Effects of human-induced land degradation on water and carbon fluxes in two different Brazilian dryland soil covers.

The Brazilian semiarid region presents a physical water scarcity and high seasonal and interannual irregularities of precipitation, known as a region with periodic droughts. This region is mainly covered by the Caatinga biome, recognized as a Seasonally Dry Tropical Forest (SDTF). Soil water availability directly impacts the ecosystem's functioning, characterized by low fertility and sparse vegetation cover during the dry season, making it a fragile ecosystem vulnerable to climatic variations. Additionally, this region has been suffering from several issues due to human activities over the centuries, which has resulted in extensive areas being severely degraded, which aggravates the impacts from climatic variations and the susceptibility to desertification. Thus, studying the soil-plant-atmosphere continuum in this region can help better understand the seasonal and annual behavior of the water and carbon fluxes. This study investigated the dynamics of water and carbon fluxes during four years (2013-2016) by using eddy covariance (EC) measurements within two areas of Caatinga (dense Caatinga (DC) and sparse Caatinga (SC)) that suffered anthropic pressures. The two study areas showed similar behavior in relation to physical parameters (air temperature, incoming radiation, vapor pressure deficit, and relative humidity), except for soil temperature. The SC area presented a surface temperature of 3 °C higher than the DC, related to their vegetation cover differences. The SC area had higher annual evapotranspiration, representing 74% of the precipitation for the DC area and 90% for the SC area. The two areas acted as a carbon sink during the study period, with the SC area showing a lower CO2 absorption capacity. On average, the DC area absorbs 2.5 times more carbon than the SC area, indicating that Caatinga deforestation affects evaporative fluxes, reducing atmospheric carbon fixation and influencing the ability to mitigate the effects of increased greenhouse gas concentrations in the atmosphere.

Evaluating effects of different control strategies for Infectious Salmon Anaemia (ISA) in marine salmonid farming by scenario simulation using a disease transmission model.

Infectious salmon anaemia (ISA) is an important viral disease causing economic losses and reduced welfare in farmed Atlantic salmon. Here, we present a spatio-temporal stochastic model for the spread of ISA between and within marine aquaculture farms. The model is estimated on historical production data for all marine salmonid farms in Norway from 2004 to February 2019. In this time 142 outbreaks of ISA occurred. We find that transmission from infected neighbouring farms accounts for around 50% of the infections, whereas transmission from "non-specified sources" accounts for around 40%. We hypothesise that the most important of the latter are viruses mutating from the non-virulent ISAV HPR0 to the virulent ISAV HPRdel. The model is used for scenario simulation, or what-if analysis, to investigate the effects of potential strategies to combat ISA, including screening, vaccination and culling. Changing from the current strategy of culling farms with detected ISA-outbreaks to mandatory screening and culling when virus is detected will reduce the fraction of cohorts with a clinical ISA outbreak from 3.8 to 0.36%. Introducing mandatory vaccination would have approximately the same effect as the current stamping-out strategy. The scenario simulation is a useful tool for deciding on appropriate mitigation measures.

Seasonal variation of surface radiation and energy balances over two contrasting areas of the seasonally dry tropical forest (Caatinga) in the Brazilian semi-arid.

Arid and semi-arid environments correspond to one-third of the Earth's terrestrial surface. In these environments, precipitation is an essential and limiting element for vegetation growth and ecosystem biomass productivity. The semi-arid region of Brazil comprises around 11.5% of the national territory, where the Caatinga biome originally composed ~ 76% of this area, with water deficit as a prominent feature, annual rainfall lower than 800 mm, temperatures ranging between 25 and 30 °C, and potential evapotranspiration higher than 2000 mm/year. Research on the dynamics of mass and heat fluxes through techniques such as eddy covariance (EC) has contributed to estimate the magnitude and seasonal patterns of turbulent exchanges between ecosystems and the atmosphere. This study was conducted in an area of dense Caatinga (DC) and another of sparse Caatinga (SC) from 2013 to 2014. It was observed that albedo (α) and net radiation (Rn) were higher in the SC compared with DC since the magnitude of incoming shortwave radiation was higher in this area. It was found that most of the Rn is converted to sensible heat flux (H), mainly during the dry period in the SC, about 50% for H and 20% for λE. The energy balance closure showed that the turbulent fluxes (H + λE) were underestimated in comparison to the available energy at the surface (Rn - G). We also observed that this discrepancy was higher in the DC area, corresponding to ~ 30%.

