Climate change has become a significant issue in Australia. Australia's extreme climate is vulnerable to climate shifts and has experienced many extreme weather events recently. Research has shown that climate change is influencing flowering timing and the relative flowering orders of plants. Changes in flowering order can have detrimental effects on plant growth and food availability. Little research has examined how flowering orders have changed over time. This data from Hudson & Keatley (2021) explores the influence of long-term climate shifts in Victoria on the relative flowering order similarity of 81 species of plants from 1983 to 2014. The species were ranked annually by the time taken to flower (FFD), and changes in flowering order were measured by computing the similarity between annual flowering order and the flowering order of 1983 using the Rank-based Order similarity metric (RBO). The earliest flowering species is ranked 1 and latest ranked 81 for the given year under study.

A decrease in similarity over time suggests that flowering orders are becoming more dissimilar from their original ordering in 1983. The aim of your analysis is to see if it is possible to determine how flowering orders respond to specific changes in climate by understanding the relationship between flowering order (RBO) and climate conditions. Climate variables are yearly averaged temperature, rainfall, radiation and relative humidity. These relationships may be used - in theory - to forecast future flowering orders. Note the term "flowering order similarity" refers to the similarity between the flowering order of the 81 plant species for a particular year and the flowering order of the same plants in the so-called baseline reference year, 1983 (the first year in the data collection period with enough observations) measured by a rank similarity metric, RBO. RBO values are therefore numbers between 0 and 1. Higher RBO values indicate higher similarity of the order of the first flowering occurrence (based on FFD) of the 81 species from 1983 compared to each of the subsequent years, 1984 to 2014, so the time series are of length 31.

Models will be built using RBO similarity values for the flowering orders of consecutive years as the dependent time series (Y_t). Your data contains 5 time series, the RBO time series of the 81 plant species studied by Hudson & Keatley (2021) and the yearly averaged climate variables measured from 1984 - 2014, as 1983 is the reference year for flowering order FFD ranks. The time series are thereby of length 31, as the RBO metrics utilise 1983 rank orders of FFD as the baseline.

All series are available here inRBO .csvDownload RBO .csv

Task 3 Part (a):Carry out your analysis based onunivariateclimate regressors (modeloneclimate indicator at a time, i.e.,univariateregressor).

• Modelling methods to try (DLM, ARDL, polyck, koyck, dynlm).
• Choice of optimal models within EACH a specific method can be assessed from values of R squared, AIC, BIC, MASE etc (as is appropriate to the method).

The goal is to forecast RBOthreeyears ahead using each regressor one at a time and (use percentiles for the regressors) in forecasting for each of thebestmodels within the methods utilised.

Point forecasts and confidence intervals are required to be obtained and reports for the forecasts. Percentiles method for relevant covariates for the forecasts can be used.

Task 3 Part (b):Flowering orders became more dissimilar over the most recent decades, particularly during the Millennium Drought (1997 - 2009), suggesting that flora in Australia is responding to changes in their environment. According to the BoM the drought period for

Australia occurred from 1996 to 2009 How would younowaccommodate for this in your analysis of the Rank RBO. Perform the appropriate analysis and obtain the 3 year ahead forecasts (suggest using the dynlm package) only for part (b)).

I JUST NEED THE R CODE FOR THIS IN RMD FORMAT.

THE FIRST PART IN THE BELOW LINK CAN HELP YOU SOLVE THIS QUESTION

https://rpubs.com/AnnaKrinochkina/537658

BELOW IS THE CSV FILE OR THE INPUT DATA.

RBO.csv

Year	RBO	Temperature	Rainfall	Radiation	RelHumidity
1984	0.755009	9.371585	2.489344	14.87158	93.9265
1985	0.740752	9.656164	2.47589	14.68493	94.93589
1986	0.842386	9.273973	2.42137	14.51507	94.09507
1987	0.748443	9.219178	2.319726	14.67397	94.49699
1988	0.798408	10.20219	2.465301	14.74863	94.08142
1989	0.79388	9.441096	2.73589	14.78356	96.08685
1990	0.792568	9.943836	2.39863	14.67671	93.77918
1991	0.81387	9.690411	2.635616	14.41096	93.15562
1992	0.815284	9.691257	2.795902	13.39617	94.09863
1993	0.775801	9.947945	2.87863	14.26575	94.91973
1994	0.747185	9.316438	1.974795	14.52329	93.26932
1995	0.75082	9.164384	2.843288	13.90411	94.45863
1996	0.664442	8.967213	2.814754	14.3306	94.6
1997	0.694121	9.038356	1.403014	14.77534	93.74685
1998	0.704554	8.934247	2.289041	14.6	94.60822
1999	0.699226	9.547945	2.126301	14.6137	96.22603
2000	0.713712	9.680328	2.471858	14.65574	95.65738
2001	0.726742	9.561644	2.227945	14.14521	94.70712
2002	0.662948	9.389041	1.74	14.63836	93.53233
2003	0.711823	9.210959	2.270411	15.11233	94.47096
2004	0.703994	9.300546	2.620492	14.64481	95.01421
2005	0.732117	9.623288	2.28411	15.09315	94.30356
2006	0.725803	8.715068	1.78137	15.41096	94.84493
2007	0.700772	9.80137	2.191233	15.19452	94.11068
2008	0.744515	9.034153	1.743169	14.80328	94.39508
2009	0.685304	9.457534	2.03863	15.12877	94.63096
2010	0.702263	9.765753	2.777808	14.29315	96.05205
2011	0.758267	9.826027	2.886301	14.01096	95.70603
2012	0.734637	9.76776	2.599454	14.4071	94.90519
2013	0.725517	10.09726	2.540274	14.43014	93.83479
2014	0.709092	10.24725	2.239286	14.60165	94.21016