Classification problem - rpart

I am performing a binary classification using a classification tree.
Ironically, the data themselves are 2483 tree (real biological ones)
locations as described by a suite of environmental variables (slope, soil
moisture, radiation load, etc). I want to separate them from an equal number
of random points. Doing eda on the data shows that there is substantial
difference between the tree and random classes, e.g., box and whisker plots
for slope show separation.

The data frame is thus:

curvegrid,dir2tl,dist2tl,slope,tasp,tci10,class
-0.000244141,266,1852.701,2.382412,0.2124468,131,random
0.3005371,246,1146.342,10.45694,0.8045813,63,random
.
.
.
.
-0.3000488,90,10,20.25561,-0.1293357,62,tree
-0.5,90,10,18.68057,-0.05228489,61,tree
-0.6994629,0,0,18.30121,0.0320744,66,tree

I've run rpart on similar data without an issue but when I try it on this
data as follows:

tree <- rpart(class ~ curvegrid + slope + tci10, method="class")

I get the following output:

tree

n= 4966 

node), split, n, loss, yval, (yprob)
      * denotes terminal node

1) root 4966 2483 dw (0.500000000 0.500000000)  
  2) slope=0.3206026,0.5159777,0.679302,0.7163697,1.1324.......... 2574   94
dw (0.963480963 0.036519037) *
  3) slope=0,0.1011371,0.1013844,0.2027681,0.2267014,0.32......... MISSING
2392    3 random (0.001254181 0.998745819) *


This is not like other trees I have run!

And:

summary(tree)

summary(tree)

Call:
rpart(formula = class ~ curvegrid + slope + tci10)
  n= 4966 

         CP nsplit  rel error    xerror       xstd
1 0.9609344      0 1.00000000 1.0322191 0.01418310
2 0.0100000      1 0.03906565 0.7635924 0.01378822

Node number 1: 4966 observations,    complexity param=0.9609344
  predicted class=dw      expected loss=0.5
    class counts:  2483  2483
   probabilities: 0.500 0.500 
  left son=2 (2574 obs) right son=3 (2392 obs)
  Primary splits:
     slope     splits as  RRRRRRLRRRLRRRRLLRRRRRRR.......
     tci10     splits as  RRRRRRRRRRLLRLLRLLRLLRLL.......

etc.

Node number 2: 2574 observations
  predicted class=dw      expected loss=0.03651904
    class counts:  2480    94
   probabilities: 0.963 0.037 

Node number 3: 2392 observations
  predicted class=random  expected loss=0.001254181
    class counts:     3  2389
   probabilities: 0.001 0.999

I'm assuming that I have to adjust something in rpart.control. I am also
hesitant at posting prematurely but am in fetters.