasticity, dispersal capabilities and physiological responses. Overall, we conclude that forest data availability across Europe is improving, but further efforts are needed to integrate, harmonise and interpret this data (i.e. making data useable for non-experts). Continuation of existing monitoring and networks schemes together with the establishments of new networks to address data gaps is crucial to rigorously predict climate change impacts on European forests. © 2019 The Author(s