Seasonal variation in net ecosystem CO2 exchange of a Brazilian seasonally dry tropical forest.

Forest ecosystems sequester large amounts of atmospheric CO2, and the contribution from seasonally dry tropical forests is not negligible. Thus, the objective of this study was to quantify and evaluate the seasonal and annual patterns of CO2 exchanges in the Caatinga biome, as well as to evaluate the ecosystem condition as carbon sink or source during years. In addition, we analyzed the climatic factors that control the seasonal variability of gross primary production (GPP), ecosystem respiration (Reco) and net ecosystem CO2 exchange (NEE). Results showed that the dynamics of the components of the CO2 fluxes varied depending on the magnitude and distribution of rainfall and, as a consequence, on the variability of the vegetation state. Annual cumulative NEE was significantly higher (p < 0.01) in 2014 (-169.0 g C m-2) when compared to 2015 (-145.0 g C m-2) and annual NEP/GPP ratio was 0.41 in 2014 and 0.43 in 2015. Global radiation, air and soil temperature were the main factors associated with the diurnal variability of carbon fluxes. Even during the dry season, the NEE was at equilibrium and the Caatinga acted as an atmospheric carbon sink during the years 2014 and 2015.

A partly stage-structured model for the abundance of salmon lice in salmonid farms.

The parasitic salmon louse constrains growth in the Norwegian salmon farming industry through density dependent host-parasite interaction. Hence, there is a need for insight into how increases in salmon production, varying spatial organisation of the production and pest control strategies affect salmon louse population dynamics. Here we present a new salmon louse model for exploring effects of varying salmon farming conditions on spatio-temporal abundances of the parasite. The salmon louse model is partly stage-structured, comprising of (i) adult female lice and (ii) other mobile stages of lice. The abundance of adult females depend on survival of females from previous weeks and recruitment from the other mobile group of lice. The other mobiles also depend on survival of other mobiles from previous weeks, as well as recruitment from the previous generation of reproducing adult females from the same farm or from farms in the neighbourhood. In addition, expected abundances of the two stage-groups are modelled as functions of biological and physical covariates. The model is fitted to weekly salmon farm data covering all marine farms producing salmonids along the Norwegian cost over the years 2012-2016. Among novel results from fitting the model are estimates of the time-lag structure representing recruitment of other mobile lice from the previous generation adult females for different temperatures. Furthermore, the model estimates how various factors affect the susceptibility of fish on farms to louse infection and effects of treatments to control infection. Finally, the model estimates density dependent effects of increasing the number of fish in farms and of increasing the numbers of farms, on the rate of recruitment of other mobile lice. Analytically, the parameters representing density dependencies suggest that few farms with many fish and large between farm distances is effective in terms of minimising the overall output of salmon lice infection.

The epidemiological and economic effects from systematic depopulation of Norwegian marine salmon farms infected with pancreas disease virus.

Pancreas disease (PD) is a viral disease associated with significant economic losses in Scottish, Irish, and Norwegian marine salmon aquaculture. In this paper, we investigate how disease-triggered harvest strategies (systematic depopulation of infected marine salmon farms) towards PD can affect disease dynamics and salmon producer profits in an endemic area in the southwestern part of Norway. Four different types of disease-triggered harvest strategies were evaluated over a four-year period (2011-2014), each scenario with different disease-screening procedures, timing for initiating the harvest interventions on infected cohorts, and levels of farmer compliance to the strategy. Our approach applies a spatio-temporal stochastic model for simulating the spread of PD in the separate scenarios. Results from these simulations were then used in cost-benefit analyses to estimate the net benefits of different harvest strategies over time. We find that the most aggressive strategy, in which infected farms are harvested without delay, was most efficient in terms of reducing infection pressure in the area and providing economic benefits for the studied group of salmon producers. On the other hand, lower farm compliance leads to higher infection pressure and less economic benefits. Model results further highlight trade-offs in strategies between those that primarily benefit individual producers and those that have collective benefits, suggesting a need for institutional mechanisms that address these potential tensions.

Space-time modelling of the spread of pancreas disease (PD) within and between Norwegian marine salmonid farms.

Infectious diseases are a constant threat to industrialised farming, which is characterised by high densities of farms and farm animals. Several mathematical and statistical models on spatio-temporal dynamics of infectious diseases in various farmed host populations have been developed during the last decades. Here we present a spatio-temporal stochastic model for the spread of a disease between and within aquaculture farms. The spread between farms is divided into several transmission pathways, including (i) distance related spread and (ii) other types of contagious contacts. The within-farm infection dynamics is modelled by a susceptible-infected-recovered (SIR) model. We apply this framework to model the spread of pancreas disease (PD) in salmon farming, using data covering all farms producing salmonids over 9 years in Norway. The motivation for the study was partly to unravel the spatio-temporal dynamics of PD in salmon farming and partly to use the model for scenario simulation of PD control strategies. We find, for example, that within-farm infection dynamics vary with season and we provide estimates of the timing from unobserved infection events to disease outbreaks on farms are detected. The simulations suggest that if a strategy involving culling of infectious cohorts is implemented, the number of detected disease outbreaks per year may be reduced by 57% after the full effect has been reached. We argue that the high detail and coverage of data on salmonid production and disease occurrence should encourage the use of simulation modelling as a means of testing effects of extensive control measures before they are implemented in the salmon farming industry.

Sexually active groups in cattle-a novel estrus sign.

The current study presents a novel objective measure for characterizing sexually active groups (SAG 3-5) and relates this measure to other behaviors of lactating Holstein-Friesian cows. Cows in SAG 3-5 were required to participate in a minimum of 1 estrus behavior per 5min while staying within 3m (2 cow lengths) of its partner(s) for a minimum of 5min. Twenty Holstein-Friesian cows were video-monitored continuously through 1 complete estrous cycle (22d). Standing behavior, SAG 3-5, secondary estrus signs (SEC), and other social and agonistic behaviors were recorded continuously. The period of mounting estrus (MTE) was divided into the 3 parts: prestand, standing estrus (STE), and poststand. The mean durations of MTE, prestand, STE, and poststand period were 12.9±1.84, 4.0±1.93, 7.1±1.44, and 1.8±0.57h (n=13). The fractions of time spent in SAG 3-5 during MTE, prestand, STE, and poststand period were 13, 8, 19, and 1% (n=11). During MTE, cows participated, on average, in 5.8±1.24 SAG 3-5 and initiated 9.5±2.99 mounts, with mean durations of 0.25±0.03h and 4.00±0.36s, respectively. The novel measure SAG 3-5 was a sign of long duration not confined only to groups of STE cows. On one day when no cows were in estrus and during the periods 4 to 24h before and after MTE, no SAG 3-5 behaviors were observed. Luteal-phase cows participated in SAG 3-5 only when the partner was a single cow in estrus. The time spent in SAG 3-5 increased between 1 and 3h before MTE and the prestand period (3 vs. 8%) and reached a peak level during STE. From STE to poststand, time spent in SAG 3-5 decreased considerably (19 vs. 1%). The observed decrease in nonmutual agonistic behaviors 4 to 24h before MTE is suggested as an early sign of pre-estrus. Changes in SAG 3-5, agonistic behaviors, and SEC are suggested as indicators of the specific stages of MTE. Increased SEC initiated and SAG 3-5 were indicators of late pre-estrus and early estrus (prestand). Peak levels of SAG 3-5, SEC, and social agonistic behaviors were indicators of STE. A sudden decrease in behaviors, preceded by frequent interactions, was indicative of late estrus (poststand). On the basis of the findings reported here, we propose that SAG 3-5, as well as proceptive and receptive patterns of SEC and agonistic behaviors, be included in estrus detection protocols. Updated knowledge of these behavioral interactions may assist when determining the stage of estrus and the optimal time to breed dairy cows.

Behavior of lactating Holstein-Friesian cows during spontaneous cycles of estrus.

The objectives of the present study were to describe, in detail, behavior associated with standing estrus (STE) in lactating dairy cows and behavioral changes during complete estrous cycles. Estrus signs were monitored by continuous video recording of 20 Holstein-Friesian (HF) cows housed on an outdoor wood-chip pad during 1 estrous cycle (22 d). Other social behavior was recorded during STE and, for comparison, during 1 selected day when none of the cows were in estrus. Standing stationary when mounted was defined as the primary estrus sign. Anogenital sniff, chin rest, attempt to mount, and mount were defined as secondary estrus signs. Ovarian cyclicity was confirmed by progesterone measurements. This study reports short mean duration of STE (7.1±1.44h) and estrus (mount period; 12.9±1.84h) of the 13 cows expressing these signs. All mounting activities involved at least one cow in, or within 4h of, STE. The most frequent sign during STE was anogenital sniff initiated, followed by chin rest received, chin rest initiated, chase up initiated, anogenital sniff received, mount initiated, head butt, mount received, attempt to mount initiated, push away received, play rub, attempt to mount received, follow initiated, threat received, flehmen, avoid, bellow, and social lick received. Standing and mounting activity in HF cows was inconsistent during estrus, indicating that other signs could be of greater use. The frequency of secondary estrus signs initiated and received increased gradually during the last 12h before STE, revealing significant differences between periods from 4 to 6 and 1 to 3h before STE. A considerable increase in receptive behavior (secondary estrus signs received) was identified between 1 to 3h prior to STE and STE. Both frequent initiated and received behaviors were associated with STE. A significant decrease in the frequency of secondary estrus signs initiated and received occurred 3h after STE. Cows in STE simultaneously predominantly chose the other standing cow as mate and expressed secondary estrus signs more frequently. Based on the results of this study, we suggest that chase up could be regarded as a reliable indicator of estrus and that the changes in proceptive (initiated) and receptive (received) behavior could be used as predictors of different stages in estrus. Knowledge of these behavioral signs may improve heat detection rates and the ability to predict the optimum breeding time for dairy cows.

Modelling the spread of infectious salmon anaemia among salmon farms based on seaway distances between farms and genetic relationships between infectious salmon anaemia virus isolates.

Infectious salmon anaemia (ISA) is an important infectious disease in Atlantic salmon farming causing recurrent epidemic outbreaks worldwide. The focus of this paper is on tracing the spread of ISA among Norwegian salmon farms. To trace transmission pathways for the ISA virus (ISAV), we use phylogenetic relationships between virus isolates in combination with space-time data on disease occurrences. The rate of ISA infection of salmon farms is modelled stochastically, where seaway distances between farms and genetic distances between ISAV isolates from infected farms play prominent roles. The model was fitted to data covering all cohorts of farmed salmon and the history of all farms with ISA between 2003 and summer 2009. Both seaway and genetic distances were significantly associated with the rate of ISA infection. The fitted model predicts that the risk of infection from a neighbourhood infectious farm decreases with increasing seaway distance between the two farms. Furthermore, for a given infected farm with a given ISAV genotype, the source of infection is significantly more likely to be ISAV of a small genetic distance than of moderate or large genetic distances. Nearly half of the farms with ISA in the investigated period are predicted to have been infected by an infectious farm in their neighbourhood, whereas the remaining half of the infected farms had unknown sources. For many of the neighbourhood infected farms, it was possible to point out one or a few infectious farms as the most probable sources of infection. This makes it possible to map probable infection pathways.

Predicting survival from microarray data--a comparative study.

MOTIVATION: Survival prediction from gene expression data and other high-dimensional genomic data has been subject to much research during the last years. These kinds of data are associated with the methodological problem of having many more gene expression values than individuals. In addition, the responses are censored survival times. Most of the proposed methods handle this by using Cox's proportional hazards model and obtain parameter estimates by some dimension reduction or parameter shrinkage estimation technique. Using three well-known microarray gene expression data sets, we compare the prediction performance of seven such methods: univariate selection, forward stepwise selection, principal components regression (PCR), supervised principal components regression, partial least squares regression (PLS), ridge regression and the lasso. RESULTS: Statistical learning from subsets should be repeated several times in order to get a fair comparison between methods. Methods using coefficient shrinkage or linear combinations of the gene expression values have much better performance than the simple variable selection methods. For our data sets, ridge regression has the overall best performance. AVAILABILITY: Matlab and R code for the prediction methods are available at http://www.med.uio.no/imb/stat/bmms/software/microsurv/.

Time series analysis of unequally spaced observations-with applications to copper contamination of the river gaula in central norway.

The upper parts of the river Gaula in Central Norway are heavily contaminated by toxic metals-particularily copper (Cu).A monitoring program for the river was established in early 1986, and the concentration of Cu, among other variables, has been measured.There is a fairly strong temporal component in the Cu measurements, which calls for some sort of time series model. The irregular pattern of the observation times, however, makes the usual models infeasible, as they assume equi-spaced observations.In the paper we present a simple DLM (Dynamic Linear Model) which gives a satisfactory description of the Cu concentration series. The model is fitted to the data using a Kalman filter technique which handles the irregularly spaced observations without problems.We have utilized the model to interpolate non-observed values, estimate the net loading and to simulate alternative patterns for the Cu series, observed values and the runoff, to obtain estimates of the extreme values and the probability that the concentration has exceeded a certain limit.